TWI831602B - Tool critical life diagnosis method based on current information and system thereof - Google Patents

Abstract

A tool critical life diagnosis method based on current information is configured to diagnose a critical life of a tool and includes performing a data obtaining step, a current increase rate calculating step and a rate confirmation diagnosis step. The data obtaining step includes performing an initial data obtaining step and a real-time data obtaining step. The initial data obtaining step includes configuring a processor to obtain an initial load current and a critical life threshold rate from a memory. The real-time data obtaining step includes configuring the processor to obtain an real-time load current of the tool. The current increase rate calculating step includes configuring the processor to calculate a current increase rate of the tool. The current increase rate is a ratio of the real-time load current and the initial load current. The rate confirmation diagnosis step includes configuring the processor to confirm whether the current increase rate is greater than or equal to the critical life threshold rate to generate a rate confirmation result, and diagnose whether the tool has reached the critical life according to the rate confirmation result. Therefore, the tool critical life diagnosis method based on current information of the present disclosure can diagnose the critical life of the tool via the current increase rate and a current increase slope, and have the features of stable measurement environment, low instrument price, low cost and low sample rate.

Description

Tool critical life diagnosis method and system based on current information

The present invention relates to a tool critical life diagnosis method and its system, and in particular to a tool critical life diagnosis method and its system based on current information.

The machinery industry has laid the foundation for the vigorous development of the entire society and today retains a complete and strong supply chain of machine tools and precision machinery parts. However, with the improvement of people's wages and education levels, most people will think twice about the factory environment and day and night shift system. For many manufacturers that perform mechanical processing, they are currently encountering the dilemma of rising operating costs and manpower shortages. Therefore, Industry 4.0 smart manufacturing is promoted to reduce the number of employees in the factory. Unmanned factories are the overall trend in the current machinery industry, and monitoring tool wear and estimating processing quality and processing abnormalities is an extremely important part of smart manufacturing. Because unmanned factories If the processing quality is ignored, it is very easy to produce a large number of scrap products or repair parts, resulting in unnecessary waste of time and cost, and a considerable negative impact on the company's operations. To sum up, tool wear monitoring can bring three main benefits. The first benefit is to improve the quality of workpiece processing, the second benefit is to improve processing efficiency, and the third benefit is to reduce operating costs.

Tool wear is closely related to the processing quality of the workpiece. Increased tool wear will cause a significant increase in cutting resistance, resulting in a corresponding increase in the size variation range of the workpiece after processing and the surface roughness of the machined surface, ultimately affecting the processing quality. Today's machining processes often use at least multiple knives (tools for different purposes) for processing. When the tools are severely worn, it will significantly increase the risk of roughing tool breakage for the purpose of material removal. When the roughing tool breaks, it will be processed. The reserved amount will be much larger than before the tool breaks, causing the subsequent processing tools to break all together or insert the tool due to a chain reaction due to the excessive reserved amount. Severe wear or breakage of finishing tools will lead to risks such as poor machining accuracy. Therefore, severe wear or breakage of finishing tools will have a significant impact on the machine utilization rate. Technicians need to stop production, change tools, set tools, and repair workpieces. The current management and control methods for tool wear mainly refer to the long-standing rules of experience of technicians. In order to reduce risks and accidents during processing, technicians often use past experience as an indicator to develop a fixed process for processing. After the number of workpieces, the tool replacement action is carried out uniformly, or during the handover process with colleagues on the next shift, it is difficult to clearly explain the use of the tools in a quantitative way, resulting in the personnel on the next shift replacing new ones before starting processing. Knives, the above situation will virtually waste many useful knives. It can be seen that there is currently a lack of a tool critical life diagnosis method and system based on current information that has a safer and simpler measurement environment, is low-cost, and has a low amount of data per unit time. Therefore, relevant researchers are looking for Its solution.

Therefore, the purpose of the present invention is to provide a method and system for diagnosing the critical life of a tool based on current information, which diagnoses the critical life of the tool through the current increase rate and the current increase slope. It not only has a safer and simpler measurement environment, but also is more cost-effective. It is low-cost and requires a low amount of data to be processed per unit time. It can also judge stable wear or rapid wear during processing, thereby realizing intelligent critical life diagnosis to solve the problem of traditional diagnosis that must be based on personnel experience rules and costs. Too high a problem.

An embodiment of the method aspect of the present invention provides a tool critical life diagnosis method based on current information, which is used to diagnose the critical life of a tool and includes the following steps: a data acquisition step, a current increase rate Calculation steps and doubling confirmation diagnostic steps. The data acquisition step includes an initial data acquisition step and a real-time data acquisition step. The initial data acquisition step includes driving a computing processor to acquire a new initial load current and a critical lifetime threshold multiple from a memory. The real-time data acquisition step includes driving the computing processor to obtain the real-time load current of the tool. The current increase rate calculation step includes driving the computing processor to calculate the current increase rate of one of the tools, and the current increase rate is a ratio of the immediate load current and the new tool's initial load current. The magnification confirmation diagnostic step includes driving the arithmetic processor to confirm whether the current increase rate is greater than or equal to the critical life threshold magnification to generate a magnification confirmation result, and diagnosing whether the tool has reached the critical life based on the magnification confirmation result.

In this way, the tool critical life diagnosis method based on current information of the present invention can diagnose the critical life of the tool through the current increase rate and current increase slope. It not only has a safer and simpler measurement environment, low cost and needs to be processed per unit time. With the low data volume, stable wear or rapid wear can be judged during processing, thereby realizing intelligent critical life diagnosis to solve the problem of conventional diagnosis that must be based on human experience rules and is too costly.

Other examples of the aforementioned embodiments are as follows: in the aforementioned magnification confirmation diagnosis step, when the magnification confirmation result is yes, the computing processor determines that the tool has reached the critical life; and when the magnification confirmation result is no, the computing processor determines that the tool has not reached the critical life. Reach critical life.

Other examples of the aforementioned implementation are as follows: the aforementioned tool critical life diagnosis method based on current information further includes a current increase slope calculation step and a slope confirmation diagnosis step. The step of calculating the current increase slope includes driving the operation processor to calculate the current increase slope of one of the tools using a linear regression analysis method. The slope confirmation diagnosis step includes driving the computing processor to confirm whether the current increase slope increases to generate a slope confirmation result, and diagnosing whether the tool is approaching critical life based on the slope confirmation result, where the current increase slope is an increase value of the current per unit time.

Other examples of the aforementioned embodiments are as follows: in the aforementioned slope confirmation diagnosis step, when the slope confirmation result is yes, the computing processor determines that the tool is approaching the critical life; and when the slope confirmation result is no, the computing processor determines that the tool is not approaching the critical life. critical life.

Other examples of the aforementioned implementation are as follows: the aforementioned critical life threshold ratio is greater than or equal to 1.3 and less than or equal to 1.5, and the linear regression analysis method includes a first-order linear regression analysis method.

An embodiment of the structural aspect of the present invention provides a tool critical life diagnosis system based on current information, which is used to diagnose the critical life of a tool and includes a memory and a computing processor. The memory stores the initial load current of a new tool and a critical life threshold rate. The computing processor is electrically connected to the memory and receives the initial load current of the new tool and the critical life threshold multiple. The computing processor is configured to perform an operation including the following steps: a real-time data acquisition step, a current increase rate calculation step, and a rate confirmation diagnostic step. The real-time data acquisition step includes obtaining a real-time load current of the tool. The current increase rate calculation step includes calculating the current increase rate of a tool, and the current increase rate is a ratio of the immediate load current to the initial load current of the new tool. The magnification confirmation diagnostic step includes confirming whether the current increase magnification is greater than or equal to the critical life threshold magnification to generate a magnification confirmation result, and diagnosing whether the tool has reached the critical life based on the magnification confirmation result.

In this way, the tool critical life diagnosis system based on current information of the present invention can diagnose the critical life of the tool through the current increase rate and current increase slope. It not only has a safer and simpler measurement environment, low cost and needs to be processed per unit time. With the low data volume, stable wear or rapid wear can be judged during processing, thereby realizing intelligent critical life diagnosis to solve the problem of conventional diagnosis that must be based on human experience rules and is too costly.

Other examples of the foregoing embodiments are as follows: when the magnification confirmation result is yes, the computing processor determines that the tool has reached the critical life; and when the magnification confirmation result is no, the computing processor determines that the tool has not reached the critical life.

Other examples of the aforementioned implementation are as follows: the aforementioned computing processor is configured to perform operations further including the following steps: a current increase slope calculation step and a slope confirmation diagnostic step. The step of calculating the current increase slope includes using a linear regression analysis method to calculate the current increase slope of one of the tools. The slope confirmation diagnostic step includes confirming whether the current increase slope increases to generate a slope confirmation result, and diagnosing whether the tool is approaching the critical life based on the slope confirmation result, where the current increase slope is an increase value of the current per unit time.

Other examples of the foregoing implementation are as follows: when the slope confirmation result is yes, the computing processor determines that the tool is close to the critical life; and when the slope confirmation result is no, the computing processor determines that the tool is not close to the critical life.

Other examples of the aforementioned implementation are as follows: the aforementioned critical life threshold ratio is greater than or equal to 1.3 and less than or equal to 1.5, and the linear regression analysis method includes a first-order linear regression analysis method.

Several embodiments of the present invention will be described below with reference to the drawings. For the sake of clarity, many practical details will be explained together in the following narrative. However, it will be understood that these practical details should not limit the invention. That is to say, in some embodiments of the present invention, these practical details are not necessary. In addition, for the sake of simplifying the drawings, some commonly used structures and components are shown in the drawings in a simple schematic manner; and repeated components may be represented by the same numbers.

In addition, when a certain component (or unit or module, etc.) is "connected" to another component in this article, it may mean that the component is directly connected to the other component, or it may mean that one component is indirectly connected to the other component. , meaning that there are other elements between the said element and another element. When it is stated that an element is "directly connected" to another element, it means that no other elements are interposed between the element and the other element. Terms such as first, second, third, etc. are only used to describe different components without limiting the components themselves. Therefore, the first component can also be renamed the second component. Moreover, the combination of components/units/circuit in this article is not a combination that is generally known, conventional or customary in this field. Whether the component/unit/circuit itself is common knowledge cannot be used to determine whether its combination relationship is easily understood in the technical field. Easily accomplished by the average person with knowledge.

Please refer to Figures 1 and 2 together. Figure 1 is a schematic flowchart illustrating the tool critical life diagnosis method 100 based on current information according to the first embodiment of the present invention; and Figure 2 is a schematic flow chart showing A schematic diagram of a tool critical life diagnosis system 200 based on current information according to the second embodiment of the present invention. As shown in the figure, the tool critical life diagnosis method 100 based on current information is applied to the tool critical life diagnosis system 200 based on current information, and is used to diagnose the critical life of the tool. The current information includes the current increase rate of 110.

In Figure 1, the tool critical life diagnosis method 100 based on current information includes the following steps: data acquisition step S02, current increase rate calculation step S04, and rate confirmation diagnosis step S06. Referring to FIG. 2 , the data acquisition step S02 includes an initial data acquisition step S022 and a real-time data acquisition step S024. The initial data acquisition step S022 includes driving the arithmetic processor 220 to acquire the new knife's initial load current and critical life threshold magnification from the memory 210 . The real-time data acquisition step S024 includes driving the arithmetic processor 220 to obtain the real-time load current of the tool. The current increase rate calculation step S04 includes driving the arithmetic processor 220 to calculate the current increase rate 110 of the tool. The current increase rate 110 is the ratio of the current load current to the initial load current of the new tool. The magnification confirmation diagnosis step S06 includes driving the arithmetic processor 220 to confirm whether the current increase magnification 110 is greater than or equal to the critical life threshold magnification to generate a magnification confirmation result, and diagnose whether the tool has reached the critical life based on the magnification confirmation result. In the magnification confirmation diagnosis step S06, when the magnification confirmation result is yes (i.e., the current increase magnification 110 is greater than or equal to the critical life threshold magnification), the arithmetic processor 220 determines that the tool has reached the critical life; when the magnification confirmation result is no (i.e., the current increase rate 110 is greater than or equal to the critical life threshold magnification) The increase magnification 110 is less than the critical life threshold magnification), and the operation processor 220 determines that the tool has not reached the critical life.

In Figure 2, the tool critical life diagnosis system 200 based on current information includes a memory 210 and a computing processor 220. The computing processor 220 is electrically connected to the memory 210 . Referring to Figure 1, the memory 210 stores the initial load current of the new tool and the critical life threshold multiple of the tool. The computing processor 220 is electrically connected to the memory 210 and receives the initial load current of the new blade and the critical life threshold magnification, and is configured to perform the real-time data acquisition step S024, the current increase magnification calculation step S04, and the magnification confirmation diagnosis step S06. Thereby, the tool critical life diagnosis method 100 based on current information and the tool critical life diagnosis system 200 based on current information of the present invention can diagnose the critical life of the tool through the current increase rate 110, and have a safer and safer measurement environment. It is simple, low-cost and requires a low amount of data to be processed per unit time, thereby realizing intelligent critical life diagnosis to solve the problem of conventional diagnosis that must be based on human experience rules and is too costly.

In the foregoing embodiments, the critical life threshold ratio may be greater than or equal to 1.3 and less than or equal to 1.5, and the preferred one is 1.4. The memory 210 may be a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions for execution by the computing processor 220, but the invention is not limited thereto. The computing processor 220 may be a processor (Processor), a microprocessor (Microprocessor), a central processing unit (CPU), a computer, a mobile device processor, a cloud processor or other electronic computing processors, but the present invention Not limited to this.

Please refer to Figures 1, 2, 3 and 4 together. Figure 3 is a schematic flow chart illustrating the tool critical life diagnosis method 100a based on current information according to the third embodiment of the present invention. ; and Figure 4 is a schematic diagram showing the relationship between load current and time of tool wear of the present invention. As shown in the figure, the tool critical life diagnosis method 100a based on current information can also be applied to the tool critical life diagnosis system 200 based on current information. The current information includes the current increase rate 110 and the current increase slope 120. The tool critical life diagnosis method 100a based on current information includes the following steps: data acquisition step S21, current increase rate calculation step S22, rate confirmation diagnosis step S23, current increase slope calculation step S24, and slope confirmation diagnosis step S25. The data acquisition step S21 includes an initial data acquisition step S212 and a real-time data acquisition step S214. The data acquisition step S21, the current increase rate calculation step S22, and the rate confirmation diagnosis step S23 are the same as the data acquisition step S02, the current increase rate calculation step S04, and the rate confirmation diagnosis step S06 in Figure 1, respectively, and will not be described again.

The current increase slope calculation step S24 includes driving the operation processor 220 to calculate the current increase slope 120 of the tool using a linear regression analysis method. The slope confirmation diagnosis step S25 includes driving the arithmetic processor 220 to confirm whether the current increase slope 120 increases to generate a slope confirmation result, and diagnose whether the tool is approaching the critical life (corresponding to the end point CL in Figure 4) based on the slope confirmation result, in which the current increase slope 120 It is the increase value of current per unit time. In the slope confirmation diagnosis step S25, when the slope confirmation result is yes (corresponding to the current increase trend 120b in Figure 4), the arithmetic processor 220 determines that the tool is approaching the critical life; when the slope confirmation result is no (corresponding to the current increase trend 120b in Figure 4) The current increasing trend 120a), the arithmetic processor 220 determines that the tool is not close to the critical life. In one embodiment, the linear regression analysis method may include a first-order linear regression analysis method, but the present invention is not limited thereto. Thus, the tool critical life diagnosis method 100a based on current information of the present invention diagnoses the critical life of the tool through the current increase rate 110 and the current increase slope 120. It not only has a safer and simpler measurement environment, low cost, and requires less time per unit time. Even if the amount of data processed is relatively low, stable wear (corresponding to the current increase trend 120a in Figure 4) or rapid wear (corresponding to the current increase trend 120b in Figure 4) can be judged during processing, thereby achieving intelligent processing. Critical life diagnostics.

Please refer to Figure 2 and Figure 5 together. Figure 5 is a schematic flowchart illustrating the data acquisition step S42, the current increase slope calculation step S44 and the data update step S46 according to the fourth embodiment of the present invention. The data acquisition step S42 includes steps S421, S422, S423, S424, and S425. Step S421 includes driving the arithmetic processor 220 to obtain the real-time load current of the tool. Step S422 includes driving the arithmetic processor 220 to confirm whether to collect current for X seconds, and if so, execute step S423; if not, execute step S421 again. Step S423 includes driving the arithmetic processor 220 to use the quartile method to remove X seconds of outliers from the current data. Step S424 includes driving the arithmetic processor 220 to average the top Y% large current values in the area. Step S425 includes driving the arithmetic processor 220 to confirm whether to collect the Z-stroke current average, and if so, execute the current increase slope calculation step S44; if not, re-execute step S421. The current increase slope calculation step S44 includes driving the operation processor 220 to calculate the average slope of the Z pen current using first-order linear regression (ie, first-order linear regression analysis method). The data updating step S46 includes driving the arithmetic processor 220 to remove the oldest current average among the Z pieces of data, thereby updating the current average. After the computing processor 220 completes the data update step S46, it will re-execute step S421 of the data acquisition step S42 until the tool reaches the critical life. In one embodiment, the values of X, Y, and Z can be 30, 20, and 6 respectively, but the invention is not limited thereto. In this way, the present invention can judge stable wear or rapid wear during processing through the data acquisition step S42, the current increase slope calculation step S44 and the data update step S46. It can not only reduce the interference caused by unnecessary data to the rule judgment, but also can The update frequency of the machining slope trend is shortened.

It can be seen from the above embodiments that the present invention has the following advantages: First, by diagnosing the critical life of the tool through the current increase rate and current increase slope, it not only has a safer and simpler measurement environment, but also has low cost and a relatively small amount of data to be processed per unit time. The efficiency is low, and then intelligent critical life diagnosis is realized to solve the problem that the conventional diagnosis must be based on human experience rules and the cost is too high. Secondly, stable wear or rapid wear can be judged during processing, which not only reduces the interference of unnecessary data on rule judgment, but also shortens the update frequency of the processing slope trend.

Although the present invention has been disclosed in the above embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make various modifications and modifications without departing from the spirit and scope of the present invention. Therefore, the protection of the present invention is The scope shall be determined by the appended patent application scope.

100,100a: Tool critical life diagnosis method based on current information 110: Current increase rate 120: current increase slope 120a,120b: current increasing trend 200: Tool critical life diagnosis system based on current information 210:Memory 220:Arithmetic processor CL: end point S02, S21, S42: Data acquisition steps S022, S212: Initial data acquisition steps S024, S214: Real-time data acquisition steps S04, S22: Current increase rate calculation steps S06, S23: Magnification confirmation diagnostic steps S24, S44: Current increase slope calculation steps S25: Slope confirmation diagnostic steps S421, S422, S423, S424, S425: steps S46: Data update steps

Figure 1 is a schematic flow chart illustrating a tool critical life diagnosis method based on current information according to the first embodiment of the present invention; Figure 2 is a schematic diagram of a tool critical life diagnosis system based on current information according to the second embodiment of the present invention; Figure 3 is a schematic flow chart illustrating a tool critical life diagnosis method based on current information according to the third embodiment of the present invention; Figure 4 is a schematic diagram illustrating the relationship between load current and time of tool wear according to the present invention; and FIG. 5 is a schematic flowchart illustrating the data acquisition step, the current increase slope calculation step and the data update step according to the fourth embodiment of the present invention.

100: Tool critical life diagnosis method based on current information

110: Current increase rate

S02: Data acquisition steps

S022: Initial data acquisition steps

S024: Steps to obtain real-time data

S04: Current increase rate calculation steps

S06: Magnification confirmation diagnostic steps

Claims (8)

A tool critical life diagnosis method based on current information is used to diagnose the critical life of a tool. The tool critical life diagnosis method based on current information includes the following steps: a data acquisition step, including: an initial data acquisition A step that includes driving a computing processor to obtain an initial load current of a new tool and a critical life threshold multiple from a memory; and a real-time data acquisition step that includes driving the computing processor to obtain a real-time load current of the tool; a. The current increase rate calculation step includes driving the computing processor to calculate a current increase rate of the tool, and the current increase rate is a ratio of the immediate load current to the initial load current of the new tool; the rate confirmation diagnosis step includes driving the The operation processor confirms whether the current increase rate is greater than or equal to the critical life threshold rate to generate a rate confirmation result, and diagnoses whether the tool has reached the critical life based on the rate confirmation result; a current increase slope calculation step includes driving the operation The processor uses a linear regression analysis method to calculate a current increase slope of the tool; and a slope confirmation diagnostic step includes driving the operation processor to confirm whether the current increase slope increases to generate a slope confirmation result, and confirm the result based on the slope Diagnose whether the tool is close to the critical life, wherein the current increase slope is one of the increasing values of the current per unit time. The tool critical life diagnosis method based on current information as described in claim 1, wherein in the magnification confirmation diagnosis step, when the magnification confirmation result is yes, the computing processor determines that the tool has reached the critical life; And when the magnification confirmation result is negative, the computing processor determines that the tool has not reached the critical life. The tool critical life diagnosis method based on current information as described in claim 1, wherein in the slope confirmation diagnosis step, when the slope confirmation result is yes, the computing processor determines that the tool is close to the critical life; and When the slope confirmation result is negative, the computing processor determines that the tool is not close to the critical life. As claimed in claim 1, the tool critical life diagnosis method based on current information, wherein the critical life threshold multiple is greater than or equal to 1.3 and less than or equal to 1.5, and the linear regression analysis method includes a first-order linear regression analysis method. A tool critical life diagnosis system based on current information is used to diagnose the critical life of a tool. The tool critical life diagnosis system based on current information includes: a memory that stores a new tool initial load of the tool. current and a critical lifetime threshold ratio; and A computing processor electrically connected to the memory and receiving the initial load current of the new tool and the critical life threshold magnification. The computing processor is configured to perform an operation including the following steps: a real-time data acquisition step, including obtaining the tool an immediate load current; a current increase rate calculation step, including calculating a current increase rate of the tool, which current increase rate is a ratio of the immediate load current to the initial load current of the new tool; a rate confirmation diagnostic step, including Confirm whether the current increase rate is greater than or equal to the critical life threshold rate to generate a rate confirmation result, and diagnose whether the tool has reached the critical life based on the rate confirmation result; a current increase slope calculation step, including using a linear regression analysis method Calculate a current increase slope of the tool; and a slope confirmation diagnostic step, including confirming whether the current increase slope increases to generate a slope confirmation result, and diagnosing whether the tool is close to the critical life based on the slope confirmation result, wherein the current increases The slope is the increase in current per unit time. The tool critical life diagnosis system based on current information as described in claim 5, wherein when the magnification confirmation result is yes, the computing processor determines that the tool has reached the critical life; and when the magnification confirmation result is If not, the arithmetic processor determines that the tool has not reaches this critical life. The tool critical life diagnosis system based on current information as described in claim 5, wherein when the slope confirmation result is yes, the computing processor determines that the tool is close to the critical life; and when the slope confirmation result is no When, the computing processor determines that the tool is not close to the critical life. As claimed in claim 5, the tool critical life diagnosis system based on current information, wherein the critical life threshold multiple is greater than or equal to 1.3 and less than or equal to 1.5, and the linear regression analysis method includes a first-order linear regression analysis method.

Images