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.
Highlights
Cutting fluids are traditionally used in metal cutting operations to improve the tool life, surface quality as well as entire machining process productivity
The results indicated that both methods can be utilized to predict the roughness value in dry turning, while the support vector regression model is preferable over response surface methodology (RSM) in high-pressure coolant (HPC) assisted turning
The performance of four methods were evaluated in terms of different statistical measures such as mean absolute percentage error, maximum absolute percentage error, the mean absolute error, normalized root mean square error and correlation coefficient and very good agreements with experimental results were observed
Summary
Cutting fluids are traditionally used in metal cutting operations to improve the tool life, surface quality as well as entire machining process productivity. Cutting fluids have negative effects on the human health and environment due to presence of potentially harmful chemicals [1]. The use of cutting fluids represents a considerable amount of total manufacturing costs [2]. Weinert et al [3] demonstrates that the estimated cost of the cutting fluids is around 7 to 17% of the aggregate machining costs. Conventional flood cooling is the most common cooling/lubricating technique used to improve machining performance. High cutting fluid consumption as well as power consumption, poor
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