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

Abstract

The natural energy crisis and the increasingly serious environmental problems have imposed all industries to reduce energy consumption. During milling process, selecting a correct cutting parameters can not only greatly improve production quality and processing efficiency, but also can reduce energy consumption, in addition, tool wear also has a great impact on them. Therefore, a milling power consumption model of CNC machine tools is established based on modern machining theory is established in this article, unlike traditional energy consumption models, our model takes full account of cutting conditions and tool wear. The surface roughness of parts is one of the important indicators to measure the machining quality of machine tools. Therefore, taking milling process as research object, a multi-objective cutting parameters optimization model that takes the machining surface roughness, material removal rate (MRR) and machining energy consumption as the optimization goals was established. Furthermore, an intelligent optimization algorithm was proposed based on improved Teaching-Learning-Based Optimization (TLBO) to solve the model under various limited milling conditions. Finally, comparing experimental results of optimized parameter and empirical parameters, it shows that goals of reducing energy consumption, improving productivity and machining quality can be achieved by optimizing cutting parameters.

Statistical analysis, modeling and multi-objective optimization of parameters intermittent turning process of AISI D3

“…In conclusion, we can say that the choice of an optimal cutting speed in the context of intermittent machining depends on the objective. Either the search for maximum surface quality, maximum productivity, minimum energy consumption, minimum production cost of machined parts or also, the combination of one, two or all the objectives together [23,[49][50][51].…”

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

“…The horizontal axis is power (P), the vertical axis is time (T), and on the right is the CNC programs corresponding to each machining stage. 3 Spindle start; 4 Quickly locate to the starting point; 5 Linear interpolation; 6 Quickly locate the starting point; 7 Linear interpolation; 8 Quickly locate to the tool change point; 9 Change tool; 10 Quick positioning to the milling point; 11 Milling ; 12 Quickly locate to the retreat point; 13 Quickly locate to the safe point; 14 Standby; 15 Shut down.…”

“…At this stage, the tool contacts the workpiece and starts cutting. Due to the cutting depth of stage 7 being deeper than the cutting depth of stage 5 , stage 7 is more powerful than stage 5 . Stage 11 is milling only in Z direction, and the feed rate is reduced, so the power of stage 11 is different from stages 5 and 7 .…”

“…have varying degrees of impact on machining energy consumption [4]. Therefore, how to accurately and 2 of 16 efficiently predict the energy consumption of CNC machine tools has become a difficult hot spot within energy research [5]. Sihag conducted a systematic literature review on machine tool energy consumption and a six-level hierarchical model was proposed for better understanding of machining energy classification [6].…”

Method for Optimizing Energy Consumption in Machining Manufacturing Process

“…Excessive pursuit of the speed of economic development will only lead to waste and shortage of resources. erefore, in addition to pursuing higher economic pro ts in the daily production and operation process, more and more people are paying more attention to the rational allocation and utilization of resources in all walks of life, striving to minimize waste and reduce the pressure caused by environmental and resource problems to the long-term and sustainable development of the economy [1][2][3]. Especially in traditional industries such as mechanical processing and manufacturing, more attention should be paid to the rationalization of energy development and utilization and to achieve sustainable economic development by optimizing energy consumption.…”