Multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm. (April 2019)

• Record Type: Journal Article
• Authors: Mumtaz, Jabir; Li, Zhang; Imran, Muhammad; Yue, Lei; Jahanzaib, Mirza; Sarfraz, Shoaib; Shehab, Essam; Ismail, Sikiru Oluwarotimi; Afzal, Kaynat
• Is Part Of: Advances in mechanical engineering. Volume 11:Number 4(2019)
• Publication Date: 2019-04
• Subjects: Response surface methodology -- Box–Behnken design -- multi-objective genetic algorithm -- end-milling -- machiningparameters -- surface roughness -- power consumption; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814019829588
• Languages: English
• ISSNs: 1687-8132

Abstract

Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication–assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication–assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.