Minimising the energy consumption of tool change and tool path of machining by sequencing the features

Abstract

and tool change plan will vary based on the different PSFP. This paper firstly aims to un-6 derstand the relationship between the PSFP and the energy consumption of tool change 7 and tool path during the feature transitions. Then, a model is introduced for the single ob-8 jective optimisation problem that minimises the energy consumption of machine tools 9 during the feature transitions which include all the tool path and tool change operations. 10Finally, optimisation approaches including depth-first search and genetic algorithm are 11 modified and applied to find the optimal PSFP which results in the minimisation of the 12 energy consumption of feature transitions (EFT). In the case study, the optimal and near-13 optimal sequences of features, in terms of the minimum EFT, of a 15 features part which 14 is processed by a machining centre have been found. The optimal PSFP achieves a 15

Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear

“…Garg et al [13] applied a variety of advanced intelligent optimization algorithms to model and analyze the energy consumption in the turning process, and finally found that cutting speed is the main factor affecting the power consumption of the machine tool. Based on the prediction model of material removal energy consumption, Some researchers found that when different processing technology planning and processing were adopted, the machine tool energy consumption was greatly affected [14,15]. Wang et al established a novel drilling energy consumption prediction model, which fully considers the impact of tool wear on energy consumption, and experimental results show that the model has high prediction accuracy [16].…”

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

“…Non-cutting energy consumption. Non-cutting energy consumption can be divided into three parts: tool path energy consumption, tool change energy consumption, and energy consumption of changes in spindle rotation speed (Hu et al, 2018). However, we assume that only one type of tool is used; hence, there is no tool to change the energy consumption in the model.…”

A review on graph search algorithms for optimal energy efficient path planning for an unmanned air vehicle

“…DFS incorporated Genetic algorithm to discover the optimal processing sequence of features of a part (PSFP) that reduces the feature transitions' energy consumption by 28.60 % [11]. A smaller search space was explored faster with reduced cost by another extended depth-first search (EDFS) algorithm [32].…”