Skip to main content
SpringerLink
Log in

Applying CBR to machine tool product configuration design oriented to customer requirements

  • Published:
Chinese Journal of Mechanical Engineering Submit manuscript

Abstract

Product customization is a trend in the current market-oriented manufacturing environment. However, deduction from customer requirements to design results and evaluation of design alternatives are still heavily reliant on the designer’s experience and knowledge. To solve the problem of fuzziness and uncertainty of customer requirements in product configuration, an analysis method based on the grey rough model is presented. The customer requirements can be converted into technical characteristics effectively. In addition, an optimization decision model for product planning is established to help the enterprises select the key technical characteristics under the constraints of cost and time to serve the customer to maximal satisfaction. A new case retrieval approach that combines the self-organizing map and fuzzy similarity priority ratio method is proposed in case-based design. The self-organizing map can reduce the retrieval range and increase the retrieval efficiency, and the fuzzy similarity priority ratio method can evaluate the similarity of cases comprehensively. To ensure that the final case has the best overall performance, an evaluation method of similar cases based on grey correlation analysis is proposed to evaluate similar cases to select the most suitable case. Furthermore, a computer-aided system is developed using MATLAB GUI to assist the product configuration design. The actual example and result on an ETC series machine tool product show that the proposed method is effective, rapid and accurate in the process of product configuration. The proposed methodology provides a detailed instruction for the product configuration design oriented to customer requirements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. QI Jin, HU Jie, PENG Yinghong, et al. AGFSM: An new FSM based on adapted Gaussian membership in case retrieval model for customer-driven design[J]. Expert Systems with Applications, 2011, 38(1): 894–905.

    Article  Google Scholar 

  2. TAHA Z, SOEWARDI H, DAWAL S Z M. Axiomatic design principles in analysing the ergonomics design parameter of a virtual environment [J]. International Journal of Industrial Ergonomics, 2014, 44(3): 368–373.

    Article  Google Scholar 

  3. ZHANG Zaifang, CHU Xuening. Fuzzy group decision-making for multi-format and multi-granularity linguistic judgments in quality function deployment[J]. Expert Systems with Applications, 2009, 36(5): 9150–9158.

    Article  Google Scholar 

  4. HU Xiao, WANG Zhaodong, WANG Guodong. Case-based reasoning(CBR) model for ultra-fast cooling in plate mill[J]. Chinese Journal of Mechanical Engineering, 2014, 27(6): 1264–1271.

    Article  Google Scholar 

  5. XU Man, YU Haiyan, SHEN Jiang. New algorithm for CBR-RBR fusion with robust thresholds[J]. Chinese Journal of Mechanical Engineering, 2012, 25(6): 1255–1263.

    Article  Google Scholar 

  6. STEPHANE N, HECTOR R, MARC L L J. Effective retrieval and new indexing method for case based reasoning: Application in chemical process design[J]. Engineering Applications of Artificial Intelligence, 2010, 23(6): 880–894.

    Article  Google Scholar 

  7. TSENG H E, CHANG C C, CHANG S H. Applying case-based reasoning for product configuration in mass customization environments[J]. Expert Systems with Applications, 2005, 29(4): 913–925.

    Article  Google Scholar 

  8. JUAN Y K, SHIH S G, PERNG Y H. Decision support for housing customization: A hybrid approach using case-based reasoning and genetic algorithm[J]. Expert Systems with Applications, 2006, 31(1): 83–93.

    Article  Google Scholar 

  9. FOK S C, YAP W P. A case-based design system for the conceptual design of electrical connectors[J]. International Journal of Advanced Manufacturing Technology, 2002, 20(11): 787–798.

    Article  Google Scholar 

  10. NOORI B. Developing a CBR system for marketing mix planning and weighting method selection using fuzzy AHP[J]. Applied Artificial Intelligence, 2015, 29(1): 1–32.

    Article  MathSciNet  Google Scholar 

  11. DAN B, GUO L F, WANG J P, et al. Intelligent configuration method of product family for mass customization based on constraints and cases[J]. Advances science letters, 2011, 4(6): 2478–2482.

    Article  Google Scholar 

  12. AHN Y W, AHN H J, PARK S J. Knowledge and case-based reasoning for customization of software processes-A hybrid approach[J]. International Journal of Software Engineering and Knowledge Engineering, 2003, 13(3): 293–312.

    Article  Google Scholar 

  13. HO G T S, LAU H C W, LEE C K M, et al. An intelligent forward quality enhancement system to achieve product customization[J]. Industrial Management & Data System, 2005, 105(3): 384–406.

    Article  Google Scholar 

  14. GUO Yuan, HU Jie, PENG Yinghong. Research on CBR system based on data mining[J]. Applied Soft Computing, 2011, 11(8): 5006–5014.

    Article  Google Scholar 

  15. LIN M C, WANG C C, CHEN M S, et al. Using AHP and TOPSIS approaches in customer-driven product design process[J]. Computers in Industry, 2008, 59(1): 17–31.

    Article  Google Scholar 

  16. ZHONG Shisheng, XIE Xiaolong, LIN Lin. Two-layer random forests model for case reuse in case-based reasoning [J]. Expert Systems with Applications, 2015, 42(24): 9412–9425.

    Article  Google Scholar 

  17. ZHANG Yu, BAI Xiaolan, ZHANG Chaobiao, et al. CBR-based intelligent modular combination method for CNC lathe[J]. Journal of Mechanical Engineering, 2014, 50(1): 120–129. (in Chinese)

    Article  Google Scholar 

  18. LI X Z, NI Y R, MING X G, et al. Module-based similarity measurement for commercial aircraft tooling design[J]. International Journal of Production Research, 2015, 53(17): 5382–5397.

    Article  Google Scholar 

  19. CHENG J C P, MA L J. A non-linear case-based reasoning approach for retrieval of similar cases and selection of target credits in LEED projects[J]. Building and Environment, 2015, 93(2): 349–361.

    Article  MathSciNet  Google Scholar 

  20. LI Zhi, ZHOU Xionghui, LIU Wei, et al. A geometry search approach in case-based tool reuse for mould manufacturing[J]. International Journal of Advanced Manufacturing Technology, 2015, 79(5): 757–768.

    Article  Google Scholar 

  21. KANG Y B, KRISHNASWAMY S, ZASLAVSKY A. A retrieval strategy for case-based reasoning using similarity and association knowledge[J]. IEEE Transactions on Cybernetics, 2014, 44(4): 473–487.

    Article  Google Scholar 

  22. ZHU Guoniu, HU Jie, QI Jin, et al. An integrated feature selection and cluster analysis techniques for case-based reasoning[J]. Engineering Applications of Artificial Intelligence, 2015, 39: 14–22.

    Article  Google Scholar 

  23. YUAN Changfeng, LIU Xiaobing, CHEN Yan. Product requirement analysis based on requirement unit[J]. Journal of Dalian Maritime University, 2008, 34(2): 113–116. (in Chinese)

    Google Scholar 

  24. LOU Jianren, ZHANG Shuyou, TAN Jianrong. Research on expressing and processing client demands for mass customization[J]. Journal China Mechanical Engineering, 2004, 15(8): 685–687. (in Chinese)

    Google Scholar 

  25. GUO Wei, HU Mingyan. Methodology of exploring and analyzing the VOC based on extensive web data source[J]. Computer Integrated Manufacturing Systems, 2004, 10(9): 1165–1170. (in Chinese)

    Google Scholar 

  26. ZHOU Kangqu, HAN Xiaogang, ZHU Xiaohong, et al. Research on customization model based on customer satisfaction[J]. Computer Integrated Manufacturing Systems, 2004, 10(9): 1338–1342. (in Chinese)

    Google Scholar 

  27. GUO Chenguang, LIU Yongxian, HOU Shouming, et al. Innovative product design based on customer requirement weight calculation model[J]. International Journal of Automation and Computing, 2010, 7(4): 578–583.

    Article  Google Scholar 

  28. ZHANG Li. Research on the structure of customer-drive parameter description for the mass customization model[J]. Journal of Hefei University of Technology (Natural Science), 2003, 26(6): 1152–1156. (in Chinese)

    Google Scholar 

  29. YAN W, KHOO L P, CHEN C H. A QFD-enabled product conceptualisation approach via design knowledge hierarchy and RCE neural network[J]. Knowledge-Based Systems, 2005, 18(6): 279–293.

    Article  Google Scholar 

  30. QI Jin, HU Jie, PENG Yinghong, et al. A case retrieval method combined with similarity measurement and multi-criteria decision making for concurrent design[J]. Expert Systems with Applications, 2009, 36(7): 10357–10366.

    Article  Google Scholar 

  31. SHIN K S, HAN I. Case-based reasoning supported by genetic algorithms for corporate bond rating[J]. Expert Systems with Application, 1999, 16(2): 85–95.

    Article  Google Scholar 

  32. CHENG Zhonghua, JIA Xisheng, GAO Ping, et al. A framework for intelligent reliability centered maintenance analysis[J]. Reliability Engineering and System Safety, 2008, 93(6): 806–814.

    Article  Google Scholar 

  33. CHIU C C, CHANG P C, CHIU N H. A case-based expert support system for due-date assignment in a wafer fabrication factory[J]. Journal of Intelligent Manufacturing, 2003, 14(3): 287–296.

    Article  Google Scholar 

  34. GUPTA K M, MONTEZEMI A R. Empirical evaluation of retrieval in case-based reasoning systems using modified cosine matching function[J]. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1997, 27(5): 601–612.

    Article  Google Scholar 

  35. ZADEH L A. Some reflections on the anniversary of Fuzzy Sets and Systems[J]. Fuzzy Sets and Systems, 1998, 100(1): 5–7.

    Article  MathSciNet  MATH  Google Scholar 

  36. CHAN F T S. Application of a hybrid case-base reasoning approach in electroplating industry[J]. Expert Systems with Applications, 2005, 29(1): 121–130.

    Article  Google Scholar 

  37. WU M C, LO Y F, HSH S H. A fuzzy CBR technique for generating product ideas[J] Expert Systems with Applications, 2008, 34(1): 530–540.

    Article  Google Scholar 

  38. LIU Qiaosheng, XI Juntong. Case-based parametric design system for test turntable[J]. Expert Systems with Applications, 2011, 38(6): 6508–6516.

    Article  Google Scholar 

  39. REYES E R, NEGNY S, ROBLES G C, et al. Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design[J]. Engineering Applications of Artificial Intelligence, 2015, 41: 1–16.

    Article  Google Scholar 

  40. LIN S W, CHEN S C. Parameter tuning, feature selection and weight assignment of features for case-based reasoning by artificial immune system[J]. Applied Soft Computing, 2011, 11(8): 5042–5052.

    Article  Google Scholar 

  41. CAO G, SHIU S C K, WANG X. A fuzzy-rough approach for the maintenance of distributed case-based reasoning systems[J]. Soft Computing, 2003,7(8): 491–499.

    Article  MATH  Google Scholar 

  42. YANG Q, WU J. Enhancing the effectiveness of interactive case-based reasoning with clustering and decision forests[J]. Applied Intelligence, 2001, 14(1): 49–64.

    Article  MATH  Google Scholar 

  43. JUNG S, LIM T, KIM D. Integrating radial basis function networks with case-based reasoning for product design[J]. Expert Systems with Applications, 2009, 36(3): 5695–5701.

    Article  Google Scholar 

  44. BAJO J, DE PAZ J F, RODRIGUEZ S, et al. A new clustering algorithm applying a hierarchical method neural network[J]. Logic Journal of The IGPL, 2011, 19(2): 304–314.

    Article  MathSciNet  Google Scholar 

  45. DURAY R, WARD P T, MILLIGAN G W, et al. Approaches to mass customization: configurations and empirical validation[J]. Journal of Operations Management, 2000, 18(6): 605–625.

    Article  Google Scholar 

  46. SHAN Quan, CHEN Yan. Product module identification based on assured customer requirements[C]//Procedia Engineering, Dali, China, August 18–19, 2011: 5313–5317.

    Google Scholar 

  47. BERNABE-MORENO J, TEJEDA-LORENTE A, PORCEL C, et al. CARESOME: A system to enrich marketing customers acquisition and retention campaigns using social media information[J]. Knowledge-Based Systems, 2015, 80: 163–179.

    Article  Google Scholar 

  48. AGARD B, KUSIAK A. Data-mining-based methodology for the design of Product families[J]. International Journal of Product Research, 2004, 42(15): 2955–2969.

    Article  MATH  Google Scholar 

  49. WANG Y, TSENG M. Incorporating tolerances of customers' requirements for customized products[J]. CIRP Annals- Manufacturing Technology, 2014, 63(1): 129–132.

    Article  Google Scholar 

  50. WANG Y, TSENG M M. Integrating comprehensive customer requirements into product design[J]. CIRP Annals-Manufacturing Technology, 2011, 60(1): 175–178.

    Article  Google Scholar 

  51. FENG C X, LI P G, LIANG M. Fuzzy mapping of requirements onto functions in detail design[J]. Computer-Aided Design, 2001, 33(6): 425–437.

    Article  Google Scholar 

  52. MCKAY A, DE PENNINGTON A, BAXTER J. Requirements management: a representation scheme for product specifications[J]. Computer-Aided Design, 2001, 33(7): 511–520.

    Article  Google Scholar 

  53. DAI J, BLACKHURST J. A four-phase AHP-QFD approach for supplier assessment: a sustainability perspective[J]. International Journal of Production Research, 2012, 50(19): 5474–5490.

    Article  Google Scholar 

  54. DU Y B, CAO H J, CHEN X, et al. Reuse-oriented redesign method of used products based on axiomatic design theory and QFD[J]. Journal of Cleaner Production, 2013, 39: 79–86.

    Article  Google Scholar 

  55. HO W, HE T, LEE C K M, et al. Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach[J]. Expert Systems with Applications, 2012, 39(12): 10841–10850.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110819, China

    Pengjia Wang, Yadong Gong, Hualong Xie & Yongxian Liu

  2. Department of Mechanical Engineering, National University of Singapore, Singapore, 117576, Singapore

    Andrew Yehching Nee

Authors
  1. Pengjia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yadong Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hualong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongxian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew Yehching Nee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pengjia Wang.

Additional information

Supported by State Science and Technology Support Program of China (Grant No. 2012BAF12B08-04), and Liaoning Provincial Key Scientific and Technological Project of China(Grant Nos. 2011216010, 2010020076-301)

WANG Pengjia, born in 1985, is currently a PhD candidate at School of Mechanical Engineering and Automation, Northeastern University, China. He received his master degree from Northeastern University, China, in 2011. His research interests include modular design, rapid response design, CAD/CAM, CIMS, manufacturing information, and product lifecycle management.

GONG Yadong, born in 1958, is currently a full professor at School of Mechanical Engineering and Automation, Northeastern University, China. His main research interests include grinding mechanism, digital manufacturing and micro-precision process.

XIE Hualong, born in 1978, is currently an associate professor at School of Mechanical Engineering and Automation, Northeastern University, China. His main research interests include intelligent robot theory, CAD/CAM, rehabilitation medical device and bionic machinery.

LIU Yongxian, born in 1945, is currently a full professor at School of Mechanical Engineering and Automation, Northeastern University, China. His research interests include CAD/CAM, CIMS, manufacturing information, product lifecycle management, and new structure machine tools.

NEE Andrew Yehching, born in 1948, is a full professor at Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore. Fellow SME(1990), Fellow CIRP(1990), Founding Fellow Academy of Engineering Singapore(2011). His research interests include virtual and augmented reality applications in manufacturing, computer-aided process and fixture planning, application of AI techniques in manufacturing, sustainable product design and manufacturing.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, P., Gong, Y., Xie, H. et al. Applying CBR to machine tool product configuration design oriented to customer requirements. Chin. J. Mech. Eng. 30, 60–76 (2017). https://doi.org/10.3901/CJME.2016.0113.007

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3901/CJME.2016.0113.007

Keywords

Search

Navigation