Method for Optimizing Energy Consumption in Machining Manufacturing Process

Abstract

With the rapid development of the economy and the continuous maturity of various industries and departments, the total amount of energy required by enterprises in the process of production and operation is also increasing year by year. To achieve sustainable development, it is necessary to adjust the current production and operation mode, reasonably evaluate the current state of resources, use advanced technology to optimize each link of production, and regard low-energy consumption and low carbon as the key to production improvement. In this paper, the integrated realization method of process planning and production scheduling based on intelligent algorithm is adopted, and the integrated model is solved by using the chromosome hierarchical coding genetic algorithm. When the alternative process schemes generated for each part through nonlinear process planning are given, the integrated model can weigh the optimization objectives and decide the suitable process route, machine tool selection scheme, and corresponding production scheduling scheme for each part. In this paper, a case study on the energy-saving effect of energy consumption optimization methods in machining and manufacturing processes is carried out. Taking a manufacturing enterprise as the background, the effectiveness of the proposed energy-saving method is verified by comparing the energy consumption of a batch of mechanical product parts in the process planning and production scheduling integrated mode and the traditional serial working mode.

Optimizing energy consumption in directed energy deposition-based hybrid additive manufacturing: an integrated modelling and experimental approach

“…The authors found that the Facebook Prophet algorithm improved more accuracy [18]. Kong et al demonstrated that with the integration process planning and scheduling, the energy consumption is less than the non-integrated process planning [19]. Few researchers have been able to draw on any structured research into energy consumption modelling on DED-based Hybrid Additive-Subtractive Manufacturing and the signi cance of their factor analysis.…”

Machine learning algorithm functional on environmental sustainability assessment in turbomachinery sector: Application on centrifugal compressors

“…Among the impact phases of a product's life cycle, manufacturing is not negligible [ 4 ], as it is related to natural resource extraction, energy consumption, and waste generation. This phase is widely analyzed in the literature from the perspective of optimizing consumption [ 5 , 6 ]. This paper investigates aspects of environmental sustainability in the manufacturing phase of components used in the turbomachinery field.…”