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.
Highlights
Technology, School of Mechanical Science and Engineering, HuazhongUniversity of Science and Technology, Wuhan, P.R
Very limited studies have been reported using response surface methodology (RSM) coupled with multiobjective genetic algorithm (MOGA) for minimum quantity lubrication (MQL)– assisted milling process of AISI 1045 material, especially on multiple objective optimisation of surface roughness and power consumption of computer numerical control (CNC) machine tool
After performing statistical analysis on the parameters and responses, the BBD based on RSM suggested quadratic model for both the surface roughness and power
Summary
University of Science and Technology, Wuhan, P.R. China 2Department of Management & HR’, NUST Business School, National. Very limited studies have been reported using RSM coupled with MOGA for minimum quantity lubrication (MQL)– assisted milling process of AISI 1045 material, especially on multiple objective optimisation of surface roughness and power consumption of CNC machine tool. This study seeks to optimise the effects of different machining parameters on surface roughness of AISI 1045 material and power consumption during end-milling operation, using hybrid RSM-MOGA concomitantly. Estimation of the optimum parameters for machining operation has been a great concern of manufacturing industries, because of the machining cost which plays an effective role in products manufacturing The novelty of this innovative study includes the integration of low-cost power measurement system, MQL technique with end-milling process and multi-objective optimisation of machining parameters using RSM and MOGA approaches.
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