Abstract
Computer Numerical Control (CNC) face milling is commonly used to manufacture products from high-strength grade-H steel in both the automotive and the construction industry. The various milling operations for these components have key performance indicators: accuracy, surface roughness (Ra), and machining time for removal of a unit volume min/cm3 (Tm). The specified surface roughness values for machining each component is achieved based on the prototype specifications. However, poor adherence to specifications can result in the rejection of the machined parts, implying extra production costs and raw material wastage. An algorithm using an artificial neural network (ANN) with the Edgeworth-Pareto method is presented in this paper to optimize the cutting parameter in CNC face-milling operations. The set of parameters are adjusted to improve surface roughness and minimal unit-volume material removal rates, thereby reducing production costs and improving accuracy. An ANN algorithm is designed in Matlab, based on a 3–10-1 Multi-Layer Perceptron (MLP), which predicts the Ra of the workpiece surface to an accuracy of ± 5.78% within the range of the experimental angular spindle speed, feed rate, and cutting depth. An unprecedented Pareto frontier for Ra and Tm was obtained for the finished grade-H steel workpiece using an ANN algorithm that was then used to determine optimized cutting conditions. Depending on the production objective, one or the other of two sets of optimum machining conditions can be used: the first one sets a minimum cutting power, while the other sets a maximum Tm with a slight increase (under 5%) in milling costs.
Highlights
Nowadays, face milling is widely used in many industries such as machine and machine tool building, the automobile industry, etc. [1]
The objective of this paper is to establish the facemilling conditions of grade H steel that provide for either minimum cutting power or maximum machining time per unit volume, Tm, while maintaining the design Ra, and taking into account the cost of machining based on an artificial neural network (ANN) model for predicting surface roughness
The optimization criteria in the milling of the cubic workpiece were established as f1 surface roughness (Ra, μm) and f2 machining time per unit volume (Tm, min/cm3), i.e., m = 2
Summary
Face milling is widely used in many industries such as machine and machine tool building, the automobile industry, etc. [1]. Grade-H steel materials have various uses and many industrial applications, such as cold-formed components in the automobile industry, among. Int J Adv Manuf Technol (2019) 105:2151–2165 others, on account of their high tensile strength. Increased rigidity of mechanical components can be achieved by using high-strength grade-H steel with no need for further reinforcements. Some examples of precision face milling of high-pressure products from grade-H steel are breech rings and breech blocks for the manufacturing of basic parts of heavy cannons. The development of resource-saving technologies including those for face milling is becoming crucial. Optimal employment of resources is an important task when machining costly essential components from gradeH steel, as well as surface quality. With the fast advance in technological and computational fields, analyzing large bodies of data using AI becomes increasingly relevant
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