Tool Path

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This paper presents the modeling and simulation of deformation of thin-wall section using finite element method
(FEM). A 3D non-linear numerical model was developed by employing Johnson-Cook material constitutive
model for aluminum 7075-T6 alloy. Johnson-cook damage law was adopted to account for damage initiation
and chip formation during cutting tool penetration into the work material. The deformation of thin-walled part
under the action of cutting forces during milling operation was studied for a set of process conditions and the
preliminary results are discussed.

Precise machining of thin-wall structures is an intensive and challenging process due to the low stiffness and geometric complexity associated with them. In-process deflection of thin-walls generally results in poor dimensional accuracy and surface finish of the product. This paper reports a systematic comparative study of various machining approaches on the quality characteristics of thin-wall components. Five machining approaches were studied and compared for their influence on the product wall thickness and surface finish. An approach with gradual increase in axial depth of cut while maintaining a constant feed rate resulted in better dimensional accuracy and surface finish as compared to the other machining approaches.

Open Access Article:
https://doi.org/10.3390/ma12172827,

Abstract:
Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654

In the last decades, several process models have been developed for simulation of 5-axis milling cycles, where the simulation results are used for parameter selection or process improvement purposes. However, integrating the process models with milling cycles is not a trivial task especially for tool path modification purposes in 5-axis free-form milling. This is mainly due to the fact that tool path modification requires the machined surface information, i.e. surface location and surface normal vector, to be known. However, this information is not explicitly given in the tool path, i.e. cutter location source (CLS), file. In this paper, a novel and practical approach is proposed to analytically calculate the surface location and surface normal vectors directly from the already generated tool path in the form of CLS file. The proposed approach is applied on representative 5-axis milling cycles, and the results are verified through CAD model comparisons. It is shown that the proposed approach can calculate the machined surface data at a reasonable accuracy depending on the
cutter location point density in the tool path file.

Open Access Article:
https://doi.org/10.3390/ma12172827,

Abstract:
Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.

Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654