Digital twin-based process reuse and evaluation approach for smart process planning

With the advances in new-generation information technologies, smart process planning is becoming the focus for smart process planning with less time and lower cost. Big data-based reusing and evaluating the multi-dimensional process knowledge is widely accepted as an effective strategy for improving competitiveness of enterprises. However, there was little research on how to reuse and evaluate process knowledge with dynamical changing machining status. In this paper, we propose a novel digital twin-based approach for reusing and evaluating process knowledge. First, the digital twin-based process knowledge model which contains the geometric information and real-time process equipment status is introduced to represent the purpose and requirement of machining planning. Second, the process big data is constructed based on the three-layer and its association rules for accumulating process knowledge. Moreover, the similarity calculation algorithm of the scene model is proposed to filter the unmatched process knowledge. For accurately reusing the process knowledge, the process reusability evaluation approach of the candidate knowledge set is presented based on the real-time machining status and the calculated confidence. Finally, the diesel engine parts are applied in the developed prototype module to verify the effectiveness of the proposed method. The proposed method can promote the development and application of the smart process planning.

[1]  Kun Chen,et al.  Integration of process planning and scheduling using a hybrid GA/PSO algorithm , 2014, The International Journal of Advanced Manufacturing Technology.

[2]  Laurent Tapie,et al.  A knowledge base model for complex forging die machining , 2011, Comput. Ind. Eng..

[3]  Gang Chen,et al.  Paradigm shift: unified and associative feature-based concurrent and collaborative engineering , 2008, J. Intell. Manuf..

[4]  Limin Tang,et al.  Drive geometry construction method of machining features for aircraft structural part numerical control machining , 2014 .

[5]  Hyung Jun Ahn,et al.  Capturing and reusing knowledge in engineering change management: A case of automobile development , 2006, Inf. Syst. Frontiers.

[6]  Keith Case,et al.  Knowledge reuse in manufacturability analysis , 2008 .

[7]  Jerry Y. H. Fuh,et al.  Toward Effective Mechanical Design Reuse: CAD Model Retrieval Based on General and Partial Shapes , 2009 .

[8]  Xiaojun Liu,et al.  An approach to mapping machining feature to manufacturing feature volume based on geometric reasoning for process planning , 2017 .

[9]  Dusan Sormaz,et al.  Recognition of interacting volumetric features using 2D hints , 2010 .

[10]  Ligang Cai,et al.  An effective hybrid graph and genetic algorithm approach to process planning optimization for prismatic parts , 2012 .

[11]  Xin Chen,et al.  A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line , 2017, IEEE Access.

[12]  Aydin Nassehi,et al.  Process comprehension for shopfloor manufacturing knowledge reuse , 2013 .

[13]  Martín G. Marchetta,et al.  An artificial intelligence planning approach to manufacturing feature recognition , 2010, Comput. Aided Des..

[14]  Rui Huang,et al.  A flexible and effective NC machining process reuse approach for similar subparts , 2015, Comput. Aided Des..

[15]  J. Jerald,et al.  An intelligent process planning system for micro turn-mill parts , 2014 .

[16]  Zhuoning Chen,et al.  Automatic generation of in-process models based on feature working step and feature cutter volume , 2014 .

[17]  M. S. Parvez,et al.  Integrated manufacturing features and Design-for-manufacture guidelines for reducing product cost under CAD/CAM environment , 2013, Comput. Ind. Eng..

[18]  Fei Tao,et al.  Digital twin-driven product design, manufacturing and service with big data , 2017, The International Journal of Advanced Manufacturing Technology.

[19]  Tomohisa Tanaka,et al.  Graph based automatic process planning system for multi-tasking machine , 2015 .

[20]  Xiaojun Liu,et al.  A systematic method for the automatic update and propagation of the machining process models in the process modification , 2016 .

[21]  J. Jerald,et al.  Feature-based modelling and process parameters selection in a CAPP system for prismatic micro parts , 2015, Int. J. Comput. Integr. Manuf..

[22]  Michael M. Marefat,et al.  Similarity-Based Retrieval of CAD Solid Models for Automated Reuse of Machining Process Plans , 2007, 2007 IEEE International Conference on Automation Science and Engineering.

[23]  Fei Tao,et al.  Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison , 2018, IEEE Access.

[24]  William C. Regli,et al.  Content-Based Classification of CAD Models with Supervised Learning , 2005 .

[25]  Yu Zheng,et al.  Decomposition of interacting machining features based on the reasoning on the design features , 2012 .

[26]  Heming Zhang,et al.  A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model , 2017, Adv. Eng. Informatics.

[27]  Wen-Ren Jong,et al.  Automatic process planning of mold components with integration of feature recognition and group technology , 2015 .

[28]  Liu Xiaojun,et al.  A flexible process information reuse method for similar machining feature , 2017 .

[29]  Xun Xu,et al.  Dealing with feature interactions for prismatic parts in STEP-NC , 2009, J. Intell. Manuf..

[30]  Rikard Söderberg,et al.  Toward a Digital Twin for real-time geometry assurance in individualized production , 2017 .

[31]  Abderrahmane Bensmaine,et al.  A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems , 2014 .

[32]  Behrooz Arezoo,et al.  A hybrid hint-based and graph-based framework for recognition of interacting milling features , 2007, Comput. Ind..

[33]  C. W. Dankwort,et al.  Engineers'CAx education - it's not only CAD , 2004, Comput. Aided Des..

[34]  Wei Liu,et al.  A geometry search approach in case-based tool reuse for mould manufacturing , 2015 .

[35]  W. L. Chen,et al.  A new process knowledge representation approach using parameter flow chart , 2011, Comput. Ind..

[36]  Kwang-Kyu Seo,et al.  A minimax p-robust optimization approach for planning under uncertainty , 2015 .

[37]  Jianhua Liu,et al.  Digital twin-based smart production management and control framework for the complex product assembly shop-floor , 2018, The International Journal of Advanced Manufacturing Technology.

[38]  Weiming Shen,et al.  Integrated manufacturing process planning and control based on intelligent agents and multi-dimension features , 2014 .