Evolutionary State Space and Adaptability Evaluation of Complex Competing Products

doi:10.3901/JME.2024.13.033

Abstract

Abstract: Competing products in the marketplace collectively form a complex artificial system that is interrelated, dynamically evolving, and difficult to predict. Adaptability is the capability of a product to be adapted for meeting different requirements. For a complex artificial system of competing products, enhanced product adaptability benefits management flexibility, rapid response to market requirements, reduced manufacturing cost, improved product maintainability and environmental friendliness. Better understanding of product evolution is crucial for enhancing products adaptability. The non-dominated design principle is proposed based on specification comparisons for product evolution analysis. On this basis, multi-order evolutionary state space of complex competing products is defined with mathematical formulations. Characteristics of the evolutionary state space include relativity, dynamicity, uniformity, boundedness, and convergence. Based on the multi-order evolutionary state space, concept and evaluation method of multi-order adaptabilities are proposed. Finally, a wind turbine was used as a case study to analyze and discuss its evolutionary state space and multi-order adaptability, illustrating the newly proposed concepts and methods.

Key words: product evolution, complex system, state space, design evaluation, adaptability

CLC Number:

TH122

ZHANG Jian, SONG Xinxian, GU Peihua. Evolutionary State Space and Adaptability Evaluation of Complex Competing Products[J]. Journal of Mechanical Engineering, 2024, 60(13): 33-47.

References

[1] CUMMING D J, NGUYEN G, NGUYEN M. Product market competition, venture capital, and the success of entrepreneurial firms[J]. Journal of Banking and Finance, 2022, 144：106561.
[2] DAVIS C, TOMODA Y. Competing incremental and breakthrough innovation in a model of product evolution[J]. Journal of Economics, 2018, 123：225-247.
[3] STARK R, FRESEMANN C, LINDOW K. Development and operation of digital twins for technical systems and services[J]. CIRP Annals-Manufacturing Technology, 2019, 68(1)：129-132.
[4] HELLWIG L, PREISSNER L, PAWLOWSKI J, et al. How digital fabrication technologies within digitalized innovation environments lead to participative aid development[J]. Journal of Enabling Technologies, 2022, 16(3)：219-230.
[5] PEDRO P, LUIS Q, JAVIER D, et al. A model of economic evaluation for the acquisition of flexible manufacturing technologies[J]. Procedia Manufacturing, 2019, 39：565-573.
[6] WATSON M, LEARY M, DOWNING D, et al. Generative design of space frames for additive manufacturing technology[J]. The International Journal of Advanced Manufacturing Technology, 2023, 127：4619- 4639.
[7] XU K, GONG Y, ZHAO Q. Comparison of traditional processing and additive manufacturing technologies in various performance aspects：A review[J]. Archives of Civil and Mechanical Engineering, 2023, 23：188.
[8] LEA B R, MIRCHANDANI D, SUMNER M, et al. Personality types in learning enterprise resource planning (ERP) systems[J]. Journal of Computer Information Systems, 2022, 62(2)：358-371.
[9] ABDISSA S, WORKU A, SHEKAR C. Design and development of product data management (PDM) for textile company[J]. Journal of Textile Science & Engineering, 2018, 8(4)：1-5.
[10] CONLON J. From PLM 1.0 to PLM 2.0：the evolving role of product lifecycle management (PLM) in the textile and apparel industries[J]. Journal of Fashion Marketing and Management：An International Journal, 2020, 24(4)：533-553.
[11] ZHANG J, LI B, PENG Q, et al. Product specifications analysis for modular product design using big sales data[J]. Chinese Journal of Mechanical Engineering, 2023, 36(1)：35-49.
[12] ULRICH K T, EPPINGER S D. Product design and development[M]. New York：McGraw-Hill Education, 2004.
[13] NIU H, VAN LEEUWEN C, HAO J, et al. Multimodal natural human–computer interfaces for computer-aided design：a review paper[J]. Applied Sciences, 2022, 13(12)：6510.
[14] HUNDE B R, WOLDEYOHANNES A D. Future prospects of computer-aided design (CAD)-a review from the perspective of artificial intelligence (AI), extended reality, and 3D printing[J]. Results in Engineering, 2022, 14：100478.
[15] 闫喜强, 李文强, 李彦. 基于Muti-Agent的复杂产品多学科协同概念设计实现[J]. 四川大学学报, 2014, 46(4)：176-182. YAN Xiqiang, LI Wenqiang, LI Yan. Implementation of multidisciplinary collaborative conceptual design for complex products based on muti agent[J]. Journal of Sichuan University, 2014, 46(4)：176-182.
[16] BONVOISIN J, HALSTENBERG F, BUCHERT T, et al. A systematic literature review on modular product design[J]. Journal of Engineering Design, 2016, 27(7)：488-514
[17] 郏维强, 刘振宇, 刘达新, 等. 基于模糊关联的复杂产品模块化设计方法及其应用[J]. 机械工程学报, 2015, 51(5)：130-142. JIA Weiqiang, LIU Zhenyu, LIU Daxin, et al. Modular design method for complex products based on fuzzy correlation and its application[J]. Journal of Mechanical Engineering, 2015, 51(5)：130-142.
[18] ACAR E, BAYRAK G, JUNG Y, et al. Modeling, analysis, and optimization under uncertainties：a review[J]. Structural and Multidisciplinary Optimization, 2021, 64：2909-2945.
[19] 韩旭, 姜潮, 陈国栋, 等. 基于代理模型的汽车结构安全多目标优化的研究报告[J]. 科技创新导报, 2016, 13(19)：179-180. HAN Xu, JIANG Chao, CHEN Guodong, et al. Research report on multi-objective optimization of automotive structural safety based on proxy model[J]. Science and Technology Innovation Guide, 2016, 13(19)：179-180.
[20] IRELAND R, LIU A. Application of data analytics for product design：Sentiment analysis of online product reviews[J]. CIRP Journal of Manufacturing Science and Technology, 2018, 23：128-144.
[21] 罗仕鉴, 李文杰, 傅业焘. 消费者偏好驱动的SUV产品族侧面外形基因设计[J]. 机械工程学报, 2016, 52(2)：173-181. LUO Shijian, LI Wenjie, FU Yetao. The gene design of SUV product family side appearance driven by consumer preference[J]. Journal of Mechanical Engineering, 2016, 52(2)：173-181.
[22] PETKOV V. A model of dales with fifferentiated and homogeneous goods[J]. Economics Letters, 2018, 171：214-217.
[23] GRAZIANO C, RONDI L. Product market competition, executive compensation, and CEO family ties[J]. Review of Industrial Organization, 2020, 58(3)：357-397.
[24] KINGSMAN B, WORDEN L, HENDRY L, et al. Integrating marketing and production planning in make-to-order companies[J]. International Journal of Production Economics, 1993, 30：53-56.
[25] ZHANG J, SIMEONE A, PENG Q, et al. Dependency and correlation analysis of specifications and parameters of products for supporting design decisions[J]. CIRP Annals- Manufacturing Technology, 2020, 69(1)：133-136.
[26] BRAHMA A, WYNN D C. Concepts of change propagation analysis in engineering design[J]. Research in Engineering Design, 2023, 34：117-151.
[27] SHAO P, TAN R, PENG Q, et al. An integrated method to acquire technological evolution potential to stimulate innovative product design[J]. Mathematics, 2023, 11(3)：1-24.
[28] 檀润华. TRIZ及应用技术创新过程与方法[M]. 北京：高等教育出版社, 2010. TAN Runhua. TRIZ and its application technology innovation process and methods[M]. Beijing：Higher Education Press, 2010.
[29] GU P, HASHEMIAN M, NEE A Y C. Adaptable design[J]. CIRP Annals-Manufacturing Technology, 2004, 53(2)：539-557.
[30] GU P, XUE D, PENG Q, et al. Adaptable design：methods and applications[M]. Singapore：Springer, 2023.
[31] SUH NP. Axiomatic design：advances and applications[M]. Oxford：Oxford University Press, 2001.
[32] LI H, JI Y, CHEN L, et al. Bi-level coordinated configuration optimization for product-service system modular design[J]. IEEE Transactions on Systems, Man, and Cybernetics：Systems, 2017, 47(3)：537-554.
[33] SUN Z L, WANG K, GU P. Information entropy approach to design adaptability evaluation[J]. CIRP Annals- Manufacturing Technology, 2023, 72(1)：97-100.
[34] ZHANG J, XIE G, PENG Q, et al. Product evolution analysis for design adaptation using big sales data[J]. Proceedings of the Institution of Mechanical Engineers, Part B：Journal of Engineering Manufacture, 2022, 237(8)：252447303.
[35] ZHANG J, GAO J, SIMEONE A, et al. Game analysis of product specifications for design optimization using big sales data[J]. Journal of Engineering Design, 2023, 34(10)：844-864.
[36] WITTEN I H, FRANK E, HALL M A, et al. Data mining：Practical machine learning tools and techniques[M]. Burlington：Morgan Kaufmann, 2016.
[37] MYERS L, SIROIS M J. Spearman correlation coefficients, differences between：Sequential estimation of the mean in finite populations to steiner's most frequent value[C]// In：Encyclopedia of Statistical Sciences, John Wiley & Sons Inc, Hoboken, New York, 2006.
[38] DASH M, LIU H, SCHEUERMANN P, et al. Fast hierarchical clustering and its validation[J]. Data & Knowledge Engineering, 2003, 44(1)：109-138.
[39] KRIEGEL HP, KRÖGER P, ZIMEK A. Clustering high- dimensional data[J]. ACM Transactions on Knowledge Discovery from Data, 2009, 3(1)：1-58.
[40] CHEN J, WANG Q. Wind turbine airfoils and blades：Optimization design theory[M]. Science Press, 2017.
[41] 吴佳梁, 李成锋. 海上风力发电机组设计[M]. 北京：化学工业出版社, 2012. WU Jialiang, LI Chenfeng. Offshore wind turbine design[M]. Beijing：Chemical Industry Press, 2012.
[42] Windturbines database [DB/OL]. https://en.wind-turbine-models.com/turbines,2023-08-01.