Ontology-based cutting tool configuration considering carbon emissions

In order to improve the precision and efficiency of cutting tool configuration and reduce carbon emissions during manufacturing process, an ontology-based cutting tool configuration process considering carbon emissions is put forward in the paper. Firstly, the architecture of ontology-based cutting tool configuration is established and key functional modules are described. Secondly, ontology is applied to describe the complex knowledge of cutting tool configuration and the Semantic Web Rule Language (SWRL) is used to build inference rules to reason feasible cutting tool configuration schemes according to machining requirements. Thirdly, taking carbon emissions as the objective, an evaluation method based on the c-PBOM-T (carbon emissions-Process Bill of Material for cutting Tools) table is studied to decide an optimal cutting tool configuration scheme from the feasible ones in the previous step for part machining. Finally, the proposed method is applied to a vortex shell workpiece to demonstrate its feasibility. The results show that the proposed method can improve the cutting tool configuration and reduce carbon emissions effectively for the machining processes. The presented method provides a valuable insight into the intelligent cutting tool configuration to support low-carbon manufacturing.

[1]  Paul Mativenga,et al.  Calculation of optimum cutting parameters based on minimum energy footprint , 2011 .

[2]  Jürgen Gausemeier,et al.  Ontology-based Determination of Alternative CNC Machines for a Flexible Resource Allocation , 2015 .

[3]  Qi Lu,et al.  Feature-based carbon emission quantitation strategy for the part machining process , 2017, Int. J. Comput. Integr. Manuf..

[4]  Pengyu Li,et al.  A quantitative approach to analyze carbon emissions of CNC-based machining systems , 2015, J. Intell. Manuf..

[5]  Rung Ching Chen,et al.  A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection , 2012, Expert Syst. Appl..

[6]  Björn Johansson,et al.  Environmental aspects in manufacturing system modelling and simulation—State of the art and research perspectives , 2013 .

[7]  Fei Liu,et al.  A decision-making framework model of cutting fluid selection for green manufacturing and a case study , 2002 .

[8]  V. K. Kher,et al.  An Expert Carbide Cutting Tools Selection System for CNC Lathe Machine , 2012 .

[9]  Alan C. Lin,et al.  Automated selection of cutting tools based on solid models , 1997 .

[10]  Roque Alfredo Osornio-Rios,et al.  Directional morphological approaches from image processing applied to automatic tool selection in computer numerical control milling machine , 2013 .

[11]  Denis Cavallucci,et al.  Ontology-based Knowledge Modeling for Using Physical Effects , 2015 .

[12]  Paul Xirouchakis,et al.  A multi-criteria decision method for sustainability assessment of the use phase of machine tool systems , 2011 .

[13]  B. Bhattacharyya,et al.  Development of an expert system for turning and rotating tool selection in a dynamic environment , 2001 .

[14]  Wim Dewulf,et al.  Unit process impact assessment for discrete part manufacturing: A state of the art , 2010 .

[15]  Gianni Campatelli,et al.  Workpiece orientation and tooling selection to reduce the environmental impact of milling operations , 2014 .

[16]  Kun-Mo Lee,et al.  Development of a low-carbon product design system based on embedded GHG emissions , 2010 .

[17]  Jun Zhao,et al.  Optimal selection of cutting tool materials based on multi-criteria decision-making methods in machining Al-Si piston alloy , 2016 .

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

[19]  Chaoyong Zhang,et al.  Multi-objective optimization of machining parameters in multi-pass turning operations for low-carbon manufacturing , 2017 .

[20]  Danchen Zhou,et al.  A granulation analysis method for cutting tool material selection using granular computing , 2016 .

[21]  Nebil Buyurgan,et al.  Tool allocation in flexible manufacturing systems with tool alternatives , 2004 .

[22]  Kunaparaju Saranya,et al.  Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations , 2018 .

[23]  Hirohisa Narita,et al.  Environmental Burden Analysis for Machining Operation Using LCA Method , 2008 .

[24]  Tan Xian A Decision-Making Framework Model of Tool Selection for Green Manufacturing and Its Applications , 2003 .

[25]  Congbo Li,et al.  Multi-objective parameter optimization of CNC machining for low carbon manufacturing , 2015 .

[26]  Engelbert Westkämper,et al.  Life Cycle Management of Cutting Tools: Comprehensive Acquisition and Aggregation of Tool Life Data , 2017 .

[27]  A. Siadat,et al.  MASON: A Proposal For An Ontology Of Manufacturing Domain , 2006, IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06).

[28]  Afrim Gjelaj INTELLIGENT OPTIMAL TOOL SELECTIONS FOR CNC PROGRAMMING OF MACHINE TOOLS , 2013 .

[29]  Jae Kwan Kim,et al.  Ontology-based modeling of process selection knowledge for machining feature , 2013 .

[30]  Xuefeng Wu,et al.  Intelligent Service Platform of Manufacturing Process and Tool Based on Data Warehouse , 2016 .

[31]  Hong Zhang,et al.  A knowledge representation for unit manufacturing processes , 2014 .

[32]  Ali Oral,et al.  Automated cutting tool selection and cutting tool sequence optimisation for rotational parts , 2004 .

[33]  Sathyan Subbiah,et al.  Multi-criteria decision making techniques for compliant polishing tool selection , 2015, The International Journal of Advanced Manufacturing Technology.

[34]  Zhi-Jie Liu,et al.  Multi-objective optimization of the operating conditions in a cutting process based on low carbon emission costs , 2016 .