Energy modeling and visualization analysis method of drilling processes in the manufacturing industry

Abstract Energy modeling and visualization of machining have been recognized as effective and powerful ways to explore energy-saving potential and to improve energy efficiency. However, energy modeling and visualization of the drilling process have not been investigated adequately. To address this challenge, sub-power models-based energy modeling and multi-angle energy visualization analysis methods of drilling process were proposed in this study. More specifically, three tasks were carried out: (1) detailed sub-power models of drilling were established; (2) sub-power models-based energy modeling method of drilling was proposed; (3) based on the detailed sub-power models and energy data, multi-angle energy visualization analysis was conducted. Application of the proposed drilling energy model in common drilling processes indicated that its average prediction accuracy of the proposed drilling energy model was 96.2%. The results also showed that 7417.8 J energy saving and 12.6% energy efficiency improvement were achieved with the visualization analysis. The proposed method contributed to energy-saving activities for the drilling process, including providing high accuracy energy model, analyzing energy saving potential and improving energy efficiency. We believe that the outcomes of this research can help engineers and managers to better understand and manage the energy characteristics of drilling.

[1]  Ying Liu,et al.  Data-driven ecological performance evaluation for remanufacturing process , 2019, Energy Conversion and Management.

[2]  William O'Brien,et al.  Data visualization and analysis of energy flow on a multi-zone building scale , 2017 .

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

[4]  Hua Zhang,et al.  An integrated MCDM approach considering demands-matching for reverse logistics , 2019, Journal of Cleaner Production.

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

[6]  Ying Liu,et al.  Therblig-embedded value stream mapping method for lean energy machining , 2017 .

[7]  Shun Jia,et al.  Energy modeling method of machine-operator system for sustainable machining , 2018, Energy Conversion and Management.

[8]  Chen-Yang Cheng.  A novel approach of information visualization for machine operation states in industrial 4.0 , 2018, Comput. Ind. Eng..

[9]  Ran Yan,et al.  A methodology to assess China's building energy savings at the national level: An IPAT-LMDI model approach , 2017 .

[10]  F. Rossi,et al.  Mechanical analysis of local cutting forces and transient state when drilling of heat-resistant austenitic stainless steel , 2019, The International Journal of Advanced Manufacturing Technology.

[11]  Mohsen A. Jafari,et al.  Energy-Performance as a driver for optimal production planning , 2016 .

[12]  Hakkı Özgür Ünver,et al.  Modelling and optimization of energy consumption for feature based milling , 2016 .

[13]  Carmita Camposeco-Negrete,et al.  Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum , 2015 .

[14]  Aydin Nassehi,et al.  A mechanistic model of energy consumption in milling , 2018, Int. J. Prod. Res..

[15]  Gui-Bing Hong,et al.  Energy efficiency benchmarking of energy-intensive industries in Taiwan , 2014 .

[16]  Thorsten Wuest,et al.  Real-time energy visualization system for light commercial businesses , 2019 .

[17]  Jian-ming Zheng,et al.  Modeling and experimental investigation of drilling force for low-frequency axial vibration-assisted BTA deep hole drilling , 2020, The International Journal of Advanced Manufacturing Technology.

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

[19]  Arturo Roman Messina,et al.  A semi-distributed energy-based framework for the analysis and visualization of power system disturbances , 2017 .

[20]  Kuan-Ming Li,et al.  Low-cost camera based laser power monitoring and stabilizing for micro-hole drilling , 2017 .

[21]  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 .

[22]  Wei Cai,et al.  Energy benchmarking rules in machining systems , 2018 .

[23]  Eraldo Jannone da Silva,et al.  Productive and environmental performance indicators analysis by a combined LCA hybrid model and real-time manufacturing process monitoring: A grinding unit process application , 2017 .

[24]  Sung-Hoon Ahn,et al.  A comparison of energy consumption in bulk forming, subtractive, and additive processes: Review and case study , 2014 .

[25]  Shun Jia,et al.  Energy modeling for variable material removal rate machining process: an end face turning case , 2016 .

[26]  K. Wegener,et al.  Influence of energy fraction in EDM drilling of Inconel 718 by statistical analysis and finite element crater-modelling , 2019, Journal of Manufacturing Processes.

[27]  Wei Yan,et al.  Energy consumption component analysis mathematical model of grinder energy unit , 2018, Int. J. Comput. Sci. Math..

[28]  Laine Mears,et al.  Energy, economy, and environment analysis and optimization on manufacturing plant energy supply system , 2016 .

[29]  Yan Wang,et al.  A modeling method for hybrid energy behaviors in flexible machining systems , 2015 .

[30]  Mozammel Mia,et al.  Multi-Response Optimization of Electrical Discharge Drilling Process of SS304 for Energy Efficiency, Product Quality, and Productivity , 2020, Materials.

[31]  Konrad Wegener,et al.  The Total Energy Efficiency Index for machine tools , 2016 .

[32]  L.G.B. Ruiz,et al.  A case study on understanding energy consumption through prediction and visualization (VIMOEN) , 2020, Journal of Building Engineering.

[33]  Ernst Worrell,et al.  Empirical investigation of energy efficiency barriers in Italian manufacturing SMEs , 2013 .

[34]  Konrad Wegener,et al.  Methods for evaluation of energy efficiency of machine tools , 2015 .

[35]  Shun Jia,et al.  Establishing prediction models for feeding power and material drilling power to support sustainable machining , 2018, The International Journal of Advanced Manufacturing Technology.

[36]  Shun Jia,et al.  Task-Oriented Energy Benchmark of Machining Systems for Energy-Efficient Production , 2020, International Journal of Precision Engineering and Manufacturing-Green Technology.

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

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

[39]  Dinghua Zhang,et al.  Energy Consumption Model for Drilling Processes Based on Cutting Force , 2019, Applied Sciences.

[40]  Shun Jia,et al.  Therblig-based energy demand modeling methodology of machining process to support intelligent manufacturing , 2014, J. Intell. Manuf..

[41]  Alessandro Franco,et al.  Analysis of energy consumption in micro-drilling processes , 2016 .

[42]  Q. Y. Wang,et al.  Methodologies for evaluating sawability of ornamental granite and relation modeling combining sawability with environmental impacts: An application in a stone industrial park of China , 2020 .

[43]  Luigi Pelliccia,et al.  Energy Visualization Techniques for Machine Tools in Virtual Reality , 2016 .

[44]  W. Hung,et al.  Electrothermal energy distribution model for EDM drilling of HSLA steels , 2017 .

[45]  Saqib Hameed,et al.  Electroplastic cutting influence on power consumption during drilling process , 2016 .

[46]  Wei Cai,et al.  Establishment of an Improved Material-Drilling Power Model to Support Energy Management of Drilling Processes , 2018, Energies.

[47]  M. Ariffin,et al.  Measurement and analysis of thrust force and torque in friction drilling of difficult-to-machine materials , 2019, The International Journal of Advanced Manufacturing Technology.

[48]  Philip Blaser,et al.  A methodology for online visualization of the energy flow in a machine tool , 2017 .