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machining parameter optimization for a batch milling system using multi-task deep reinforcement learning

JOURNAL OF MANUFACTURING SYSTEMS 2025年 78卷 124-152页

作者： Wang, Pei Cui, Yixin Tao, Haizhen Xu, Xun Yang, Sheng Xidian Univ Sch Mechanoelect Engn Xian 710071 Peoples R China Xidian Univ State Key Lab Electromech Integrated Mfg High Perf Xian 710071 Peoples R China Univ Auckland Dept Mech & Mechatron Engn Auckland 1142 New Zealand Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada

The integrated multi-objective optimization of machining parameters for improved machining quality and efficiency is important in batch milling systems. Due to the change of the batch milling system state, the continuous use of the same machining parameters may lead to degradation in quality and efficiency for workpieces in batches. machining parameter optimization is usually determined by manual experience or trial-and-error methods, making it difficult to achieve a synergistic consideration of both quality and efficiency. To address this issue, a novel multi-task deep reinforcement learning method for machining parameter optimization in a batch machining system is proposed. Firstly, a reliable parallel joint estimation model of multiple machining quality and efficiency indicators is established using a multi-task time series estimation method, which can learn the correlation of these indicators to improve estimation accuracy. Then, the parameter optimization problem is formalized as a Markov decision process supported by a reinforcement learning virtual environment and an agent. The reinforcement learning virtual environment with the joint estimation model is constructed to improve the accuracy of optimized machining parameters for the collaborative optimization of quality and efficiency indicators. Within the virtual environment, time series sequential state, sequential action, multi-objective reward function, and constraint conditions adapted to the joint estimation model are defined to repeatedly evaluate different machining parameters. The agent with a multi-head attention and a dynamic weight adjustment mechanism is designed to improve the stability of the optimization process. Finally, experiments on a real machining dataset of thin-walled parts show that compared with the traditional deep reinforcement learning algorithm, the optimization effect of the proposed framework is improved by 9 %-12 %, and the standard deviation is decreased by 9 % -18 %.

关键词： Deep reinforcement learning Multi-task learning Time series estimation machining parameter optimization

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Energy-aware machining parameter optimization Using Flower Pollination Algorithm

Energy-aware Machining Parameter Optimization Using Flower P...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Liu, Jianxing Li, Xiaoxia Sui, Zhibo Huazhong Agr Univ Coll Informat Wuhan Peoples R China

ISBN: (纸本)9781665442077

machining parameter optimization can be an effective way to reduce the energy consumption of machining processes and contribute to sustainable manufacturing. In this paper, the energy-aware machining parameter optimization problem is described and formulated. Based on the description and formula, an approach which considers multiple machining parameters including spindle speed, feed rate, depth of cut and width of cut simultaneously is developed. This approach consists of two key steps: modelling the energy consumed in the machining process and searching for the optimal machining parameters. In the modelling step, a hybrid modelling method which integrates regression and artificial neural networks (ANN) is developed to characterize the relationship between energy consumption and the machining parameters and obtain the energy consumption prediction model. In the searching step, an improved flower pollination algorithm (FPA) is presented to achieve the further searching for the optimal machining parameters with energy consideration. The approach has been tested and validated on multiple groups of experimental data. The results demonstrate that the approach can lead to a more accurate energy consumption prediction and an optimal solution for machining parameters.

关键词： sustainable manufacturing machining parameter optimization artificial neural networks regression flower pollination

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An improved wolf pack algorithm for sustainable machining parameter optimization 3

An improved wolf pack algorithm for sustainable machining pa...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Zhao, Dongsheng Li, Xiaoxia Yang, Jie Huazhong Agr Univ Coll Sci Wuhan 430072 Hubei Peoples R China Huazhong Agr Univ Coll Informat Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9781538645093

machining parameter is one of the influence factors of energy consumption in numerical control machining process. In this paper, spindle speed, feedrate, cutting depth and cutting width are selected as the machining parameters to be optimized. The artificial neural network is used to establish an energy consumption model to characterize the relationship between energy consumption and the machining parameters. Based on the energy consumption model, an improved wolf pack algorithm is presented to search for optimal machining parameters to minimize the energy consumed in machining process. The method has been tested and verified based on experimental data. The experimental results show that this optimization method is very effective for the sustainable machining parameter optimization.

关键词： machining parameter optimization sustainable manufacturing wolf pack algorithm artificial neural network

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Harmonic Differential Quadrature Method for Surface Location Error Prediction and machining parameter optimization in Milling

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2015年第2期137卷 024501-024501页

作者： Ding, Ye Zhang, XiaoJian Ding, Han Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper presents a semi-analytical numerical method for surface location error (SLE) prediction in milling processes, governed by a time-periodic delay-differential equation (DDE) in state-space form. The time period is discretized as a set of sampling grid points. By using the harmonic differential quadrature method (DQM), the first-order derivative in the DDE is approximated by the linear sums of the state values at all the sampling grid points. On this basis, the DDE is discretized as a set of algebraic equations. A dynamic map can then be constructed to simultaneously determine the stability and the steady-state SLE of the milling process. To obtain optimal machining parameters, an optimization model based on the milling dynamics is formulated and an interior point penalty function method is employed to solve the problem. Experimentally validated examples are utilized to verify the accuracy and efficiency of the proposed approach.

关键词： milling time delay semi-analytical method surface location error differential quadrature method machining parameter optimization

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Study of machining parameter optimization in high speed milling of Inconel 718 curved surface based on cutting force

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第1-4期75卷 269-277页

作者： Ma, Jian-wei Wang, Fu-ji Jia, Zhen-yuan Xu, Qiang Yang, Yan-yu Dalian Univ Technol Sch Mech Engn Minist Educ Key Lab Precis & Nontradit Machining Technol Dalian 116024 Peoples R China

Nickel-based alloy Inconel 718 is a kind of difficult-to-machine material, and the development of highspeed milling technology provides an effective machining method for the Inconel 718 parts. As the machining parameters play a crucial role in the high-speed milling of Inconel 718 parts, a lot of researches on the optimization of machining parameters have been conducted, and the optimum machining parameters under different test conditions have been obtained according to the experiments of flank milling or slot milling of Inconel 718. However, the machining parameter optimization in high-speed milling of Inconel 718 curved surface is seldom involved. The variation of the geometric features of the curved surface results in the sharp fluctuation of the cutting force as well as the vibration of the machine tool, which not only makes a severe impact on the surface machining quality and the tool life but also greatly affects the efficiency of the highspeed milling. In the machining process of the Inconel 718 parts with curved surface, the change of cutting force in highspeed milling should be smooth as far as possible. In this study, the experimental study is carried out on machining parameter optimization in high-speed milling of Inconel 718 curved surface based on the cutting force. The relationship between the cutting force and the spindle speed is analyzed, and in particular, the machining parameter optimization is investigated. The experiment results indicate that the cutting force relates to the geometric feature of curved surface, the cutting force is relatively smaller and the change of cutting force is smoother when the spindle speed changes from 4,000 to 4,500 rpm, the effect of the feed per tooth on the cutting force is little, and the cutting depth should be small to lessen the fluctuation of the cutting force. The research results can be considered to improve the machining quality of Inconel 718 parts with curved surface and will enrich the processing and man

关键词： machining parameter optimization High speed milling Curved surface Inconel 718 Cutting force

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Energy optimization method for variable curvature contour machining

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第5-6期132卷 2187-2207页

作者： Ma, Junyan Liu, Jiangyou Wei, Lutao Lu, Juan Liao, Xiaoping Ou, Chengyi Guangxi Univ Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China Beibu Gulf Univ Dept Mech & Marine Engn Qinzhou 535011 Peoples R China

Energy saving and consumption reduction is one of the current important research in the field of green and sustainable manufacturing. Products or components containing variable curvature contouring are widely used in the automotive, medical, aerospace, and mold industries, while there is a lack of methods to model the energy consumption ratio for variable curvature contouring and improve its energy efficiency. A method for modeling the specific energy consumption of variable curvature contouring and energy consumption optimization is proposed for this problem. Firstly, the components of energy consumption in processing of the CNC machine tool machining are analyzed, and the relationship between curvature characteristics and material removal rate is investigated from the geometric perspective. Secondly, orthogonal experiments with different curvatures of straight lines, convex arcs, and concave arcs are designed to collect energy consumption data. Based on the experimental data, the Dueling Deep Q-Network optimization support vector regression (Dueling DQN-SVR) was used to establish the specific energy consumption model considering the curvature. Finally, a multi-objective optimization model is constructed when considering specific energy consumption, efficiency, and quality, and the Pareto solution set is solved using a multi-objective Gray Wolf optimization algorithm (MOGWO). The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to select the optimal combination of machining parameters. The experimental results show that the accuracy of the established model is more than 95%. The method improved energy efficiency by more than 7.82% and efficiency by more than 1.128%. These research results are of great theoretical and practical significance for achieving energy-efficient variable curvature contour machining.

关键词： Variable curvature contouring Specific energy consumption machining parameter optimization Energy saving

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Study on tool wear in longitudinal-torsional composite ultrasonic vibration-assisted drilling of Ti-6Al-4V alloy

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第7-8期113卷 1989-2002页

作者： Tian, Yingjian Zou, Ping Kang, Di Fan, Fan Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

Severe tool wear is always considered as a main negative result in the process of drilling Ti-6Al-4V alloy. Although the idea of employing one-dimensional longitudinal ultrasonic vibration in the drilling process to reduce tool wear has been proposed for many years, studies on longitudinal-torsional composite ultrasonic vibration-assisted drilling (LT-UAD) are seldom reported. LT-UAD is a hybrid machining process in which high-frequency and low-amplitude vibrations are superimposed simultaneously on the rotational direction and the axial direction of the drilling bit respectively. In this study, a hollow vibration converter with thread grooves is presented for LT-UAD purpose. Based on an in-depth kinematic analysis, a condition for separating the tool from the workpiece is derived, indicating that a lower feed rate, a lower spindle speed, and a higher amplitude are more advantageous. machining experiments are performed, wherein good agreement is found between the experimental and theoretical results. The results show that LT-UAD has obvious advantages over longitudinal ultrasonic vibration-assisted drilling and conventional drilling in reducing tool wear. Additionally, the influences of the vibration amplitude, spindle speed, and feed rate on the flank wear width are analyzed, revealing the influences of the three factors in descending order are feed rate, amplitude, and spindle speed. The optimum machining parameters are obtained as 23 mu m of amplitude, 385 r/min of spindle speed, and 0.06 mm/r of feed rate. Furthermore, a reliable regression model of flank wear width is established based on response surface methodology. With this prediction model, the flank wear width of drill bit under different machining parameters can be accurately predicted.

关键词： Ti-6Al-4V alloy Longitudinal-torsional composite vibration Ultrasonic vibration assisted drilling Tool wear machining parameter optimization Prediction model

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Flow shop scheduling with low carbon emission and variable machining parameters

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2019年第5期233卷 1561-1572页

作者： Lin, Wenwen Tian, Guangdong Li, Zhiwu Zhang, Yuejun Zhang, Chaoyong Ningbo Univ Fac Mech Engn & Mech 818 Fenghua Rd Ningbo 315211 Zhejiang Peoples R China Jilin Univ Sch Transportat Changchun Jilin Peoples R China Xidian Univ Sch Electromech Engn Xian Shaanxi Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Hubei Peoples R China

Synergistic improvement on low carbon manufacturing is not taken full consideration in flow shop scheduling in literatures. To fill the gap, this article extends a flow shop scheduling problem by considering low carbon emission and variable machining parameters. The difference between the extended scheduling problem and the traditional one is that the former investigates the comprehensive effects of machine and scheduling levels on production throughput and environmental impact. A mathematical model of the extended problem is formulated to find the Pareto frontier, which is the set of scheduling solutions that are all Pareto efficient. Moreover, two problem-specific scheduling methods are presented and their effects on the considered problem are proved. A novel multi-objective heuristic is developed to deal with the extended problem because it is a non-deterministic polynomial acceptable problem. An actual case is studied to demonstrate the feasibility and applicability of the proposed extended problem and two scheduling methods. These finds can be employed in manufacturing execution systems and do not require additional cost.

关键词： Low carbon manufacturing flow shop scheduling machining parameter optimization multi-objective optimization grey wolf optimizer

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optimization of machining parameters Based on Principal Component Analysis and Artificial Neural Network 19

Optimization of Machining Parameters Based on Principal Comp...

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5th International Conference on Mechatronics and Robotics Engineering (ICMRE)

作者： Yuan, Lei Zeng, Shasha Hainan Univ Mech & Elect Engn Coll Haikou 570228 Hainan Peoples R China Wuhan Univ Sch Power & Mech Engn Hubei Key Lab Waterjet Theory & New Technol Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9781450360951

In this paper, a method to optimize the cutting parameters based on the principal component analysis (PCA) and artificial neural network (ANN) is proposed. Experiments have been designed with four input machining process parameters at four different levels. The Taguchi Method is used to conduct the experimental design. The PCA is used to construct the model for characterizing the parameter's impact, with the Grey relational analysis (GRA) applied to search the near optimal combination of the parameters for multiple response characteristics. Furthermore, the artificial neural network (ANN) model is used for prediction of responses as reference. The ranking order for each response by prediction of ANN model is the same as the machining experiments do, which verify the feasibility of the proposed method.

关键词： machining parameter optimization Principal component analysis Artificial neural network Surface topography

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Multi-objective optimization of machining parameters in multi-pass turning operations for low-carbon manufacturing

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2017年第13期231卷 2372-2383页

作者： Lin, Wenwen Yu, Daoyuan Zhang, Chaoyong Zhang, Sanqiang Tian, Yuhui Liu, Shengqiang Luo, Min Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China Hubei Univ Technol Hubei Agr Machinery Engn Res & Design Inst Wuhan Hubei Peoples R China Shandong Hoteam Software Co Ltd Jinan Shandong Peoples R China China Natl Heavy Duty Truck Grp Co Ltd Jinan Shandong Peoples R China Hubei Univ Automot Technol Coll Elect & Informat Engn Shiyan Peoples R China

Most recent studies on machining parameter optimization in machining operations focused on reducing machining cost and energy consumption. However, environmental impacts caused by manufacturing activities were not involved in those studies, which can be quantified by equivalent carbon dioxide emissions. In this study, a direct method was proposed to quantify the carbon emissions generated during multi-pass turning operations. Moreover, machining parameter optimization models of multi-pass turning operations in dry and wet cut environments were established using an experimental design method. Three objectives were considered in both models: carbon emissions, operation time, and machining cost. Furthermore, a multi-objective teaching-learning-based optimization algorithm was used to deal with the models. The optimization results indicated that the use of cutting fluids could significantly reduce carbon emissions and machining cost and improve production efficiency in multi-pass turning operations.

关键词： machining parameter optimization multi-pass turning operation energy consumption carbon emissions teaching-learning-based optimization

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