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The optimization of energy efficiency of industrial processes is a desirable objective. Roll forming is a continuous sheet metal forming process gradually producing profile-shaped parts. Although cold forming processes are usually comparatively energy-efficient, in roll forming, an efficiency of deformation of 25% and less has been observed in previous studies. At the same time, a significant energy-saving potential has been identified by means of the evaluation of drive torques and the readjustment of tool velocities. However, the efficiency of deformation is still small in comparison to other cold forming processes. In this study, a new decision rule for optimizing the tool velocity in roll forming is proposed and tested experimentally and numerically. The objective of this decision rule is, firstly, to avoid decelerating drive torques that counteract the forming process and need to be compensated by additional drive torques in other process steps. Secondly, the accelerating drive torques should not exceed a certain limit in order to ensure an effective process with respect to wear and surface quality of the products. The results show that a reduction of the energy demand of 66% in comparison with a conventionally operated roll forming process is possible resulting in an efficiency of deformation of 89%. Furthermore, the mechanism of the optimization is validated experimentally using a novel sensor concept, a sensorial parallel key. [ABSTRACT FROM AUTHOR]

In this paper, a single-item capacitated lot-sizing problem in a flow-shop system with energy consideration is addressed. The planning horizon is split into T periods where each one is characterized by a duration, an electricity cost, a maximum peak power and a demand. This problem is NP-hard, since its simple version is known to be NP-hard. Therefore, to deal with the complexity and to find good quality solutions in a reasonable time, a fix-and-relax heuristic and a genetic algorithm are developed. Computational experiments are performed on different instances to show the efficiency of these proposed heuristics. To evaluate their performances, problems of different scales have been studied and analyzed. [ABSTRACT FROM AUTHOR]

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining. [ABSTRACT FROM AUTHOR]