CN118287634A - Silica sol shell manufacturing operation supervision system based on artificial intelligence - Google Patents
Silica sol shell manufacturing operation supervision system based on artificial intelligence Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 104
- RMAQACBXLXPBSY-UHFFFAOYSA-N silicic acid Chemical compound O[Si](O)(O)O RMAQACBXLXPBSY-UHFFFAOYSA-N 0.000 title claims abstract description 67
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 15
- 238000001514 detection method Methods 0.000 claims abstract description 127
- 238000000576 coating method Methods 0.000 claims abstract description 118
- 238000007605 air drying Methods 0.000 claims abstract description 115
- 239000011248 coating agent Substances 0.000 claims abstract description 108
- 239000004576 sand Substances 0.000 claims abstract description 93
- 238000000034 method Methods 0.000 claims abstract description 65
- 238000004458 analytical method Methods 0.000 claims abstract description 60
- 238000011156 evaluation Methods 0.000 claims abstract description 57
- 238000012544 monitoring process Methods 0.000 claims abstract description 39
- 239000002184 metal Substances 0.000 claims abstract description 37
- 238000012545 processing Methods 0.000 claims abstract description 37
- 238000003723 Smelting Methods 0.000 claims abstract description 33
- 238000005266 casting Methods 0.000 claims abstract description 19
- 238000012805 post-processing Methods 0.000 claims abstract description 11
- 230000007547 defect Effects 0.000 claims description 59
- 230000002159 abnormal effect Effects 0.000 claims description 53
- 239000003973 paint Substances 0.000 claims description 46
- 238000004364 calculation method Methods 0.000 claims description 36
- 238000005259 measurement Methods 0.000 claims description 24
- 238000005507 spraying Methods 0.000 claims description 21
- 238000004891 communication Methods 0.000 claims description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 9
- 239000012535 impurity Substances 0.000 claims description 7
- 239000002245 particle Substances 0.000 claims description 7
- 230000003313 weakening effect Effects 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims 2
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000007689 inspection Methods 0.000 description 13
- 230000006866 deterioration Effects 0.000 description 7
- 230000003746 surface roughness Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 239000007787 solid Substances 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000005058 metal casting Methods 0.000 description 2
- 238000005272 metallurgy Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 239000000919 ceramic Substances 0.000 description 1
- 238000005495 investment casting Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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Abstract
The invention belongs to the technical field of silica sol shell manufacturing processing supervision, in particular to an artificial intelligence-based silica sol shell manufacturing processing operation supervision system, which comprises a supervision platform, a coating sand scattering supervision module, an air drying supervision module, a post-processing supervision module and a processing management end; the invention realizes real-time monitoring and reasonable evaluation of coating and sanding processes by the coating and sanding supervision module based on the quality judgment information of the coating, the quality judgment information of the sand grains and the operation detection analysis of the coating and sanding processes or not so as to judge whether to generate supervision early warning signals, monitors the air drying operation, dewaxing roasting operation and metal smelting casting operation of the silica sol shell manufacturing process by the air drying supervision module and the post-treatment supervision module, can carry out full-flow monitoring and control and timely and accurately feed back early warning on the silica sol shell manufacturing process, is beneficial to ensuring the quality of the produced silica sol shell manufacturing processed product and the efficient and stable performance of the processing process, and has high intelligent and automatic degrees.
Description
Technical Field
The invention relates to the technical field of silica sol shell manufacturing and monitoring, in particular to an artificial intelligence-based silica sol shell manufacturing and operation monitoring system.
Background
The silica sol shell manufacturing process is a process for precision casting, is widely applied to the fields of ceramics, metallurgy, electronics, chemical industry and the like, has the advantages of fine structure, convenient assembly, no burrs on the surface, uniform thickness and the like, and can manufacture product shells with various structural shapes and attractive appearance;
At present, the processing supervision is often carried out in the processing process of the silica sol shell making by a manual supervision mode, so that the whole process monitoring and control of the whole processing process of the shell making are difficult to realize, the early warning is timely and accurately fed back, the quality of the processed product of the silica sol shell making and the high-efficiency and stable performance of the processing process are not guaranteed, the workload and the management difficulty of management staff are increased, and the intelligent and automatic degree is low;
In view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based silica sol shell manufacturing operation supervision system, which solves the problems that the whole flow monitoring control and timely and accurate feedback early warning of the whole shell manufacturing process are difficult to realize in the prior art, the quality of a silica sol shell manufacturing processed product and the high-efficiency and stable performance of the processing process are not guaranteed, and the intelligent and automatic degree is low.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The silica sol shell manufacturing operation monitoring system based on artificial intelligence comprises a monitoring platform, a coating sand scattering monitoring module, an air drying monitoring module, a post-processing monitoring module and a processing management end; the monitoring platform obtains a full-automatic silica sol shell manufacturing production line to be monitored, and marks the corresponding full-automatic silica sol shell manufacturing production line as a pipe conveying production line; the coating sand-spraying supervision module is used for acquiring quality judgment information of corresponding coating during coating and sand-spraying, acquiring quality judgment information of corresponding sand grains during sand-spraying, generating a supervision and early-warning signal of coating or sand-spraying if the quality judgment information of the coating is abnormal or the quality of the sand grains is abnormal, otherwise, performing operation detection analysis on the coating process or the sand-spraying process, judging whether to generate the supervision and early-warning signal through analysis, and transmitting the supervision and early-warning signal to a processing management end through a supervision platform;
The air-drying monitoring module monitors air-drying operation in the silica sol shell manufacturing process, generates an air-drying normal signal or an air-drying abnormal signal through analysis, and sends the air-drying normal signal or the air-drying abnormal signal to the processing management end through the monitoring platform; the post-treatment supervision module monitors dewaxing roasting operation and metal smelting casting operation in the silica sol shell manufacturing processing process, judges whether to generate a post-treatment abnormal signal through analysis, and sends the post-treatment abnormal signal to a processing management end through a supervision platform; and the processing management terminal receives the supervision early warning signal, the air-drying abnormal signal or the post-processing abnormal signal and sends out corresponding early warning.
Further, the supervision platform is in communication connection with the wax pattern scanning detection module, before the wax pattern enters a full-automatic silica sol production line and is ready for a silica sol shell making process, the wax pattern scanning detection module scans and detects the wax pattern, whether the quality of the wax pattern meets the requirement or not is judged through analysis, a wax pattern evaluation signal or a wax pattern deterioration evaluation signal is generated, the wax pattern evaluation signal or the wax pattern deterioration evaluation signal is sent to a processing management end through the supervision platform, and the processing management end sends corresponding early warning when receiving the wax pattern deterioration evaluation signal; the specific operation process of the wax pattern scanning detection module comprises the following steps:
Comprehensively scanning the corresponding wax pattern to obtain a wax pattern scanned image, marking the wax pattern scanned image as a wax pattern live image, overlapping the wax pattern live image with the corresponding wax pattern standard image to obtain coincidence degree data of the wax pattern live image and the wax pattern standard image, carrying out numerical comparison on the coincidence degree data and a preset coincidence degree data threshold value, and generating an inferior wax pattern signal if the coincidence degree data does not exceed the preset coincidence degree data threshold value; if the coincidence data exceeds the preset coincidence data threshold value, evaluating and analyzing through surface anomaly detection.
Further, the specific analysis process of the surface abnormality detection evaluation analysis is as follows:
Identifying surface defects in the corresponding wax patterns through the wax pattern live patterns, classifying all the surface defects, calling preset parameter data requirements of all the parameters to be detected of the corresponding type of surface defects from a supervision platform, collecting actual parameter data of all the parameters to be detected of the corresponding surface defects, comparing all the actual parameter data with the corresponding preset parameter data requirements one by one, and judging that the corresponding surface defects are high defects if the actual parameter data which do not meet the corresponding preset parameter data requirements exist; if the wax pattern has a high defect, generating a low-quality wax pattern signal;
If no high-quality defects exist in the wax mould, acquiring the number of the surface defects of the corresponding type in the wax mould, marking the number as surface defect condition measurement values, presetting a group of preset defect weight values corresponding to each type of surface defects, multiplying the surface defect condition measurement values of the corresponding type of surface defects by the corresponding preset defect weight values to obtain surface defect condition analysis values, and carrying out summation calculation on the surface defect condition analysis values of all types of surface defects in the wax mould to obtain a surface defect total analysis value;
Collecting surface roughness data of the wax pattern, carrying out difference value calculation on the surface roughness data and a median value of a preset proper surface roughness data range, and taking an absolute value to obtain a roughness detection value; performing numerical calculation on the coincidence ratio data, the total analysis value of the surface defects and the roughness detection value to obtain a wax pattern surface evaluation value, performing numerical comparison on the wax pattern surface evaluation value and a preset wax pattern surface evaluation threshold value, and generating a wax pattern inferior evaluation signal if the wax pattern performance evaluation value exceeds the preset wax pattern surface evaluation threshold value; and if the wax pattern performance evaluation value does not exceed the preset wax pattern surface evaluation threshold value, generating a wax pattern evaluation signal.
Further, the coating sand monitoring module is in communication connection with the coating sand evaluation module, the coating sand evaluation module detects the quality of the coating and sand used in the pipeline, judges whether the coating and sand meet the quality requirement through analysis, and sends the coating quality judgment information and the sand quality judgment information to the coating sand monitoring module; the specific operation process of the paint sand evaluation module is as follows:
When coating operation is carried out, collecting the temperature, viscosity and water content of the used paint, carrying out difference calculation on the temperature of the paint and the median value of a preset proper temperature range, taking an absolute value to obtain a paint Wen Kuang value, obtaining a paint viscosity condition value and a paint water condition value in a similar way, collecting the concentration of solid particles in the used paint, marking the concentration as a paint particle-containing value, carrying out numerical calculation on the paint Wen Kuangzhi, the paint viscosity condition value, the paint water condition value and the paint particle-containing value to obtain a paint condition detection value, carrying out numerical comparison on the paint condition detection value and a preset paint condition detection threshold value, and judging that the paint quality is abnormal if the paint condition detection value exceeds the preset paint condition detection threshold value; if the paint condition detection value does not exceed the preset paint condition detection threshold value, judging that the paint quality is normal;
When the sand spraying operation is carried out, collecting the average value of the grain sizes of the used sand grains, marking the deviation value of the average value of the grain sizes of the used sand grains compared with the median value of the preset proper grain size range as a grain size detection value, collecting the proportion of the sand grains with the grain sizes which are not in the preset proper grain size range as a non-proper grain detection value, carrying out numerical calculation on the grain size detection value and the non-proper grain detection value to obtain a grain condition value, carrying out numerical comparison on the grain condition value and a preset grain condition threshold, and judging that the quality of the sand grains is abnormal if the grain condition value exceeds the preset grain condition threshold; if the sand detection condition value does not exceed the preset sand detection condition threshold value, judging that the sand quality is normal.
Further, the specific analysis process for performing operation detection analysis on the coating process or the sanding process is as follows:
In the coating process, coating speed data and coating flow data are acquired, the coating speed data and the coating flow data are respectively compared with a preset coating speed data range and a preset coating flow data range in numerical values, and if the coating speed data or the coating flow data are not in the corresponding preset range, a supervision and early warning signal of coating operation is generated;
In the sanding process, sanding speed data and sanding force data are acquired, the sanding speed data and the sanding force data are respectively compared with a preset sanding speed data range and a preset sanding force data range in numerical value, and if the sanding speed data or the sanding force data are not in the corresponding preset range, a supervision early warning signal of sanding operation is generated.
Further, the specific operation process of the air drying supervision module comprises the following steps:
Setting detection points in a plurality of directions of the positions of the wax pattern, collecting the temperature, the humidity and the wind speed of the corresponding detection points, marking the deviation value of the temperature compared with the preset proper air drying temperature standard value as an air drying temperature table value, and obtaining an air drying humidity table value and an air drying speed table value in the same way; carrying out numerical calculation on an air drying temperature table value, an air drying humidity table value and an air drying speed table value to obtain an air drying measured condition value of a corresponding detection point, carrying out numerical comparison on the air drying measured condition value and a preset air drying measured condition threshold value, and marking the corresponding detection point as an air drying table inferior point if a plurality of air measured condition values exceed the preset air drying measured condition threshold value;
If the air drying table deterioration points exist, generating an air drying abnormal signal, if the air drying table deterioration points do not exist, performing variance calculation on air drying condition values of all detection points to obtain air drying deviation measurement values, performing numerical comparison on the air drying deviation measurement values and a preset air drying deviation measurement threshold value, and if the air drying deviation measurement values exceed the preset air drying deviation measurement threshold value, generating the air drying abnormal signal; if the air drying deviation measurement value does not exceed the preset air drying deviation measurement threshold value, generating an air drying normal signal.
Further, the analysis process of the post-processing supervision module is specifically as follows:
In the dewaxing roasting process, a temperature curve of the dewaxing roasting process is acquired, a plurality of coordinate points are marked on the temperature curve, the temperature data of all coordinate points are subjected to mean value calculation and variance calculation to obtain a dewaxing roasting temperature table value and a dewaxing roasting Wen Bo value, the dewaxing roasting temperature table value and the dewaxing roasting Wen Bo value are respectively compared with a preset dewaxing roasting temperature table value range and a preset dewaxing roasting Wen Bo threshold value in numerical values, and if the dewaxing roasting temperature table value is not in the preset dewaxing roasting temperature table value range or the dewaxing roasting Wen Bo value exceeds the preset dewaxing roasting Wen Bo threshold value, the dewaxing roasting operation is judged to be unqualified;
in the metal smelting and casting process, collecting the temperature and the casting speed of the cast molten metal, marking a deviation value of the temperature of the molten metal compared with a preset standard molten metal temperature value as molten metal Wen Kuangzhi, and marking a deviation value of the casting speed compared with a preset standard casting speed value as a molten metal casting speed value; the method comprises the steps of collecting fluidity data and impurity data of molten metal, carrying out numerical calculation on a molten metal Wen Kuang value, a molten metal pouring speed value, fluidity data and impurity data to obtain a smelting pouring detection value, carrying out numerical comparison on the smelting pouring detection value and a preset smelting pouring detection threshold value, and judging that the metal smelting pouring operation is unqualified if the smelting pouring detection value exceeds the preset smelting pouring detection threshold value; and generating a post-treatment abnormal signal when the dewaxing roasting operation is judged to be unqualified or the metal smelting casting operation is judged to be unqualified.
Further, the supervision platform is in communication connection with the pipeline production line analysis module, the pipeline production line analysis module is used for setting a detection period, collecting the total number and the number of scrapped products of the silica sol shell-making products produced by the corresponding pipeline production line in the detection period, and marking the ratio of the number of scrapped products to the total number as a scrapped detection value;
Collecting the occurrence times of the pause operation of the corresponding pipeline production line due to faults in the detection period, marking the occurrence times as a production line stop value, and summing the time length of each pause operation to obtain a production line stop time value; carrying out numerical calculation on the number of scrapped products, the scrapped detection value, the line accident stopping value and the line stoppage value to obtain a line inspection condition value, carrying out numerical comparison on the line inspection condition value and a preset line inspection condition threshold value, and marking the corresponding pipeline production line as a key supervision line if the line inspection condition value exceeds the preset line inspection condition threshold value; and if the line condition detection value does not exceed the preset line condition detection threshold, marking the corresponding pipeline conveying line as a weakening supervision line.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the coating sand spraying supervision module generates supervision early warning signals when receiving judgment information of abnormal coating quality or abnormal sand quality, otherwise, the coating sand spraying process or sand spraying process is operated, detected and analyzed to continuously judge corresponding operation conditions, real-time monitoring and reasonable evaluation of the coating sand spraying process are realized, and the air drying operation, dewaxing roasting operation and metal smelting casting operation in the silica sol shell manufacturing process are reasonably monitored and analyzed through the air drying supervision module and the post-treatment supervision module, so that the whole-flow monitoring, controlling and timely accurate feedback early warning can be carried out on the silica sol shell manufacturing process, the quality of the produced silica sol shell manufacturing process product and the high-efficiency stable operation of the process are guaranteed, and the intelligent and automatic degree is high;
2. According to the invention, the wax pattern is scanned and detected before entering the full-automatic silica sol production line and preparing for the silica sol shell making process by the wax pattern scanning and detecting module, whether the quality of the wax pattern meets the requirement is judged by analysis, and corresponding early warning is sent out to remind a manager to discard the corresponding wax pattern and replace the new wax pattern when a wax pattern inferior evaluation signal is generated, so that the quality of a produced silica sol shell making product is ensured, and the running condition of the full-automatic silica sol shell making production line in a detection period is reasonably analyzed by the pipe conveying production line detecting and analyzing module to determine an important supervision production line and a weakening supervision production line, so that the manager can manufacture an adaptive supervision scheme aiming at different pipe conveying production lines, and the running stability and the quality of the produced product are ensured.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
FIG. 2 is a system block diagram of a second embodiment of the present invention;
Fig. 3 is a system block diagram of a third embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1, the silica sol shell manufacturing operation monitoring system based on artificial intelligence provided by the invention comprises a monitoring platform, a coating sand scattering monitoring module, an air drying monitoring module, a post-processing monitoring module and a processing management end; the monitoring platform obtains a full-automatic silica sol shell manufacturing production line to be monitored, and marks the corresponding full-automatic silica sol shell manufacturing production line as a pipe conveying production line; when the silica sol shell manufacturing processing is carried out on a pipeline production line, a coating sand scattering monitoring module, an air drying monitoring module and a post-treatment monitoring module are used for carrying out full-flow monitoring control on the whole processing process; in the full-automatic silica sol shell manufacturing production line, the specific processing process comprises coating, sanding, air drying, dewaxing roasting and metal smelting casting;
When the corresponding transportation pipeline operates, the coating and sanding supervision module is used for acquiring quality judgment information of corresponding coating during coating and sanding, acquiring quality judgment information of corresponding sand grains during sanding, generating a coating and hanging or sanding supervision early warning signal if the quality judgment information of the coating or sand grains is abnormal, otherwise, performing operation detection analysis on the coating and hanging process or sanding process, judging whether to generate the supervision early warning signal or not through analysis, and transmitting the supervision early warning signal to a processing management end through a supervision platform, so that real-time monitoring and reasonable evaluation on the coating and hanging and sanding process are realized, and the coating and hanging and sanding process can be effectively supervised; the specific analysis process for operation detection analysis of the coating process or the sanding process is as follows:
In the coating process, coating speed data and coating flow data are acquired, wherein the coating speed data are data values representing the coating speed of the coating on the surface of the wax mould in the coating process, and the coating flow data are data values representing the volume of the coating output through a coating pipeline or a nozzle in unit time; respectively comparing the coating speed data and the coating flow data with a preset coating speed data range and a preset coating flow data range in numerical value, and if the coating speed data or the coating flow data are not in the corresponding preset range, indicating that the coating operation is poor in performance, generating a supervision and early warning signal of the coating operation;
In the sanding process, acquiring sanding speed data and sanding force data, wherein the sanding speed data is a data value representing the sand throwing speed in the sanding process, and the sanding force data is a data value representing the force exerted on the sand in the sanding process; and respectively carrying out numerical comparison on the sanding speed data and the sanding force data as well as a preset sanding speed data range and a preset sanding force data range, and if the sanding speed data or the sanding force data are not in the corresponding preset range, indicating that the sanding operation is poor in performance, generating a supervision and early warning signal of the sanding operation.
Furthermore, the coating sand monitoring module is in communication connection with the coating sand inspection and evaluation module, the coating sand inspection and evaluation module performs quality detection on the coating and sand used in the pipeline production line, judges whether the coating and sand meet the quality requirement through analysis, and sends coating quality judgment information and sand quality judgment information to the coating sand monitoring module, so that the quality conditions of the coating and sand can be monitored and accurately fed back in real time, and a manager can conveniently perform inspection and regulation in time or perform replacement of the coating and sand, so that the stability and the high efficiency of the corresponding operation process are ensured, the quality of a shell manufacturing product is improved, and data support can be provided for the analysis process of the coating sand monitoring module so as to ensure the accuracy of the analysis result; the specific operation process of the paint sand evaluation module is as follows:
When the coating operation is carried out, acquiring the temperature, viscosity and water content of the used coating, carrying out difference calculation on the temperature of the coating and the median value of a preset proper temperature range, taking an absolute value to obtain a coating Wen Kuangzhi, carrying out difference calculation on the viscosity of the coating and the median value of the preset proper viscosity range, taking an absolute value to obtain a coating viscosity condition value, carrying out difference calculation on the water content of the coating and the median value of the preset proper water content range, taking an absolute value to obtain a coating water condition value, and acquiring the concentration of solid particles in the used coating and marking the concentration as a coating particle-containing value;
By the formula Carrying out numerical calculation on a paint Wen Kuangzhi TW, a paint viscosity state value TN, a paint water state value TS and a paint particle-containing value TL to obtain a paint condition detection value TX, wherein b1, b2, b3 and b4 are preset proportion coefficients, the values of b1, b2, b3 and b4 are positive numbers, and the larger the numerical value of the paint condition detection value TX is, the worse the quality condition of the paint is; comparing the paint condition detection value TX with a preset paint condition detection threshold value, and judging that the paint quality is abnormal if the paint condition detection value TX exceeds the preset paint condition detection threshold value, which indicates that the quality condition of the paint is poor; if the paint condition detection value TX does not exceed the preset paint condition detection threshold value, the quality condition of the paint is good, and the paint quality is judged to be normal;
When the sand spraying operation is carried out, collecting the average value of the grain sizes of the used sand grains, marking the deviation value of the average value of the grain sizes of the sand grains compared with the median value of the preset proper grain size range as a grain size detection value, and collecting the proportion of the sand grains with the grain sizes which are not in the preset proper grain size range as a non-proper grain detection value; the larger the values of the sand grain size detection value and the non-fit sand grain detection occupation value are, the worse the sand condition is, and the more the sand needs to be replaced in time;
By the formula Carrying out numerical calculation on a sand grain size analysis value HX and a non-fit sand grain occupation detection value HK to obtain a sand grain condition detection value HY, wherein ey1 and ey2 are preset proportionality coefficients, and ey2 is greater than ey1 and greater than 0; and, the larger the value of the sand detection condition value HY, the worse the sand quality condition as a whole; comparing the sand detection condition value HY with a preset sand detection condition threshold value, and judging that the sand quality is abnormal if the sand detection condition value HY exceeds the preset sand detection condition threshold value, which indicates that the sand quality condition is poor as a whole; if the sand detection condition value HY does not exceed the preset sand detection condition threshold, the sand quality condition is better as a whole, and the sand quality is judged to be normal.
The air-drying monitoring module monitors air-drying operation in the silica sol shell manufacturing process, generates an air-drying normal signal or an air-drying abnormal signal through analysis, and sends the air-drying normal signal or the air-drying abnormal signal to the processing management end through the monitoring platform so that a manager can perform air-drying regulation and control in time, thereby ensuring the air-drying effect in the silica sol shell manufacturing process, reducing the management difficulty of the manager and further improving the quality of the produced shell manufacturing product; the specific operation process of the air drying supervision module is as follows:
Setting detection points in a plurality of directions of the positions of the wax mould, collecting the temperature, the humidity and the wind speed of the corresponding detection points, marking the deviation value of the temperature compared with the preset proper air drying temperature standard value as an air drying temperature table value, marking the deviation value of the humidity compared with the preset proper air drying humidity standard value as an air drying humidity table value, and marking the deviation value of the wind speed compared with the preset proper air drying wind speed standard value as an air drying speed table value;
By the formula Carrying out numerical calculation on an air-drying temperature meter value WF, an air-drying humidity meter value WS and an air-drying speed meter value WK to obtain an air-drying measured condition value WX of a corresponding detection point, wherein kp1, kp2 and kp3 are preset proportional coefficients, and the values of kp1, kp2 and kp3 are positive numbers; and, the larger the value of the air-drying measured condition value WX is, the more abnormal the air-drying environment in the corresponding direction is indicated; comparing the air-drying measured condition value WX with a preset air-drying measured condition threshold value, and marking the corresponding detection point as an air-drying table bad point if the air-drying measured condition value WX exceeds the preset air-drying measured condition threshold value to indicate that the air-drying environment in the corresponding direction is abnormal;
If the air drying surface deterioration points exist, generating an air drying abnormal signal, if the air drying surface deterioration points do not exist, performing variance calculation on air drying measurement condition values of all detection points to obtain air drying deviation measurement values, performing numerical comparison on the air drying deviation measurement values and a preset air drying deviation measurement threshold value, and if the air drying deviation measurement values exceed the preset air drying deviation measurement threshold value, indicating that the uniformity of air drying environments in all directions is poor, and not beneficial to uniform air drying of silica sol shell products, generating the air drying abnormal signal; if the air drying deviation measurement value does not exceed the preset air drying deviation measurement threshold value, the air drying deviation measurement value indicates that the uniformity of the air drying environment in all directions is good, and then an air drying normal signal is generated.
The post-treatment supervision module monitors dewaxing roasting operation and metal smelting casting operation in the silica sol shell manufacturing process, judges whether to generate a post-treatment abnormal signal through analysis, and sends the post-treatment abnormal signal to a processing management end through a supervision platform so that management personnel can timely regulate and control the dewaxing roasting and metal smelting casting process, thereby ensuring smooth proceeding of the silica sol shell manufacturing process and further improving the quality of the produced shell manufacturing product; the analysis process of the post-processing supervision module is specifically as follows:
In the dewaxing roasting process, a temperature curve of the dewaxing roasting process is acquired, a plurality of coordinate points are marked on the temperature curve, the temperature data of all coordinate points are subjected to mean value calculation and variance calculation to obtain a dewaxing roasting temperature table value and a dewaxing roasting Wen Bo value, the dewaxing roasting temperature table value and the dewaxing roasting Wen Bo value are respectively compared with a preset dewaxing roasting temperature table value range and a preset dewaxing roasting Wen Bo threshold value in numerical values, and if the dewaxing roasting temperature table value is not in the preset dewaxing roasting temperature table value range or the dewaxing roasting Wen Bo value exceeds the preset dewaxing roasting Wen Bo threshold value, the operation performance condition of the dewaxing roasting process is poor, the dewaxing roasting operation is judged to be unqualified;
in the metal smelting and casting process, collecting the temperature and the casting speed of the cast molten metal, marking a deviation value of the temperature of the molten metal compared with a preset standard molten metal temperature value as molten metal Wen Kuangzhi, and marking a deviation value of the casting speed compared with a preset standard casting speed value as a molten metal casting speed value; the method comprises the steps of collecting fluidity data and impurity data of molten metal, wherein the fluidity data is a data value representing the flowing speed of the molten metal, and the impurity data is a data value representing the concentration of solid impurities in the molten metal;
By the formula Carrying out numerical calculation on a molten metal Wen Kuangzhi RW, a molten metal pouring speed value RS, fluidity data RL and impurity data RZ to obtain a smelting pouring detection value RX, wherein eq1, eq2, eq3 and eq4 are preset proportion coefficients, and the values of eq1, eq2, eq3 and eq4 are positive numbers; and, the larger the value of the smelting pouring detection value RX is, the more abnormal the metal smelting pouring operation is indicated;
Comparing the smelting pouring detection value RX with a preset smelting pouring detection threshold value, and judging that the metal smelting pouring operation is unqualified if the smelting pouring detection value RX exceeds the preset smelting pouring detection threshold value, which indicates that the metal smelting pouring operation is abnormal and corresponding regulation and control are needed in time; and generating a post-treatment abnormal signal when the dewaxing roasting operation is judged to be unqualified or the metal smelting casting operation is judged to be unqualified.
It should be noted that, the processing management end receives the supervision early warning signal, air-dries the unusual signal or sends corresponding early warning when the aftertreatment unusual signal to the managers in time makes corresponding counter measures, has realized the real-time whole flow control and the timely accurate early warning of silica sol system shell course of working, has improved stability and the product quality of course of working, has reduced manufacturing cost and has improved production efficiency, but wide application in the silica sol system shell course of working in fields such as pottery, metallurgy, electron, chemical industry, show the supervision degree of difficulty of reducing silica sol system shell course of working, reduce managers' work load, intelligent degree and degree of automation are high.
Embodiment two: as shown in fig. 2, the difference between the present embodiment and embodiment 1 is that the supervision platform is in communication connection with the wax pattern scanning detection module, and before the wax pattern enters the full-automatic silica sol production line and is ready for a silica sol shell making process, the wax pattern scanning detection module scans and detects the wax pattern, and determines whether the quality of the wax pattern meets the requirement and generates a wax pattern evaluation signal or a wax pattern inferior evaluation signal through analysis, and sends the wax pattern evaluation signal or the wax pattern inferior evaluation signal to the processing management end through the supervision platform, and the processing management end sends a corresponding early warning when receiving the wax pattern inferior evaluation signal so as to remind a manager to discard the corresponding wax pattern and replace the new wax pattern, thereby ensuring the quality of the produced silica sol shell making product; the specific operation process of the wax pattern scanning detection module is as follows:
Comprehensively scanning the corresponding wax pattern to obtain a wax pattern scanned image, marking the wax pattern scanned image as a wax pattern live image, and overlapping the wax pattern live image with the corresponding wax pattern standard image to obtain coincidence ratio data of the wax pattern live image and the wax pattern standard image, wherein the larger the value of the coincidence ratio data is, the higher the appearance size precision of the wax pattern is, the numerical comparison is carried out on the coincidence ratio data and a preset coincidence ratio data threshold value, and if the coincidence ratio data does not exceed the preset coincidence ratio data threshold value, the poor appearance size precision of the wax pattern is indicated, and an inferior wax pattern signal is generated;
If the coincidence data exceeds the preset coincidence data threshold value, the appearance size precision of the wax pattern is shown to be better, and the analysis is evaluated and analyzed through surface anomaly detection, specifically: identifying surface defects in the corresponding wax patterns through the wax pattern live diagram, classifying all the surface defects (such as shrinkage cavities, bubbles, cracks and the like), calling preset parameter data requirements of all parameters to be detected of the corresponding type of surface defects from a supervision platform, collecting actual parameter data of all the parameters to be detected of the corresponding surface defects (aiming at the cracks, the parameters to be detected comprise crack length, depth, width and the like), comparing all the actual parameter data with the corresponding preset parameter data requirements one by one, and judging that the corresponding surface defects are inferior defects if the actual parameter data which do not meet the corresponding preset parameter data requirements exist; if the wax pattern has a high defect, which indicates that the quality condition of the corresponding wax pattern is poor, and the quality of the produced shell-making product is not guaranteed, an inferior wax pattern signal is generated;
If no inferior defects exist in the wax pattern, acquiring the number of the corresponding type of surface defects in the wax pattern, marking the number as a surface defect condition measurement value, and presetting a group of preset defect weight values corresponding to each type of surface defects respectively, wherein the values of the preset defect weight values are positive numbers, and the larger the adverse effect of the corresponding type of defects on the quality of the wax pattern is, the larger the value of the preset defect weight value matched with the corresponding type of defects is; multiplying the surface defect condition measured value of the corresponding type of surface defects with a corresponding preset defect weight value to obtain a surface defect condition analysis value, and carrying out summation calculation on the surface defect condition analysis values of all types of surface defects existing in the wax pattern to obtain a surface defect total analysis value, wherein the larger the value of the surface defect total analysis value is, the worse the quality condition of the corresponding wax pattern is indicated;
Collecting surface roughness data of the wax pattern, carrying out difference value calculation on the surface roughness data and a median value of a preset proper surface roughness data range, and taking an absolute value to obtain a roughness detection value; by surface mass analysis formula Performing numerical calculation on the coincidence degree data LS, the total analysis value LP of the surface defects and the roughness detection value LK to obtain a wax pattern surface abnormal evaluation value LM, wherein a1, a2 and a3 are preset proportionality coefficients, and the values of a1, a2 and a3 are positive numbers; and, the larger the value of the wax pattern surface dissimilarity evaluation value LM is, the worse the quality condition of the corresponding wax pattern is;
Comparing the wax pattern surface abnormal evaluation value LM with a preset wax pattern surface abnormal evaluation threshold value, and generating a wax pattern inferior evaluation signal if the wax pattern performance abnormal evaluation value LM exceeds the preset wax pattern surface abnormal evaluation threshold value, which indicates that the quality condition of the corresponding wax pattern is poor in the comprehensive aspect, and the corresponding wax pattern is not suitable for the subsequent silica sol shell making processing and needs to be replaced; and if the wax pattern performance evaluation value LM does not exceed the preset wax pattern surface evaluation threshold value, indicating that the quality condition of the corresponding wax pattern is good in combination, generating a wax pattern evaluation signal.
Embodiment III: as shown in fig. 3, the difference between the present embodiment and embodiments 1 and 2 is that the supervision platform is in communication connection with the pipeline production line analysis module, and the pipeline production line analysis module is used for setting a detection period, preferably, the detection period is forty-eight hours; collecting the total number of the silica sol shell-making products and the number of the scrapped products produced by the corresponding pipeline production line in the detection period, and marking the ratio of the number of the scrapped products to the total number as a scrapped detection value; the larger the number of the scrapped detection values and the scrapped product numbers is, the worse the quality condition of the products produced by the corresponding pipe conveying production line in the detection period is, and the worse the running quality of the corresponding pipe conveying production line is; collecting the occurrence times of the pause operation of the corresponding pipeline production line due to faults in the detection period, marking the occurrence times as a production line stop value, and summing the time length of each pause operation to obtain a production line stop time value; it should be noted that, the larger the values of the line stop value and the line stop time value, the worse the operation stability of the corresponding pipeline in the detection period is indicated;
By the formula Carrying out numerical calculation on the number XF of the scrapped products, the scrapped detection value XK, the line stop value XW and the line stop value XQ to obtain a line detection condition value XP, wherein, the values of the w1, the w2, the w3 and the w4 are preset proportion coefficients, and the values of the w1, the w2, the w3 and the w4 are positive numbers; and the larger the value of the line condition detection value XP is, the worse the running condition of the corresponding pipeline in the detection period is;
Comparing the line inspection condition value XP with a preset line inspection condition threshold value, and marking the corresponding line as a key supervision line if the line inspection condition value XP exceeds the preset line inspection condition threshold value, which indicates that the operation condition of the corresponding line is poor in combination in the inspection period, and the full-automatic silica sol shell manufacturing line needs to be subjected to the subsequent intensive supervision; if the line condition detection value XP does not exceed the preset line condition detection threshold, indicating that the running condition of the corresponding pipeline in the detection period is better in a comprehensive way, marking the corresponding pipeline as a weakening supervision pipeline; the manager prepares an adaptive supervision scheme for different pipeline production lines, and subsequently strengthens the operation supervision of the key supervision production line, thereby helping to ensure the operation stability and the quality of the produced products.
The working principle of the invention is as follows: when the system is used, the quality judgment information of the corresponding paint is obtained when the paint is coated and hung by the coating sand spraying supervision module, the quality judgment information of the corresponding sand grains is obtained when the sand spraying is carried out, and a supervision early warning signal is generated if the judgment information of the abnormal paint quality or the abnormal sand grain quality is received, otherwise, the operation detection analysis is carried out on the coating process or the sand spraying process to judge whether the supervision early warning signal is generated, the real-time monitoring and the reasonable evaluation on the coating and sand spraying processes are realized, the air drying operation of the silica sol shell manufacturing process is effectively monitored by the air drying supervision module, the air drying effect of the silica sol shell manufacturing process is ensured, the dewaxing roasting operation and the metal smelting casting operation of the silica sol shell manufacturing process are monitored by the post-processing supervision module, the analysis is carried out to judge whether the post-processing abnormal signal is generated, the whole flow monitoring control and the timely accurate feedback early warning of the whole silica sol shell manufacturing process are realized, the quality and the high-efficiency stable processing of the produced silica sol shell manufacturing process are favorably, the workload and management of a manager are reduced, and the intelligent degree of automation is high.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The silica sol shell manufacturing operation supervision system based on artificial intelligence is characterized by comprising a supervision platform, a coating sand scattering supervision module, an air drying supervision module, a post-processing supervision module and a processing management end; the monitoring platform obtains a full-automatic silica sol shell manufacturing production line to be monitored, and marks the corresponding full-automatic silica sol shell manufacturing production line as a pipe conveying production line; the coating sand-spraying supervision module is used for acquiring quality judgment information of corresponding coating during coating and sand-spraying, acquiring quality judgment information of corresponding sand grains during sand-spraying, generating a supervision and early-warning signal of coating or sand-spraying if the quality judgment information of the coating is abnormal or the quality of the sand grains is abnormal, otherwise, performing operation detection analysis on the coating process or the sand-spraying process, judging whether to generate the supervision and early-warning signal through analysis, and transmitting the supervision and early-warning signal to a processing management end through a supervision platform;
The air-drying monitoring module monitors air-drying operation in the silica sol shell manufacturing process, generates an air-drying normal signal or an air-drying abnormal signal through analysis, and sends the air-drying normal signal or the air-drying abnormal signal to the processing management end through the monitoring platform; the post-treatment supervision module monitors dewaxing roasting operation and metal smelting casting operation in the silica sol shell manufacturing processing process, judges whether to generate a post-treatment abnormal signal through analysis, and sends the post-treatment abnormal signal to a processing management end through a supervision platform; and the processing management terminal receives the supervision early warning signal, the air-drying abnormal signal or the post-processing abnormal signal and sends out corresponding early warning.
2. The silica sol shell manufacturing operation supervision system based on artificial intelligence according to claim 1, wherein the supervision platform is in communication connection with the wax pattern scanning detection module, the wax pattern scanning detection module scans and detects the wax pattern before the wax pattern enters a full-automatic silica sol production line and is ready for a silica sol shell manufacturing process, whether the quality of the wax pattern meets the requirements or not is judged through analysis, a wax pattern evaluation signal or a wax pattern inferior evaluation signal is generated, the wax pattern evaluation signal or the wax pattern inferior evaluation signal is sent to a processing management end through the supervision platform, and the processing management end sends corresponding early warning when receiving the wax pattern inferior evaluation signal; the specific operation process of the wax pattern scanning detection module comprises the following steps:
Comprehensively scanning the corresponding wax pattern to obtain a wax pattern scanned image, marking the wax pattern scanned image as a wax pattern live image, overlapping the wax pattern live image with the corresponding wax pattern standard image to obtain coincidence ratio data of the wax pattern live image and the wax pattern standard image, and generating an inferior wax pattern signal if the coincidence ratio data does not exceed a preset coincidence ratio data threshold value; if the coincidence data exceeds the preset coincidence data threshold value, evaluating and analyzing through surface anomaly detection.
3. The artificial intelligence-based silica sol shell manufacturing operation supervision system according to claim 2, wherein the specific analysis process of the surface anomaly detection evaluation analysis is as follows:
Identifying surface defects in the corresponding wax patterns through the wax pattern live patterns, classifying all the surface defects, and judging that the corresponding surface defects are high defects if the actual parameter data which do not meet the requirements of the corresponding preset parameter data exist; if the wax pattern has a high defect, generating a low-quality wax pattern signal;
if no high-quality defects exist in the wax pattern, multiplying the surface defect condition measured values of the corresponding type of surface defects with corresponding preset defect weight values to obtain surface defect condition analysis values, and carrying out summation calculation on the surface defect condition analysis values of all the type of surface defects in the wax pattern to obtain a surface defect total analysis value; performing numerical calculation on the coincidence ratio data, the total analysis value of the surface defects and the roughness detection value to obtain a wax pattern surface abnormal evaluation value, and generating a wax pattern inferior evaluation signal if the wax pattern performance abnormal evaluation value exceeds a preset wax pattern surface abnormal evaluation threshold value; and if the wax pattern performance evaluation value does not exceed the preset wax pattern surface evaluation threshold value, generating a wax pattern evaluation signal.
4. The silica sol shell manufacturing operation supervision system based on artificial intelligence according to claim 1, wherein the coating sand supervision module is in communication connection with a coating sand evaluation module, the coating sand evaluation module performs quality detection on coating and sand used in a pipeline production line, judges whether the coating and sand meet quality requirements through analysis, and sends coating quality judgment information and sand quality judgment information to the coating sand supervision module; the specific operation process of the paint sand evaluation module is as follows:
When coating operation is carried out, a coating condition value is obtained by carrying out numerical calculation on the coating Wen Kuangzhi, the coating viscosity condition value, the coating water condition value and the coating particle-containing value, and if the coating condition value exceeds a preset coating condition threshold value, the quality of the coating is judged to be abnormal; if the paint condition detection value does not exceed the preset paint condition detection threshold value, judging that the paint quality is normal;
When the sand spraying operation is carried out, a sand grain size detection and analysis value and a non-fit sand grain detection and occupation value are subjected to numerical calculation to obtain a sand grain detection condition value, and if the sand grain detection condition value exceeds a preset sand grain detection condition threshold value, the quality of sand grains is judged to be abnormal; if the sand detection condition value does not exceed the preset sand detection condition threshold value, judging that the sand quality is normal.
5. The silica sol shell manufacturing operation supervision system based on artificial intelligence according to claim 1, wherein the specific analysis process of operation detection analysis of the coating process or the sanding process is as follows:
In the coating process, coating speed data and coating flow data are acquired, and if the coating speed data or the coating flow data are not in a corresponding preset range, a supervision early warning signal of coating operation is generated;
In the sanding process, sanding speed data and sanding force data are acquired, and if the sanding speed data or the sanding force data are not in a corresponding preset range, a supervision early warning signal of sanding operation is generated.
6. The silica sol shell manufacturing operation supervision system based on artificial intelligence according to claim 1, wherein the specific operation process of the air drying supervision module comprises:
Setting detection points in a plurality of directions of the positions of the wax patterns, carrying out numerical calculation on an air drying temperature table value, an air drying humidity table value and an air drying speed table value to obtain air drying condition values of corresponding detection points, and marking the corresponding detection points as air drying condition inferior points if the air condition values exceed a preset air drying condition threshold;
If the air drying table bad points exist, generating an air drying abnormal signal, if the air drying table bad points do not exist, performing variance calculation on air drying measured condition values of all detection points to obtain an air drying deviation measurement value, and if the air drying deviation measurement value exceeds a preset air drying deviation measurement threshold value, generating an air drying abnormal signal; if the air drying deviation measurement value does not exceed the preset air drying deviation measurement threshold value, generating an air drying normal signal.
7. The silica sol shell manufacturing operation supervision system based on artificial intelligence according to claim 1, wherein the analysis process of the post-processing supervision module is specifically as follows:
In the dewaxing roasting process, a temperature curve of the dewaxing roasting process is acquired, a plurality of coordinate points are marked on the temperature curve, the temperature data of all coordinate points are subjected to mean value calculation and variance calculation to obtain a dewaxing roasting temperature table value and a dewaxing roasting Wen Bo value, and if the dewaxing roasting temperature table value is not in a preset dewaxing roasting temperature table value range or the dewaxing roasting Wen Bo value exceeds a preset dewaxing roasting Wen Bo threshold value, the dewaxing roasting operation is judged to be unqualified;
In the metal smelting and pouring process, a smelting and pouring detection value is obtained by carrying out numerical calculation on a metal liquid Wen Kuang value, a metal liquid pouring speed value, fluidity data and impurity data, and if the smelting and pouring detection value exceeds a preset smelting and pouring detection threshold value, the metal smelting and pouring operation is judged to be unqualified; and generating a post-treatment abnormal signal when the dewaxing roasting operation is judged to be unqualified or the metal smelting casting operation is judged to be unqualified.
8. The silica sol shell manufacturing operation supervision system based on artificial intelligence according to claim 1, wherein the supervision platform is in communication connection with a pipeline production line analysis module, the pipeline production line analysis module is used for setting a detection period, and performing numerical calculation on the number of scrapped products, the scrapped detection value, the production line accident stop value and the production line stop value to obtain a production line condition value, and marking the corresponding pipeline production line as an important supervision production line if the production line condition value exceeds a preset production line condition threshold; and if the line condition detection value does not exceed the preset line condition detection threshold, marking the corresponding pipeline conveying line as a weakening supervision line.
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