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

Cica, Djordje (author), Sredanović, Branislav (author), Tešić, Saša (author), Kramar, Davorin (author)

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.

• Language: English
• Keywords: machine learning, sustainable machining, machining force, cutting power, cutting pressure
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: Str. 162-180
• Numbering: Vol. 20, no. 1/2
• PID: 20.500.12556/RUL-165000
• UDC: 621.9:004.85
• ISSN on article: 2210-8327
• DOI: 10.1016/j.aci.2020.02.001
• COBISS.SI-ID: 66118659
• Publication date in RUL: 20.11.2024

Record is a part of a journal

• Title: Applied computing and informatics
• Publisher: Elsevier
• ISSN: 2210-8327
• COBISS.SI-ID: 519427097

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: strojno učenje, trajnostno odrezavanje, odrezovalna sila, rezalna moč, rezalni tlak