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Selective laser sintering technology is a method of additive manufacturing that is growing with widely application. Due to the increasing tense of energy situation, it is also timely to consider the economic and environmental issues of growth in additive manufacturing. The innovative selective laser sintering technology optimization approach proposed in this article encourages and enables the designers and users to obtain optimal sintering parameters and reduces energy consumption and cost in sintering process. This paper creates a potential approach for realizing the relationship between main sintering parameters and energy consumption and material cost. To achieve high efficiency of the process, optimization of parameters based on energy and cost consumption are investigated. A multi-objective model with optimized constraints is set up and solved by non-dominated sorting genetic algorithm II. Energy consumption and material cost are treated as the two objectives, which are affected by three variables, namely scanning speed, gap distance and layer thickness. The effectiveness of the multi-objective optimization model was verified experimentally and results are fully discussed. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption of machine tool, the process parameters were optimized by the optimization model with the cutting specific energy consumption (CSEC) and the processing time. Based on the energy consumption characteristics of the numerical control (NC) machine tool, the energy consumption module of NC machine tool was analyzed. The power composition of machining process which was based on energy consumption module was proposed, and then CSEC was given with the cutting process parameters. The optimization model to minimum cutting specific energy consumption and minimum processing time was established with the process parameters under the actual constraint conditions in the manufacturing process. The multi-objective optimization model was transformed into the single objective optimization model by introducing the subjective and objective comprehensive weights and solved by the quantum genetic algorithm. When the optimization goal was to trade off processing time and CSEC to reduce the energy consumption, the selecting of the milling parameters should consider the complex effect if the constraints could be met with, large feed speed and large milling depth could benefit if the constrains of milling process were met with. The processing energy consumption of optimized process parameters decreased 27.21% compared with that of preferred process parameters while CSEC was reduced 32.07% and the processing time was reduced 34.11%. The proposed approach in this paper provided an efficient solution to reduce the impact of the environment caused by energy consumption and to realize the sustainable manufacturing. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing concern over the environmental impact of traditional manufacturing, especially in terms of energy consumption and related emissions of carbon dioxide. Besides the adoption of new equipment, production scheduling could play a key role in reducing the total energy consumption of a manufacturing plant. In this paper, we explicitly introduce the objective of minimizing energy consumption into a typical production scheduling model, i.e., the job shop scheduling problem, based on a machine speed scaling framework. To solve this bi-objective optimization problem, we propose a multi-objective genetic algorithm incorporated with two problem-specific local improvement strategies. These local improvement procedures aim to enhance the solution quality by utilizing the mathematical models of two restricted subproblems derived from the original problem. Comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approach. The results presented in this work may be useful for future research on energy-efficient production scheduling. [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]