Predictive modeling of turning operations under different cooling/lubricating conditions for sustainable manufacturing with machine learning techniques

Abstract

Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

“…Quality control capability (QCC) is considered an essential component of a manufacturing unit (Cica et al, 2020 ; Eslamirad et al, 2020 ; Rana et al, 2020 ). Silbernagel et al ( 2020 ) said that QCC is a process by which manufacturing firms can assess and review factors involved in a manufacturing firm’s production quality.…”

Progressing towards Sustainable Machining of Steels: A Detailed Review

“…Currently, the most widely used cooling/lubricating techniques with a low negative effect on the environment and human operator's health are dry cutting, cryogenic cooling, and minimum quantity lubrication (MQL), etc. [90].…”

“…To eliminate these problems, dry machining is used in which the need for cutting oils is eliminated. Dixit et al [90] reported that the use of dry machining significantly minimized air and water pollution. They called dry cutting an eco-friendly process.…”

Recent Advances on Machine Learning Applications in Machining Processes

“…Neural Network applications have been applied for tool tip dynamics, stability and optimization problems [36,44,87,88]. Misaka et al [89] considered Neural Networks, under the form of CNN, based on camera images of the metal cutting processing for machining parameters extraction, obtaining a model accuracy of 85.5% and a precision of 92.9%.…”

“…Modeling [43,44]: there are several applications that require to model and predict a phenomenon related to the machine technology, for example, it is useful to determine the correct material removal or the most suitable cooling/lubricating technology to improve the equipment sustainability.…”