Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system

[1]  Günther Seliger,et al.  Online Fault-monitoring in Machine Tools Based on Energy Consumption Analysis and Non-invasive Data Acquisition for Improved Resource-efficiency☆ , 2016 .

[2]  Hoda A. ElMaraghy,et al.  Energy use analysis and local benchmarking of manufacturing lines , 2017 .

[3]  Ming Luo,et al.  Optimization of varying-parameter drilling for multi-hole parts using metaheuristic algorithm coupled with self-adaptive penalty method , 2020, Appl. Soft Comput..

[4]  Ji Zhao,et al.  Energy consumption considering tool wear and optimization of cutting parameters in micro milling process , 2020 .

[5]  Michele Germani,et al.  Resources value mapping: A method to assess the resource efficiency of manufacturing systems , 2019, Applied Energy.

[6]  David Flynn,et al.  Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review , 2020, Renewable and Sustainable Energy Reviews.

[7]  Y. T. Lee,et al.  Simulating a virtual machining model in an agent-based model for advanced analytics , 2017, Journal of Intelligent Manufacturing.

[8]  Carla Seatzu,et al.  Modelling and simulation of manufacturing systems with first-order hybrid Petri nets , 2001 .

[9]  Li Li,et al.  A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving , 2016 .

[10]  Ali Vatankhah Barenji,et al.  A digital twin-driven approach towards smart manufacturing: reduced energy consumption for a robotic cell , 2020, Int. J. Comput. Integr. Manuf..

[11]  Dimitris Kiritsis,et al.  Energy Management in Manufacturing: From Literature Review to a Conceptual Framework , 2017 .

[12]  Alexander Verl,et al.  A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing , 2009 .

[13]  Weidong Li,et al.  A Systematic Approach of Process Planning and Scheduling Optimization for Sustainable Machining , 2015, Sustainable Manufacturing and Remanufacturing Management.

[14]  Junkai Wang,et al.  Batch scheduling for minimal energy consumption and tardiness under uncertainties: A heat treatment application , 2016 .

[15]  Xun Xu,et al.  Energy-efficient machining systems: a critical review , 2014 .

[16]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[17]  Jörg Franke,et al.  Implementing Energy Management System to Increase Energy Efficiency in Manufacturing Companies , 2015 .

[18]  Duc Truong Pham,et al.  Digital Twin-Based Energy Modeling of Industrial Robots , 2018 .

[19]  Chaoyang Zhang,et al.  Digital twin-driven carbon emission prediction and low-carbon control of intelligent manufacturing job-shop , 2019, Procedia CIRP.

[20]  Cristian Mahulea,et al.  SimHPN: a MATLAB toolbox for simulation, analysis and design with hybrid Petri nets ⋆ , 2012 .

[21]  Marco Taisch,et al.  A Production-State Based Approach for Energy Flow Simulation in Manufacturing Systems , 2013, APMS.

[22]  Frank L. Lewis,et al.  Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems , 2013 .

[23]  Hongcheng Li,et al.  Modelling and simulation of energy consumption of ceramic production chains with mixed flows using hybrid Petri nets , 2018, Int. J. Prod. Res..

[24]  Shiqi Li,et al.  Event-Driven Online Machine State Decision for Energy-Efficient Manufacturing System Based on Digital Twin Using Max-Plus Algebra , 2019, Sustainability.

[25]  Meng Zhang,et al.  Equipment energy consumption management in digital twin shop-floor: A framework and potential applications , 2018, 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC).

[26]  S. Mallapaty,et al.  How China could be carbon neutral by mid-century , 2020, Nature.

[27]  Carlos Ocampo-Martinez,et al.  Dual mode control strategy for the energy efficiency of complex and flexible manufacturing systems , 2020 .

[28]  Sami Kara,et al.  A hierarchical framework for concurrent assessment of energy and water efficiency in manufacturing systems , 2016 .

[29]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Huiyue Dong,et al.  Review of digital twin about concepts, technologies, and industrial applications , 2020 .

[31]  Yuemin Ding,et al.  Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management , 2020, Applied Energy.

[32]  Bin He,et al.  Digital twin-based sustainable intelligent manufacturing: a review , 2020, Advances in Manufacturing.

[33]  Yan Wang,et al.  A generic energy prediction model of machine tools using deep learning algorithms , 2020 .

[34]  Jingxiang Lv,et al.  Energy-cyber-physical system enabled management for energy-intensive manufacturing industries , 2019, Journal of Cleaner Production.

[35]  Seung Ho Hong,et al.  An IoT-based energy-management platform for industrial facilities , 2016 .

[36]  Marco Taisch,et al.  Combined Energy, Material and Building Simulation for Green Factory Planning , 2013 .

[37]  Ashish Joglekar,et al.  Digital Twin for Energy Optimization in an SMT-PCB Assembly Line , 2018, 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS).

[38]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[39]  Huajun Cao,et al.  Energy value mapping: A novel lean method to integrate energy efficiency into production management , 2021 .

[40]  N. Arunachalam,et al.  A Digital Clone for Grinding Wheel ? An Information Sharing Platform for Sustainable Grinding Process , 2019 .

[41]  Günther Seliger,et al.  Methodology for planning and operating energy-efficient production systems , 2011 .

[42]  Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing , 2021, Journal of Cleaner Production.

[43]  Giuliano Bissacco,et al.  Real time power consumption monitoring for energy efficiency analysis in micro EDM milling , 2015 .

[44]  Xun Xu,et al.  Energy-efficient cyber-physical production network: Architecture and technologies , 2019, Comput. Ind. Eng..

[45]  Huajun Cao,et al.  An IoT based framework for energy monitoring and analysis of die casting workshop , 2019, Procedia CIRP.