An energy consumption prediction approach of die casting machines driven by product parameters

Abstract

Die casting machines, which are the core equipment of the machinery manufacturing industry, consume great amounts of energy. The energy consumption prediction of die casting machines can support energy consumption quota, process parameter energy-saving optimization, energy-saving design, and energy efficiency evaluation; thus, it is of great significance for Industry 4.0 and green manufacturing. Nevertheless, due to the uncertainty and complexity of the energy consumption in die casting machines, there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration. To fill this gap, this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters. Firstly, the system boundary of energy consumption prediction is defined, and subsequently, based on the energy consumption characteristics analysis, a theoretical energy consumption model is established. Consequently, a systematic energy consumption prediction approach for die casting machines, involving product, die, equipment, and process parameters, is proposed. Finally, the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products. The results show that the prediction accuracy of production time and energy consumption reached 91.64% and 85.55%, respectively. Overall, the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.

Graphical abstract

Keywords

die casting machine / energy consumption prediction / product parameters

References

[1] Fu M W, Yong M S. Simulation-enabled casting product defect prediction in die casting process. International Journal of Production Research, 2009, 47( 18): 5203– 5216

[2] Accessed at The North American Die Casting Association (NADCA) website, 2012

[3] Brevick J R, Mount-Campbell A F, Mount-Campbell C A. Modeling alloy and energy utilization in high volume die casting. Clean Technologies and Environmental Policy, 2014, 16( 1): 201– 209

[4] Brevick J, Mount-Campbell C, Mobley C. Energy Consumption of Die Casting Operations. Scientific and Technical Information Technical Reports DE-FC07-00ID13843, 2004

[5] Schwam D. Energy Saving Melting and Revert Reduction Technology: Melting Efficiency in Die Casting Operations. Office of Scientific and Technical Information Technical Reports DE-FC36-04GO14230, 2012

[6] Liu W, Tang R, Peng T. An IoT-enabled approach for energy monitoring and analysis of die casting machines. In: Proceedings of the 25th CIRP Life Cycle Engineering (LCE) Conference. Copenhagen: CIRP, 2018, 69 : 656– 661

[7] Chen E, Cao H, He Q. An IoT based framework for energy monitoring and analysis of die casting workshop. In: Proceedings of the 26th CIRP Life Cycle Engineering (LCE) Conference. West Lafayette: CIRP, 2019, 80 : 693– 698

[8] Watkins M F, Mani M, Lyons K W. Sustainability characterization for die casting process. In: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE). Portland: ASME, 2013, 2A : 1– 10

[9] Bettoni L, Zanoni S. Energy implications of production planning decisions. In: Frick J, Laugen B T, eds. Advances in Production Management Systems. Value Networks: Innovation, Technologies, and Management. APMS 2011. IFIP Advances in Information and Communication Technology. Berlin: Springer, 2012, 384 : 9– 17

[10] He K, Tang R, Jin M. Energy modeling and efficiency analysis of aluminum die-casting processes. Energy Efficiency, 2019, 12( 5): 1167– 1182

[11] Gutowski T, Dahmus J, Thiriez A. Electrical energy requirements for manufacturing processes. In: Proceedings of the 13th CIRP Life Cycle Engineering (LCE) Conference. Leuven: CIRP, 2006, 623– 627

[12] Li B J, Cao H J, Hon B. Exergy-based energy efficiency evaluation model for machine tools considering thermal stability. International Journal of Precision Engineering and Manufacturing-Green Technology, 2021, 8( 2): 423– 434

[13] Imani Asrai R, Newman S T, Nassehi A. A mechanistic model of energy consumption in milling. International Journal of Production Research, 2018, 56(1‒2): 642– 659

[14] Ribeiro I, Peas P, Henriques E. Assessment of energy consumption in injection moulding process. In: Proceedings of the 19th CIRP Conference on Life Cycle Engineering. Berkeley: CIRP, 2012, 263– 268

[15] Pawanr S, Garg G K, Routroy S. Development of a transient energy prediction model for machine tools. Procedia CIRP, 2021, 98 : 678– 683

[16] Deng Z, Zhang H, Fu Y. Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption. Journal of Cleaner Production, 2017, 166 : 1407– 1414

[17] Sealy M P, Liu Z Y, Zhang D. Energy consumption and modeling in precision hard milling. Journal of Cleaner Production, 2016, 135 : 1591– 1601

[18] Meekers I, Refalo P, Rochman A. Analysis of process parameters affecting energy consumption in plastic injection moulding. In: Proceedings of the 25th CIRP Life Cycle Engineering (LCE) Conference. Copenhagen: CIRP, 2018, 69 : 342– 347

[19] Vishnu V S, Varghese K G, Gurumoorthy B. Energy prediction in process planning of five-axis machining by data-driven modelling. Procedia CIRP, 2020, 93 : 862– 867

[20] Pawar S S, Bera T C, Sangwan K S. Modelling of energy consumption for milling of circular geometry. Procedia CIRP, 2021, 98( 22): 470– 475

[21] He Y, Wu P, Li Y. A generic energy prediction model of machine tools using deep learning algorithms. Applied Energy, 2020, 275 : 115402–

[22] Pan J, Li C B, Tang Y. Energy consumption prediction of a CNC machining process with incomplete data. IEEE/CAA Journal of Automatica Sinica, 2021, 8( 5): 987– 1000

[23] Liu Z, Guo Y. A hybrid approach to integrate machine learning and process mechanics for the prediction of specific cutting energy. CIRP Annals-Manufacturing Technology, 2018, 67( 1): 57– 60

[24] Peng T, Xu X, Wang L. A novel energy demand modelling approach for CNC machining based on function blocks. Journal of Manufacturing Systems, 2014, 33( 1): 196– 208

[25] Kolar M, Vyroubal J, Smolik J. Analytical approach to establishment of predictive models of power consumption of machine tools’ auxiliary units. Journal of Cleaner Production, 2016, 137 : 361– 369

[26] Li W, Kara S. An empirical model for predicting energy consumption of manufacturing processes: a case of turning process. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2011, 225( 9): 1636– 1646

[27] Madan J, Mani M, Lee J H. Energy performance evaluation and improvement of unit-manufacturing processes: injection molding case study. Journal of Cleaner Production, 2015, 105 : 157– 170

[28] Andresen B. Die Casting Engineering: A Hydraulic, Thermal, and Mechanical Process. Boca Raton: CRC Press, 2004

[29] Boothroyd G. Product design for manufacture and assembly. Computer-Aided Design, 1994, 26( 7): 505– 520

[30] Ostwald P F. American Machinist Cost Estimator. New York: Mcgraw-Hill, 1986

[31] Blum C. Early cost estimation of die cast components. Thesis for the Master’s Degree. Kingston: University of Rhode Island, 1989

[32] Pokorny H H, Thukkaram P. Gating Die Casting Dies. River Grove: Society of Die Casting Engineers, 1981

[33] Herman E A. Die Casting Dies: Designing. River Grove: Society of Die Casting Engineers, 1985

[34] Johannaber F. Injection Molding Machines. 4th ed. Munich: Hanser Publications, 2007

[35] Ke J, Price L, McNeil M. Analysis and practices of energy benchmarking for industry from the perspective of systems engineering. Energy, 2013, 54 : 32– 44

[36] Yang F, Liu Y, Liu G. A process simulation based benchmarking approach for evaluating energy consumption of a chemical process system. Journal of Cleaner Production, 2016, 112 : 2730– 2743

[37] Zhan S, Liu Z, Chong A. Building categorization revisited: a clustering-based approach to using smart meter data for building energy benchmarking. Applied Energy, 2020, 269 : 114920–

[38] Veloso A, Souza R, Santos F. Energy benchmarking for office building towers in mild temperate climate. Energy and Buildings, 2020, 222 : 110059–

[39] Cai W, Li L, Jia S. Task-oriented energy benchmark of machining systems for energy-efficient production. International Journal Precision Engineering Manufacturing-Green Technology, 2020, 7( 1): 205– 218

[40] Chen D, Cao L, Si H. Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods. Advanced Engineering Informatics, 2020, 43 : 101029–

[41] Spiering T, Kohlitz S, Sundmaeker H. Energy efficiency benchmarking for injection moulding processes. Robotics and Computer-Integrated Manufacturing, 2015, 36( C): 45– 59

[42] Bunse K, Vodicka M, Schönsleben P. Integrating energy efficiency performance in production management–gap analysis between industrial needs and scientific literature. Journal of Cleaner Production, 2011, 19(6‒7): 667– 679

RIGHTS & PERMISSIONS

Higher Education Press 2021.