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

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

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.

• Jezik: Angleški jezik
• Ključne besede: machine learning, sustainable machining, machining force, cutting power, cutting pressure
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2024
• Št. strani: Str. 162-180
• Številčenje: Vol. 20, no. 1/2
• PID: 20.500.12556/RUL-165000
• UDK: 621.9:004.85
• ISSN pri članku: 2210-8327
• DOI: 10.1016/j.aci.2020.02.001
• COBISS.SI-ID: 66118659
• Datum objave v RUL: 20.11.2024

Gradivo je del revije

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

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, trajnostno odrezavanje, odrezovalna sila, rezalna moč, rezalni tlak