Thinking process rules extraction for manufacturing process design

doi:10.1007/s40436-017-0205-6

Advances in Manufacturing ›› 2017, Vol. 5 ›› Issue (4): 321-334.doi: 10.1007/s40436-017-0205-6

• Articles • Previous Articles Next Articles

Thinking process rules extraction for manufacturing process design

Jing-Tao Zhou¹, Xiang-Qian Li², Ming-Wei Wang¹, Rui Niu¹, Qing Xu¹

1 Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education of China, Northwestern Polytechnical University, Xi'an 710072, People's Republic of China;
2 Department of Software and Information Service Research, Electronic Technology Information Research Institute, Ministry of Industry and Information Technology of the People's Republic of China, Beijing 100040, People's Republic of China

Received:2017-07-04 Revised:2017-11-13 Online:2017-12-25 Published:2017-12-25
Contact: Jing-Tao Zhou,E-mail:zhoujt@nwpu.edu.cn E-mail:zhoujt@nwpu.edu.cn
Supported by:
The work was supported by the National Key Technology R&D Program (Grant No.2015BAF17B01).

Abstract

Abstract:

To realize the reuse of process design knowledge and improve the efficiency and quality of process design, a method for extracting thinking process rules for process design is proposed. An instance representation model of the process planning reflecting the thinking process of technicians is established to achieve an effective representation of the process documents. The related process attributes are extracted from the model to form the related events. The manifold learning algorithm and clustering analysis are used to preprocess the process instance data. A rule extraction mechanism of process design is introduced, which is based on the related events after dimension reduction and clustering, and uses the association rule mining algorithm to realize the similar process information extraction in the same cluster. Through the vectorization description of the related events, the final process design rules are formed. Finally, an example is given to evaluate the method of process design rules extraction.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-017-0205-6/fulltext.html

Key words: Process design rules, Process design rules extraction, Process planning model, Related event, Manifold learning

Jing-Tao Zhou, Xiang-Qian Li, Ming-Wei Wang, Rui Niu, Qing Xu. Thinking process rules extraction for manufacturing process design[J]. Advances in Manufacturing, 2017, 5(4): 321-334.

TrendMD

References

1. Kusiak A (1989) Knowledge-based systems in manufacturing. Taylor & Francis, London
2. Aalst WVD (2016) Process mining:data science in action. Springer, Berlin
3. Dorst K (2011) The core of ‘design thinking’ and its application. Des Stud 32(6):521-532
4. Beynon M, Curry B, Morgan P (2010) Classification and rule induction using rough set theory. Exp Syst 17(3):136-148
5. Zhang J, Li T, Pan Y (2012) Parallel rough set based knowledge acquisition using MapReduce from big data. In:International workshop on big data, streams and heterogeneous source mining:algorithms, systems, programming models and applications. pp 20-27
6. Chao Y, Sun W (2015) Welding type oriented association rules acquisition based on rough sets. Comput Eng Appl 15:244-248
7. Augasta MG, Kathirvalavakumar T (2012) Reverse engineering the neural networks for rule extraction in classification problems. Neural Process Lett 35(2):131-150
8. Masood I, Hassan A (2013) Pattern recognition for bivariate process mean shifts using feature-based artificial neural network. Int J Adv Manuf Technol 66(9-12):1201-1218
9. Khorasani AM, Yazdi MRS (2015) Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. Int J Adv Manuf Technol 57(3):1-11
10. Armaghan N, Renaud J (2012) An application of multi-criteria decision aids models for case-based reasoning. Inf Sci 210(22):55-66
11. Olsson E, Funk P, Andersson A (2013) Case-based reasoning applied to geometric measurements for decision support in manufacturing. Int J Syst Assur Eng Manag 4(3):223-230
12. Zhang J, Wang F, Wang C (2016) Integrating case-based with rule-based reasoning in body-in-white fixture design. Int J Adv Manuf Technol 85(5-8):1807-1824
13. Prado RP, Garcia-Galan S, Exposito JEM et al (2010) Knowledge acquisition in fuzzy-rule-based systems with particle-swarm optimization. IEEE Trans Fuzzy Syst 18(6):1083-1097
14. Tsai T (2012) Development of a soldering quality classifier system using a hybrid data mining approach. Expert Syst Appl 39(5):5727-5738
15. Jiang Z, Jiang Y, Wang Y et al (2016) A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. J Intell Manuf. https://doi.org/10.1007/s10845-016-1231-0
16. Efthymiou K, Sipsas K, Mourtzis D et al (2015) On knowledge reuse for manufacturing systems design and planning:a semantic technology approach. CIRP J Manufact Sci Technol 8:1-11
17. Chryssolouris G, Papakostas N, Mourtzis D et al (2008). Knowledge management in manufacturing process modeling:case studies in selected manufacturing processes. In:Bernard A, Tichkiewitch S (eds) Methods and tools for effective knowledge life-cycle-management. Springer, Berlin, pp 507-520
18. Obayashi S (2011) Extraction of design rules from multi-objective design exploration (MODE) using rough set theory. Fluid Dyn Res 43(4):1465-1474
19. Anantasarn N, Suriyapraphadilok U, Babi DK (2017) A computer-aided approach for achieving sustainable process design by process intensification. Comput Chem Eng 105:56-73
20. Batoulis K, Meyer A, Bazhenova E et al (2015) Extracting decision logic from process models. Lect Notes Comput Sci 9097:349-366
21. Kluza K, Nalepa GJ (2017) A method for generation and design of business processes with business rules. Inf Softw Technol 91:123-141
22. Tran L, Banerjee D, Wang J et al (2013) High-dimensional MRI data analysis using a large-scale manifold learning approach. Mach Vis Appl 24(5):995-1014
23. Mantziou E, Papadopoulos S, Kompatsiaris Y (2015) Learning to detect concepts with approximate Laplacian eigen maps in largescale and online settings. Int J Multimed Inf Retr 4(2):95-111
24. Jain VK, Tapaswi S, Shukla A (2012) Location estimation based on semi-supervised locally linear embedding (SSLLE) approach for indoor wireless networks. Wirel Pers Commun 67(4):879-893
25. Kashniyal J, Verma S, Singh KP (2016) Wireless sensor networks localization using progressive isomap. Wirel Pers Commun 92:1-22
26. Srikant R, Agrawal R (2015) Mining generalized association rules. vldb-1995. Future Gener Comput Syst 13(2-3):161-180
27. Hu F, Wang CT, Wu YC et al (2014) Parameters selection of LLE algorithm for classification tasks. Adv Mater Res 1037:422-427

Thinking process rules extraction for manufacturing process design

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 0

Recommended Articles

Metrics

Comments