A modeling method for hybrid energy behaviors in flexible machining systems

Increasingly environmental and economic pressures have led to great concerns regarding the energy consumption of machining systems. Understanding energy behaviors of flexible machining systems is a prerequisite for improving energy efficiency of these systems. This paper proposes a modeling method to predict energy behaviors in flexible machining systems. The hybrid energy behaviors not only depend on the technical specification related of machine tools and workpieces, but are significantly affected by individual production scenarios. In the method, hybrid energy behaviors are decomposed into Structure-related energy behaviors, State-related energy behaviors, Process-related energy behaviors and Assignment-related energy behaviors. The modeling method for the hybrid energy behaviors is proposed based on Colored Timed Object-oriented Petri Net (CTOPN). The former two types of energy behaviors are modeled by constructing the structure of CTOPN, whist the latter two types of behaviors are simulated by applying colored tokens and associated attributes. Machining on two workpieces in the experimental workshop were undertaken to verify the proposed modeling method. The results showed that the method can provide multi-perspective transparency on energy consumption related to machine tools, workpieces as well as production management, and is particularly suitable for flexible manufacturing system when frequent changes in machining systems are often encountered.

[1]  C. Park,et al.  Energy consumption reduction technology in manufacturing — A selective review of policies, standards, and research , 2009 .

[2]  Eberhard Abele,et al.  Simulation of the Energy Consumption of Machine Tools for a Specific Production Task , 2012 .

[3]  Xiuli Meng,et al.  Modeling of reconfigurable manufacturing systems based on colored timed object-oriented Petri nets , 2010 .

[4]  Tao Wu,et al.  An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops , 2015 .

[5]  Seyed Farid Ghaderi,et al.  Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms , 2012 .

[6]  John W. Sutherland,et al.  A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction , 2011 .

[7]  Timothy G. Gutowski,et al.  An Environmental Analysis of Machining , 2004 .

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

[9]  Shahin Rahimifard,et al.  Minimising Embodied Product Energy to support energy efficient manufacturing , 2010 .

[10]  Bopaya Bidanda,et al.  Assessing the environmental footprint of manufactured products: A survey of current literature , 2013 .

[11]  Reimund Neugebauer,et al.  Structure principles of energy efficient machine tools , 2011 .

[12]  Guohui Zhang,et al.  Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency , 2016 .

[13]  Patrik Thollander,et al.  Barriers to and driving forces for energy efficiency in the non-energy intensive manufacturing industry in Sweden , 2006 .

[14]  P. Balachandra,et al.  Barriers to energy efficiency in small industry clusters: Multi-criteria-based prioritization using the analytic hierarchy process , 2006 .

[15]  David Dornfeld,et al.  Cost and Energy Consumption Optimization of Product Manufacture in a Flexible Manufacturing System , 2012 .

[16]  Fouad Al-Mansour,et al.  Energy efficiency trends and policy in Slovenia , 2011 .

[17]  Li-Chih Wang,et al.  Modeling with colored timed object-oriented Petri nets for automated manufacturing systems , 1998 .

[18]  Huai Gao,et al.  A modeling method of task-oriented energy consumption for machining manufacturing system , 2012 .

[19]  Tao Wu,et al.  Analysis and estimation of energy consumption for numerical control machining , 2012 .

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

[21]  Andrea Trianni,et al.  Quick-E-scan: A methodology for the energy scan of SMEs , 2010 .

[22]  Thorsten Pawletta,et al.  A discrete-event simulation approach to predict power consumption in machining processes , 2011, Prod. Eng..

[23]  Li-Chih Wang,et al.  Object-oriented Petri nets for modelling and analysis of automated manufacturing systems , 1996 .

[24]  Bin Lin,et al.  Research on the analytical thermal model in surface grinding by cup wheel , 2013 .

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

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

[27]  Christoph Herrmann,et al.  Process chain simulation to foster energy efficiency in manufacturing , 2009 .

[28]  Peter Ball,et al.  Steps towards sustainable manufacturing through modelling material, energy and waste flows , 2012 .

[29]  Robert Schmitt,et al.  Modelling Machine Tools for Self-Optimisation of Energy Consumption , 2011 .

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

[31]  Eberhard Abele,et al.  Analyzing Energy Consumption of Machine Tool Spindle Units and Identification of Potential for Improvements of Efficiency , 2011 .

[32]  M. Gheorghe,et al.  Models of machine tool efficiency and specific consumed energy , 2003 .

[33]  Uwe Heisel,et al.  Simulation and Prediction of Process-Oriented Energy Consumption of Machine Tools , 2012 .