Abstract
Increasingly shortening product life cycles, regional market challenges and unforeseeable global events require highly iterative product and production adaptions. For faster adaptation, it is necessary to have a systematic understanding of the relationships between product design and production planning. A unified model and data structure are fundamental. Basic data must be extracted from both domains and integrated for consistent product-production co-design. For this purpose, we use a biological analogy, the genome-proteome phenomenon, to model the interdependencies of product (customer needs, functional requirements, design parameters) and production (technologies capabilities, machine information, process chain alternatives). From the genome, which represents the totality of available data of product and production, we contextualize the proteome, which represents an instance of a concrete product design and the corresponding production configuration. Thereby, one gene represents one incremental information set consisting of all above mentioned product and production information for a specific product function. For each of the mentioned information domains (e.g. product requirements) within a gene, a methodology exists (e.g. NLP) to model the interlinkage to the adjacent information domain (e.g. product function). Utilizing the interdependencies and heredity of product design and production planning enables quick analysis of adaptation-induced impact which will provide enhanced competitiveness in a volatile world.
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18 December 2021
In the original version of the book, the following belated correction has been incorporated: In the chapter “Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations”, the affiliation of Patrizia Gartner has been changed from “Institute of Production Science, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany” to “Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 02139, USA”.
References
Agarwal, R., Tiwari, M.K., Mukherjee, S.K.: Artificial immune system based approach for solving resource constraint project scheduling problem. Int. J. Adv. Manuf. Technol. 34, 584–593 (2007)
Ai, Q.S., Wang, Y., Liu, Q.: An intelligent method of product scheme design based on product gene. Adv. Mech. Eng. 5, 48927 (2013)
Akay, H., Kim, S.-G.: Design transcription: deep learning based design feature representation. CIRP Ann. 69, 141–144 (2020)
Akay, H., Kim, S.-G.: Reading functional requirements using machine learning-based language processing. CIRP Ann. 70, 139–142 (2021)
AlGeddawy, T., ElMaraghy, H.: A co-evolution model for prediction and synthesis of new products and manufacturing systems. J. Mech. Des. 134, 051008 (2012)
Byrne, G., Dimitrov, D., Monostori, L., Teti, R., van Houten, F., Wertheim, R.: Biologicalisation: biological transformation in manufacturing. CIRP J. Manuf. Sci. Technol. 21, 1–32 (2018)
Chen, K.-Z., Feng, X.-A., Chen, X.-C.: Reverse deduction of virtual chromosomes of manufactured products for their gene-engineering-based innovative design. Comput.-Aided Des. 37, 1191–1203 (2005)
Chen, Y., Feng, P., Lin, Z.: A genetics-based approach for the principle conceptual design of mechanical products. Int. J. Adv. Manuf. Technol. 27, 225–233 (2005)
Demeester, L., Eichler, K., Loch, C.H.: organic production systems: what the biological cell can teach us about manufacturing. M&SOM 6, 115–132 (2004)
Denkena, B., Dittrich, M.-A., Stamm, S., Wichmann, M., Wilmsmeier, S.: Gentelligent processes in biologically inspired manufacturing. CIRP J. Manuf. Sci. Technol. 32, 1–15 (2021)
Dias-Ferreira, J., Ribeiro, L., Akillioglu, H., Neves, P., Onori, M.: BIOSOARM: a bio-inspired self-organising architecture for manufacturing cyber-physical shopfloors. J. Intell. Manuf. 29(7), 1659–1682 (2016)
Feng, Y., Bagheri, E., Ensan, F., Jovanovic, J.: The state of the art in semantic relatedness: a framework for comparison. Knowl. Eng. Rev. 32, 10 (2017)
Park, H., Tran, N.H.: Development of a biology inspired manufacturing system for machining transmission cases. Int. J. Autom. Technol. 14(2), 233–240 (2013)
Hayes, R.H., Wheelwright, S.C.: Restoring our Competitive Edge: Competing Through Manufacturing. Wiley, New York (1984)
Hou, Y., Linhong, J.: Gene transcription and translation in design. In: 27th International Conference on Design Theory and Methodology. ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Boston, Massachusetts, USA, 02–05 Aug 2015, vol. 7. American Society of Mechanical Engineers (2015)
Jacob, A., Windhuber, K., Ranke, D., Lanza, G.: Planning, evaluation and optimization of product design and manufacturing technology chains for new product and production technologies on the example of additive manufacturing. Procedia CIRP 70, 108–113 (2018)
Jin, Y., Zouein, G.E., Lu, S.C.-Y.: A synthetic DNA based approach to design of adaptive systems. CIRP Ann. 58, 153–156 (2009)
Mak, K.L., Wong, Y.S., Wang, X.X.: An adaptive genetic algorithm for manufacturing cell formation. Int. J. Adv. Manuf. Technol. 16, 491–497 (2000)
Kim, S.-G., Yoon, S.M., Yang, M., Choi, J., Akay, H., Burnell, E.: AI for design: virtual design assistant. CIRP Ann. 68, 141–144 (2019)
Leitão, P., Barbosa, J., Trentesaux, D.: Bio-inspired multi-agent systems for reconfigurable manufacturing systems. Eng. Appl. Artif. Intell. 25, 934–944 (2012)
Madureira, A., Pereira, I.: Intelligent bio-inspired system for manufacturing scheduling under uncertainties. In: 2010 10th International Conference on Hybrid Intelligent Systems. 2010 10th International Conference on Hybrid Intelligent Systems (HIS 2010), Atlanta, GA, USA, 23–25 August 2010, pp. 109–112. IEEE (201)
Mostafa Mouassa, H.E.: Bio-inspired phylogenetics for designing product platforms and delayed differentiation utilizing hybrid additive or subtractive manufacturing. CIRP J. Manuf. Sci. Technol. 34, 119–132 (2021)
Mozgova, I., Lachmayer, R., Gottwald, P.: Formulations of paradigms of technical inheritance. In: International Conference on Engineering Design, ICED15. Leibniz Universität Hannover, Germany (2015)
Suh, N.P.: Axiomatic design theory for systems. Res. Eng. Des. 10, 189–209 (1998)
Renna, P.: Job shop scheduling by pheromone approach in a dynamic environment. Int. J. Comput. Integr. Manuf. 23, 412–424 (2010)
Sackett, P.J., Al-Gaylani, M.F., Tiwari, A., Williams, D.: A review of data visualization: opportunities in manufacturing sequence management. Int. J. Comput. Integr. Manuf. 19, 689–704 (2006)
Schindler, S.: Strategische Planung von Technologieketten für die Produktion. Dissertation (2014)
Suh, N.P.: Axiomatic design of mechanical systems. J. Mech. Des. 117, 2–10 (1995)
Tang, D., Zheng, K., Gu, W.: Adaptive Control of Bio-Inspired Manufacturing Systems. Springer, Singapore (2020)
Tassey, G.: Competing in advanced manufacturing: the need for improved growth models and policies. J. Econ. Perspect. 28, 27–48 (2014)
Ueda, K.: A concept for bionic manufacturing systems based on DNA-type information. In: Human Aspects in Computer Integrated Manufacturing, pp. 853–863. Elsevier (1992)
Waris, M.M., Sanin, C., Szczerbicki, E.: Smart innovation process enhancement using SOEKS and decisional DNA. J. Inf. Telecommun. 1, 290–303 (2017)
Zhang, W., Price, M., Robinson, T., Nolan, D., Kilpatrick, P., Barbhuiya, S.: Gene-inspired development of innovative design: principles and algorithm. Procedia CIRP 91, 838–843 (2020)
Zouhri, W., Rostami, H., Homri, L., Dantan, J.-Y.: A genetic-based SVM approach for quality data classification. In: Masrour, T., Cherrafi, A., El Hassani, I. (eds.) A2IA 2020. AISC, vol. 1193, pp. 15–31. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51186-9_2
Acknowledgements
The authors gratefully acknowledge funding from “MeSATech” as part of the “ProMed” project: production in medical technology (funding reference number: 02P18C135) and supervised by the Project Management Agency Karlsruhe (PTKA).
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Gartner, P. et al. (2022). Manufacturing Genome: A Foundation for Symbiotic, Highly Iterative Product and Production Adaptations. In: Andersen, AL., et al. Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems. CARV MCPC 2021 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-90700-6_3
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