Exergy efficiency optimization model of motorized spindle system for high-speed dry hobbing

High-speed dry hobbing (HSDH) has been regarded as an environmentally benign gear machining technique. Current research on energy efficiency of machine tool focuses on the energy efficiency for workpiece material removal but rarely involves the energy consumption required to control thermal stability. Due to the fact that thermal effect dominates the machining accuracy and accuracy consistency, especially in the dry machining process, the energy consumption for thermal stability control should be taken as useful energy consumption. In view of this, by taking the motorized spindle system (MSS) as the study objective, which is a core subsystem of machine tool enabling HSDH and characterizes intensive and inefficient energy consumption, an exergy-based method is proposed to evaluate the comprehensive energy efficiency of MSS. Furthermore, an exergy efficiency optimization model is proposed to maximize total exergy efficiency and minimize average temperature of MSS. A solution method integrating Pareto Dominant-based Multi-objective Simulated Annealing (PDMOSA) and Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to search the best solution of the optimization model. A case study is introduced to validate the proposed model and a final optimal solution is obtained at the total exergy efficiency of 53.5% with balanced temperature of 35.4 °C. The cooling water in MSS is identified to be a dominant factor that affects total exergy destruction. The presented model can give a reference to select appropriate process parameters of MSS for green and precision machining.

[1]  Xiao Yang,et al.  A 3D chip geometry driven predictive method for heat-loading performance of hob tooth in high-speed dry hobbing , 2017 .

[2]  Christian Brecher,et al.  Thermal issues in machine tools , 2012 .

[3]  Yi-Kuei Lin,et al.  Multi-objective optimization for stochastic computer networks using NSGA-II and TOPSIS , 2012, Eur. J. Oper. Res..

[4]  Mohammad Passandideh-Fard,et al.  Energy and exergy analysis of nanofluid based photovoltaic thermal system integrated with phase change material , 2018 .

[5]  Hoseyn Sayyaadi,et al.  Efficiency enhancement of a gas turbine cycle using an optimized tubular recuperative heat exchanger , 2012 .

[6]  Chiuh-Cheng Chyu,et al.  Optimizing fuzzy makespan and tardiness for unrelated parallel machine scheduling with archived metaheuristics , 2011 .

[7]  S. C. Kaushik,et al.  Energy and exergy analysis of thermoelectric heat pump system , 2015 .

[8]  Yuebin Guo,et al.  Energy consumption in machining: Classification, prediction, and reduction strategy , 2017 .

[9]  Y. Çengel,et al.  Thermodynamics : An Engineering Approach , 1989 .

[10]  Lihui Wang,et al.  Dynamic feature based adaptive process planning for energy-efficient NC machining , 2017 .

[11]  Jay F. Tu,et al.  A power flow model for high speed motorized spindles : Heat generation characterization , 2001 .

[12]  Rudolph F. Laubscher,et al.  Recent developments in sustainable manufacturing of gears: a review , 2016 .

[13]  Chinedum E. Okwudire,et al.  Design and control of a novel hybrid feed drive for high performance and energy efficient machining , 2013 .

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

[15]  Sami Kara,et al.  Unit process energy consumption models for material removal processes , 2011 .

[16]  Wei Cai,et al.  Energy efficiency evaluation for machining systems through virtual part , 2018, Energy.

[17]  Li Li,et al.  An integrated approach of process planning and cutting parameter optimization for energy-aware CNC machining , 2017 .

[18]  Christian Brecher,et al.  Machine tool spindle units , 2010 .

[19]  Yongming Han,et al.  Review: Multi-objective optimization methods and application in energy saving , 2017 .

[20]  S. Fellaou,et al.  Analyzing thermodynamic improvement potential of a selected cement manufacturing process: Advanced exergy analysis , 2018, Energy.

[21]  Paolo Albertelli,et al.  Energy saving opportunities in direct drive machine tool spindles , 2017 .

[22]  Paul Xirouchakis,et al.  Evaluating the use phase energy requirements of a machine tool system , 2011 .

[23]  Balram Suman,et al.  Study of simulated annealing based algorithms for multiobjective optimization of a constrained problem , 2004, Comput. Chem. Eng..

[24]  Brian Elmegaard,et al.  Energy, exergy and advanced exergy analysis of a milk processing factory , 2018, Energy.

[25]  Peng Chen,et al.  Thermal error compensation of dry hobbing machine tool considering workpiece thermal deformation , 2016 .

[26]  Jing Li,et al.  Energy consumption model and energy efficiency of machine tools: a comprehensive literature review , 2016 .

[27]  Madjid Tavana,et al.  Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS , 2016, Expert Syst. Appl..

[28]  Fabio Rinaldi,et al.  Thermal–economic–environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling , 2014 .

[29]  Ulvi Seker,et al.  Investigation of the effect of rake angle on main cutting force , 2004 .

[30]  Paul Mativenga,et al.  Specific energy based evaluation of machining efficiency , 2016 .

[31]  Mohsen Ghazikhani,et al.  Temporal exergy analysis of adsorption cooling system by developing non-flow exergy function , 2018, Applied Thermal Engineering.

[32]  Wei Cai,et al.  A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking , 2017 .

[33]  Xiao Yang,et al.  Multi-variable driving thermal energy control model of dry hobbing machine tool , 2017 .

[34]  Qi Zhang,et al.  Energy-exergy analysis and energy efficiency improvement of coal-fired industrial boilers based on thermal test data , 2018, Applied Thermal Engineering.

[35]  Tojiro Aoyama,et al.  Fluid elements in machine tools , 2017 .

[36]  Oguz Salim Sogut,et al.  Conventional and advanced exergy analyses of a marine steam power plant , 2018, Energy.

[37]  Benjie Li,et al.  Thermal energy balance control model of motorized spindle system enabling high-speed dry hobbing process , 2018, Journal of Manufacturing Processes.

[38]  Xiao Yang,et al.  Exergy analysis and multi-objective optimization of air cooling system for dry machining , 2017 .

[39]  Xiao Yang,et al.  A thermal energy balance optimization model of cutting space enabling environmentally benign dry hobbing , 2018 .

[40]  Li Li,et al.  Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials , 2018 .

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

[42]  R. Sahin,et al.  A simulated annealing algorithm to find approximate Pareto optimal solutions for the multi-objective facility layout problem , 2009 .

[43]  Yang Li,et al.  A review on spindle thermal error compensation in machine tools , 2015 .

[44]  Andrew Honegger,et al.  Analysis of thermal errors in a high-speed micro-milling spindle , 2010 .

[45]  Jiin-Yuh Jang,et al.  3-D numerical and experimental analysis of a built-in motorized high-speed spindle with helical water cooling channel , 2008 .

[46]  Peter Krajnik,et al.  Transitioning to sustainable production – Part I: application on machining technologies , 2010 .

[47]  Paul Mativenga,et al.  Energy centric selection of machining conditions for minimum cost , 2018 .

[48]  Soumitra Paul,et al.  Modelling of specific energy requirement during high-efficiency deep grinding , 2008 .

[49]  Mahrokh G. Shayesteh,et al.  Efficient contrast enhancement of images using hybrid ant colony optimisation, genetic algorithm, and simulated annealing , 2013, Digit. Signal Process..

[50]  Jürgen Weber,et al.  Energy, Power and Heat Flow of the Cooling and Fluid Systems in a Cutting Machine Tool☆ , 2016 .

[51]  Sami Kara,et al.  Towards Energy and Resource Efficient Manufacturing: A Processes and Systems Approach , 2012 .

[52]  Jay F. Tu,et al.  A thermal model for high speed motorized spindles , 1999 .

[53]  Xuesong Mei,et al.  Simulation and experimental study on the thermally induced deformations of high-speed spindle system , 2015 .

[54]  Z. Yue A method for group decision-making based on determining weights of decision makers using TOPSIS , 2011 .