4.7 Article

Multiple platforms design and product family process planning for combined additive and subtractive manufacturing

期刊

JOURNAL OF MANUFACTURING SYSTEMS
卷 61, 期 -, 页码 509-529

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ELSEVIER SCI LTD
DOI: 10.1016/j.jmsy.2021.09.019

关键词

Additive manufacturing; Product platform; Product family; Mass customization

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Industry 4.0 promotes the utilization of new technologies such as additive manufacturing to address manufacturing challenges. The integration of additive and subtractive manufacturing technologies, as well as the use of product platforms, can improve manufacturing efficiency and customization. A genetic algorithm-based model is developed to determine optimal multiple platforms for cost-effective manufacturing of product families.
Industry 4.0 promotes the utilization of new exponential technologies such as additive manufacturing in responding to different manufacturing challenges. Among these, the integration of additive and subtractive manufacturing technologies can play an important role and be a game changer in manufacturing products. In addition, using product platforms improves the efficiency and responsiveness of manufacturing systems and is considered an enabler of mass customization. In this paper, a model to design multiple platforms that can be customized using additive and subtractive manufacturing to manufacture a product family cost-effectively is proposed. The developed model is used to determine the optimal number of product platforms, each platform design (i.e. its features set), the assignment of each platform to various product variants, and the macro process plans for customizing the platforms while minimizing the overall product family manufacturing cost. The multiple additive/subtractive platforms and their process plans are determined by considering not only the commonality between the product variants but also their various manufacturing cost elements and the customer demand of each variant. The design of multiple product family platforms and their process plans is NPhard problem. A genetic algorithm-based model is developed to reduce the computational complexity and find optimal or near optimal solution. Two case studies are used to illustrate the developed multiple platform model. The model results were compared with a single platform model in literature and the results demonstrate the multiple platform model superiority in manufacturing product families in lower cost. The use of the developed model enables manufacturing product families cost efficiently and allows manufacturers to manage diversity in products and market demands.

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