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Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. However, the ever increasing demand of customers for quality has led to better surface finish and thus more energy consumption. The energy efficiency of machines tools is generally very low particularly during the discrete part manufacturing. This paper provide a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters. The statistical significance of the proposed predictive model has been tested by the analysis of variance (ANOVA) test. The obtained results indicate that feed is the most significant machining parameter followed by depth of cut and cutting speed to reduce power consumption and surface roughness. The constructed response surface contours can be used by the shop floor people to find and use the best combination of machining parameters for the given situation. The reduction of peak load through optimization will results in lowering the power consumption of the machine tools during non-cutting idling time. [ABSTRACT FROM AUTHOR]

Abstract: Reducing energy usage is an essential consideration in sustainable manufacturing. In the past, metal cutting operations have been mainly optimized based on economical and technological considerations without the environmental dimension. It is essential to improve production rate and cutting quality while simultaneously mitigating the effect of manufacture on the environment. This paper presents a multi-objective optimization method based on weighted grey relational analysis and response surface methodology (RSM), which is applied to optimize the cutting parameters in milling process in order to evaluate trade-offs between sustainability, production rate and cutting quality. Three objectives, such as surface roughness, material removal rate and cutting energy, are simultaneously optimized. The parameters evaluated are spindle speed, feed rate, depth of cut and width of cut. The grey relational grade values for the multiple responses are obtained using weighted grey relational analysis. The weighted grey relational analysis is a quantitative method we proposed to determine the weight factors of multiple responses for grey relational analysis. Based on weighted grey relational analysis and RSM, the optimal milling parameters were identified, and the obtained results indicated that width of cut is the most influential parameter. Experiments using Taguchi design method were performed to verify the proposed optimization method and promising results were achieved. The experimental results indicate that the proposed optimization method is a very useful tool for multi-objective optimization of cutting parameters. Moreover, the results also show that low spindle speed cutting is more energy efficient than cutting at initial speed for milling process and the traditional multi-objective optimization does not satisfy the requirement for sustainable machining. [Copyright &y& Elsevier]

Presented in this paper is a methodology for evaluation of dry turning process along with optimal machining parameters, which allows a compromise between technological, economical, and ecological requirements. Dry longitudinal turning of Ni-based superalloy - Inconel 601 workpiece using three turning inserts coated with TiAlN + AlCr 2 O 3 by physical vapour deposition was investigated. The machining was performed at three different levels of cutting speed, feed, and depth of cut. During machining, power characteristics were measured in the form of current and interphase voltage. Machined parts and turning inserts were tested for surface roughness and tool life, respectively. Machining times, power and energy consumption were also calculated. Regression models were defined for arithmetical mean roughness, flank wear, and power. Negative environmental impact of the cutting process was monitored through midpoint and endpoint life cycle assessment (LCA). Finally, multi-objective optimization of output functions was performed. Optimization problem of minimizing the objective function was solved for eight output functions which characterize the process. The objective functions within this problem are: minimization of surface roughness, minimization of flank wear, minimization of cutting time, minimization of energy consumption, and minimization of four LCA indicators. The results indicate that dry turning of Ni-based superalloy - Inconel 601 requires higher turning inserts radii, higher cutting speeds, and smaller feeds and depths of cut. This allows acceptable flank wear of turning insert and required surface quality of workpiece, while drastically reducing cutting time, energy consumption and negative impact on the environment. The results also indicate that, within the set limitations and conditions of this experiment, increase of cutting speed can be a good strategy for reducing energy consumption and diminishing the impact of machining on the environment. • The longitudinal turning of an Inconel 601 in dry environment is presented. • Current, voltage, mean roughness, and flank wear are measured; while time, power and energy consumption are calculated. • Life cycle assessment is applied to quantify the negative environmental impacts. • Multi-objective optimization is applied to evaluate input variables of the process. • The results indicate that increasing cutting speed can be a good strategy for cleaner production. [ABSTRACT FROM AUTHOR]