Narrow Results

Energy Consumption (EC) in the process of mechanical manufacturing directly leads to environmental pollution and resource waste. However, the EC characteristics of machine tool processing are complex, and most energy-saving optimization models require accurate material performance data and cutting force models. In response to the above issues, the study first analyzes the structural composition of the machining system, clarifies the main variable parameters for optimization, and then establishes a mathematical model with the determined optimization variables to describe the EC characteristics. Finally, the established optimization model is solved using the adaptive particle swarm algorithm to find the optimal combination of process parameters and achieve energy-saving optimization. The improved adaptive particle swarm intelligence algorithm tends to converge after more than 50 iterations. When taking low cost and low EC as the optimization goal, the cutting EC of the optimization solution is 3.49 × 10⁵ J, the processing time is 42.68 s, and the processing cost is 46.71 points, and the processing cost and EC are between the single optimization goal of low cost and low EC. It is indicated that the proposed method provides a reasonable energy-saving optimization strategy for machining process parameters, and provides support for the implementation of energy-saving optimization of machining center process parameters.

In this paper, we investigate the well-known permutation flow shop (PFS) scheduling problem with a particular objective, the minimization of total idle energy consumption of the machines. The problem considers the energy waste induced by the machine idling, in which the idle energy consumption is evaluated by the multiplication of the idle time and power level of each machine. Since the problem considered is NP-hard, theoretical results are given for several basic cases. For the two-machine case, we prove that the optimal schedule can be found by employing a relaxed Johnson’s algorithm within O($n^2$) time complexity. For the cases with multiple machines (not less than 3), we propose a novel NEH heuristic algorithm to obtain an approximate energy-saving schedule. The heuristic algorithms are validated by comparison with NEH on a typical PFS problem and a case study for tire manufacturing shows an energy consumption reduction of approximately $5\%$ by applying the energy-saving scheduling and the proposed algorithms.