Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

Abstract

Abstract:The present paper investigates the influence of main cutting parameters on the machinability during turning process for three typical materials namely AISI D6 tool steel, Ti6Al4V ELI and CuZn39Pb3 brass, all three under dry cutting environment. Spindle speed, feed rate and depth of cut were selected for study whilst arithmetic surface roughness average (R a ) and main cutting force component (F C ) were treated as quality objectives characterizing machinability. For the aforementioned materials a full factorial design of experiments was conducted to exploit main effects and interactions among parameters it terms of quality objectives. The results obtained from dry turning experiments were utilized as a data set to test, train and validate a feed-forward back propagation artificial neural network for machinability prediction regarding all three materials. The work presents the results obtained from the aforementioned experimental effort under an extensive state-of-the-art survey concerning neural network technology and implementation to machining optimization problems.

Machinability evaluation and screening of leaded and lead-free brasses using a non-linear robust multifactorial profiler

“…The optimal cutting parameters such as speed, feed and depth of cut as a multi-criterion plain turning problem were defined and experimentally confirmed, using a non-linear design of experiments approach [8][9][10]. The main objective of this study was to concurrently screen and optimize one quality and one process characteristic-chip morphology and power consumption-against the four controlling factors-speed, feed, depth of cut and alloy type-using multilevel Taguchi experimental designs and nonparametric data analysis.…”

Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey Wolf algorithm

“…In general, this force is represented by three components, namely, the power component (Fc), the radial component (Fr) and the axial (or feed) component (F f ) as shown in Fig.1b. Of these three components, the greatest, usually, is the power component, which is often called the main cutting force (Fc) [7]. The material under investigation was a CuZn39Pb3 (CW614N-brass 583) brass alloy of 130 HB hardness.…”

“…Metal cutting operations are widespread in manufacturing industry and the prediction and/or the control of the resulted machinability always is of great importance. One basic machinability parameter is the surface roughness, as it is closely associated with the quality, reliability and functional performance of components [6,7]. Another one is the cutting force system developed; it is needed for the estimation of power requirements and for the design of machine tool elements, tool-holders and fixtures, adequately rigid and free from vibration.…”

“…friction, wear, state of lubrication) are highly dependent on profile shape [7,[11][12][13]. Cutting forces occur when extreme conditions encountered at the tool-workpiece contact [7,14,15]. This interaction can be related to tool wear and failure.…”

“…Soft computing/ artificial intelligence methods include neural network (NN), fuzzy set theory, genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) and particle swarm optimization (PSO); see Refs. [7,14]. The present study investigates the influence of cutting parameters on machinability indicators such as the main cutting force Fc and surface roughness parameters Ra and Rt when longitudinally turning CuZn39Pb3 brass alloy.…”