Modelling and Prediction of Thrust Force and Torque in Drilling Operations of Al7075 Using ANN and RSM Methodologies

Abstract

Many developed approaches for the improvement of sustainability during machining operations; one of which is the optimized utilization of cutting tools. Increasing the efficient use of cutting tool results in better product quality and longer tool life. Drilling is one of the most popular manufacturing processes in the metal-cutting industry. It is usually carried out at the final steps of the production process. In this study, the effects of cutting parameters (cutting velocity, feed rate) and tool diameter on thrust force (Fz) and torque (Mz) are investigated in the drilling of an Al7075 workpiece using solid carbide tools. The full factorial experimental design is implemented in order to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and response surface methodology (RSM) approaches are used to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. RSM-and ANN-based models are compared, and it is clearly determined that the proposed models are capable of predicting the thrust force (Fz) and torque (Mz). Nevertheless, the ANN models estimate in a more accurate way the outputs used in comparison to the RSM models.

Influence of the Nose Radius on the Machining Forces Induced during AISI-4140 Hard Turning: A CAD-Based and 3D FEM Approach

“…The RSM is a well-established statistical tool that formulates a defined relation between two groups of data; one contains dependent variables and the other independent variables. This methodology was embraced by many researchers for prediction and optimization purposes during studies related to typical machining processes [2,3,15,[44][45][46] due to the fact that it is versatile and can generate both linear and quadratic models. The three levels of the cutting parameters used (see Table 1) and the number of the numerical tests led to a full factorial design with three factors and a total of twenty-seven experiments.…”

CAD-Based 3D-FE Modelling of AISI-D3 Turning with Ceramic Tooling

“…This way, the FE model can be validated, as well as the future experimental work on turning of AISI-D3 can be minimized. The RSM was chosen for this case, as it is a proven tool which is employed by many researchers for several purposes, such as optimization of machining conditions and prediction of cutting forces [40,[50][51][52]. With respect to the L 16 design, which includes the three factors and the four levels, the results from the 16 simulation runs were used for the development of the statistical model.…”

Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

“…Indirect monitoring is based on different cutting parameters by the different sensor signals such as thrust force [26][27][28], torque [29], current [30,31], spindle power [32], computer vision [33,34], vibration [35,36], acoustic emission [37], etc. These indirect parameter measurements are related to the tool condition.…”