Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

• DOI: 10.1007/s00170-019-04327-4
• Publication Date: 2019-10-19T16:14:56Z
• Authors: Adel Taha Abbas; Danil Yurievich P...; Ivan Nikolaevich ...; Tadeusz Mikolajczyk; Mahmoud Sayed Sol...; Magdy Mostafa El ...
• Keywords: Cnc milling; Perceptron; Orthogonal array

Abstract Computer Numerical Control (CNC) face milling is commonly used to manufacture products from high-strength grade-H steel in both the automotive and construction industry. The various operations for these components have key performance indicators: accuracy, surface roughness ( Ra ), machining time removal of a unit volume min/cm 3 T m ). specified values each component achieved based on prototype specifications. However, poor adherence specifications can result rejection machined parts, implying extra production costs raw material wastage. An algorithm using an artificial neural network (ANN) with Edgeworth-Pareto method presented this paper optimize cutting parameter CNC face-milling operations. set parameters are adjusted improve minimal unit-volume rates, thereby reducing improving accuracy. ANN designed Matlab, 3–10-1 Multi-Layer Perceptron (MLP), which predicts workpiece accuracy ± 5.78% within range experimental angular spindle speed, feed rate, depth. unprecedented Pareto frontier was obtained finished that then determine optimized conditions. Depending objective, one or other two sets optimum conditions be used: first minimum power, while maximum slight increase (under 5%) costs.