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 (Ra) and main cutting force component (FC) 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.
Highlights
Machining is one among the four popular manufacturing processes, the other three being forming, casting, and joining [1]
The present paper aims to the development of an artificial neural network capable of estimating/predicting the main cutting force (Fc) and surface quality (Ra)
It is mentioned here that Response Surface Methodology (RSM) and Taguchi's Design of Experiments (DOE) are constitute trustworthy optimization strategies when it comes in machining but they are not presented since they are out of the scope in the present study
Summary
Machining is one among the four popular manufacturing processes, the other three being forming, casting, and joining [1]. Among these conventional machining processes, the attention has been paid to turning which is a type of a material removal operation where a cutting tool is used to remove material from a revolved raw stock aiming to produce a final product. Due to inadequate knowledge of the complexity of the process and factors affecting the surface integrity in turning operation [2], an improper decision may cause high production costs and low machining quality. The proper selection of cutting tools and process parameters for achieving high cutting performance in a turning operation is a critical task [3]
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