Articles
Publication Date: 2023
International Journal of Advanced Manufacturing Technology (02683768)128(9-10)pp. 4003-4022
In this paper, a virtualized ball-end milling model is presented in Unity game engine environment, in which the machining process is simulated by proposing a new geometric approach. The model calculates and illustrates cutter-workpiece engagement area and cutting forces in a real-time manner. To calculate cutter-workpiece engagement, the workpiece surface is considered a set of nodes. Then, using a new geometric method, the engagement area is calculated at any node on the engaging surface. Utilizing the calculated engagement area and adopting mechanistic force model, the cutting forces applied from each node on the workpiece surface to the tool’s cutting edge are calculated. Adopting the proposed new geometric method simplifies the mechanistic force model to such an extent that the cutting forces are calculated in a real-time manner. The extracted cutter-workpiece engagement areas have been compared with solid model results, and the cutting forces have been compared with the available experimental data. Good agreement between the results proved that the model can calculate the engagement area and cutting forces accurately. By changing the geometrical parameters of the model, it was shown that the speed of analyses can be increased to such an extent that the machining process can be simulated faster than 30 frames per second. The presented model is compatible with any game engine and can be used for augmented reality applications and machining process monitoring. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Publication Date: 2023
International Journal of Advanced Manufacturing Technology (02683768)129(7-8)pp. 2949-2968
The mechanistic force model is one of the most common methods used to predict cutting forces in milling processes. In this model, the cutting forces are considered a function of cutting geometry and the material properties of the tool and workpiece, generally known as cutting coefficients. These coefficients are commonly identified by performing special calibration tests and are applied to predict cutting forces for other conditions. Although the mechanistic model is a powerful tool for predicting cutting forces, its accuracy decreases as the cutter-workpiece engagement geometry differs from the calibration tests. Thus, it is necessary to update the cutting coefficients to preserve the accuracy of the model. This paper proposes a real-time intelligent method, named “the mechanistic network,” for identifying and updating the cutting coefficients. To this end, an analogy between the mechanistic force model and artificial neural networks is identified, in which the weight coefficients of the artificial neural networks have been replaced with the cutting coefficients. To identify and update the cutting coefficients, an algorithm is proposed using stochastic gradient descent, which updates the coefficients in each iteration. In addition, some other important parameters in milling processes, such as the phase shift between the measured and predicted forces and run-out parameters, are calculated using stochastic gradient descent. The good performance of the proposed network is shown through case studies by utilizing reliable data existing in the literature and also by performing ball-end milling experimental tests. The results show that the proposed network can predict the cutting forces with an error of less than 10% and update the cutting coefficients with a calculation speed of 125k iterations per second. The robustness of the network against noise that may arise in real machining conditions is also shown. The proposed mechanistic network is a reliable and efficient tool that can be applied to real-time applications such as cyber-physical manufacturing systems and condition monitoring of machining processes. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
