Design optimization of lathe spindle system for optimum energy efficiency

Abstract Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modelled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved.

[1]  Shun Jia,et al.  An investigation into reducing the spindle acceleration energy consumption of machine tools , 2017 .

[2]  Shaohua Hu,et al.  Energy Survey of Machine Tools: Separating Power Information of the Main Transmission System During Machining Process , 2012 .

[3]  Li Li,et al.  Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time , 2019, Energy.

[4]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[5]  Vivek K. Patel,et al.  A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO) , 2016, Inf. Sci..

[6]  Michael P Sealy,et al.  Energy consumption characteristics in finish hard milling , 2018, Journal of Manufacturing Processes.

[7]  Jun Xie,et al.  A method for predicting the energy consumption of the main driving system of a machine tool in a machining process , 2015 .

[8]  T. Gutowski,et al.  Electrical Energy Requirements for Manufacturing Processes , 2006 .

[9]  Giacomo Bianchi,et al.  Electric load management in spindle run-up and run-down for multi-spindle machine tools via optimal power-torque trajectories and peak load synchronization , 2018 .

[10]  A. Diez-Ibarbia,et al.  Efficiency analysis of spur gears with a shifting profile , 2016 .

[11]  Chongyang Xie,et al.  Optimization design on dynamic load sharing performance for an in-wheel motor speed reducer based on genetic algorithm , 2018 .

[12]  Wei Hua,et al.  Systematic multi-level optimization design and dynamic control of less-rare-earth hybrid permanent magnet motor for all-climatic electric vehicles , 2019, Applied Energy.

[13]  Javad Jafari Fesharaki,et al.  Gear train optimization based on minimum volume/weight design , 2014 .

[14]  Ray Y. Zhong,et al.  An optimization model for energy-efficient machining for sustainable production , 2019, Journal of Cleaner Production.

[15]  Hamid Khakpour Nejadkhaki,et al.  A design methodology for selecting ratios for a variable ratio gearbox used in a wind turbine with active blades , 2018 .

[16]  Li Li,et al.  A comprehensive approach to parameters optimization of energy-aware CNC milling , 2019, J. Intell. Manuf..

[17]  Ying Tang,et al.  Meta-Reinforcement Learning of Machining Parameters for Energy-Efficient Process Control of Flexible Turning Operations , 2021, IEEE Transactions on Automation Science and Engineering.

[18]  Kamal Chakkarapani,et al.  Multiobjective design optimization and analysis of magnetic flux distribution for slotless permanent magnet brushless DC motor using evolutionary algorithms , 2019, Journal of Magnetism and Magnetic Materials.

[19]  Jun Xie,et al.  An integrated model for predicting the specific energy consumption of manufacturing processes , 2016 .

[20]  J. C. Balda,et al.  Optimum speed ratio of induction motor drives for electrical vehicle propulsion , 2001, APEC 2001. Sixteenth Annual IEEE Applied Power Electronics Conference and Exposition (Cat. No.01CH37181).

[21]  Pragasen Pillay,et al.  A Novel In Situ Efficiency Estimation Algorithm for Three-Phase IM Using GA, IEEE Method F1 Calculations, and Pretested Motor Data , 2015, IEEE Transactions on Energy Conversion.

[22]  Fatih Emre Boran,et al.  Optimization of module, shaft diameter and rolling bearing for spur gear through genetic algorithm , 2010, Expert Syst. Appl..

[23]  Li Li,et al.  A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning , 2019, Energy.

[24]  Paul Waide,et al.  Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems , 2011 .

[25]  Fei Liu,et al.  A new approach for calculating the input power of machine tool main transmission systems , 2017 .

[26]  D. Miler,et al.  Multi-objective spur gear pair optimization focused on volume and efficiency , 2018, Mechanism and Machine Theory.

[27]  E.J.A. Armarego,et al.  AN APPRAISAL OF EMPIRICAL MODELING AND PROPRIETARY SOFTWARE DATABASES FOR PERFORMANCE PREDICTION OF MACHINING OPERATIONS , 2000 .