Modular product design with grouping genetic algorithm—a case study

2004 ◽  
Vol 46 (3) ◽  
pp. 443-460 ◽  
Author(s):  
Victor B. Kreng ◽  
Tseng-Pin Lee
2019 ◽  
Vol 27 (4) ◽  
pp. 331-346 ◽  
Author(s):  
Olivia Borgue ◽  
Massimo Panarotto ◽  
Ola Isaksson

For space manufacturers, additive manufacturing promises to dramatically reduce weight and costs by means of integral designs achieved through part consolidation. However, integrated designs hinder the ability to change and service components over time – actually increasing costs – which is instead enabled by highly modular designs. Finding the optimal trade-off between integral and modular designs in additive manufacturing is of critical importance. In this article, a product modularisation methodology is proposed for supporting such trade-offs. The methodology is based on combining function modelling with optimisation algorithms. It evaluates product design concepts with respect to product adaptability, component interface costs, manufacturing costs and cost of post-processing activities. The developed product modularisation methodology is derived from data collected through a series of workshops with industrial practitioners from three different manufacturer companies of space products. The implementation of the methodology is demonstrated in a case study featuring the redesign of a satellite antenna.


Author(s):  
Junfeng Ma ◽  
Gül E. Okudan Kremer

Sustainability has been the emphasis of intense discussion over recent decades, but mostly focused on addressing critical aspects of environmental issues. An increasing awareness of social responsibilities and ever-shifting customer requirements have led manufacturers to consider social sustainability during the design phase in tandem with addressing environmental concerns; thus, design for social sustainability has evolved as a new product design direction. Modular product design (MPD), has been widely used in both academia and industry because of its significant benefits in design engineering. Because of the potential synergy, investigating design for social sustainability in association with MPD holds promise as a field of investigation. In this paper, we introduce a novel MPD approach that uses the elements of key component specification and product impact on social sustainability. The key components carry core technologies or have the highest sustainability effects in a product (i.e., the most costly or environmentally polluting parts). Product competitiveness strongly relies on a few key components that should be a focal point during product development. However, to the best of our knowledge, key components have not been well addressed in modular product design. In this paper, we employ labor time as an indicator to measure social sustainability. A heuristic-based clustering algorithm with labor time optimization is developed to categorize components into modules. A coffee-maker case study is conducted to demonstrate the applicability of the proposed methodology.


2009 ◽  
Vol 2009.19 (0) ◽  
pp. 259-261
Author(s):  
Akihiro Hirao ◽  
Tsuyoshi Koga ◽  
Takashi Niwa ◽  
Kazuya Oizumi ◽  
Kazuhiro Aoyama

Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 37 ◽  
Author(s):  
Charul Chadha ◽  
Kathryn Crowe ◽  
Christina Carmen ◽  
Albert Patterson

This work explores an additive-manufacturing-enabled combination-of-function approach for design of modular products. AM technologies allow the design and manufacturing of nearly free-form geometry, which can be used to create more complex, multi-function or multi-feature parts. The approach presented here replaces sub-assemblies within a modular product or system with more complex consolidated parts that are designed and manufactured using AM technologies. This approach can increase the reliability of systems and products by reducing the number of interfaces, as well as allowing the optimization of the more complex parts during the design. The smaller part count and the ability of users to replace or upgrade the system or product parts on-demand should reduce user risk, life-cycle costs, and prevent obsolescence for the user of many systems. This study presents a detailed review on the current state-of-the-art in modular product design in order to demonstrate the place, need and usefulness of this AM-enabled method for systems and products that could benefit from it. A detailed case study is developed and presented to illustrate the concepts.


Author(s):  
John Jung-Woon Yoo ◽  
Anirudh Aryasomayajula ◽  
Seung Ki Moon

In our earlier work, we have proposed a cyberinfrastructure-based collaboration system for modular product design. One of the main components of the system is a design repository to which suppliers can upload the descriptions of their components using machine-readable, interface-based component description language, so that manufacturers can refer to the descriptions during product design phases. In this paper, we propose an efficient algorithmic approach based on a branch-and-bound (BnB) algorithm to support product design using the interface-based component descriptions stored in the design repository. This product design problem is categorized into a planning problem, whose complexity is known as non-deterministic polynomial-time (NP) hard. For performance evaluation, we compare the performance of the branch-and-bound algorithm with that of a depth-first search (DFS) algorithm, which is an exhaustive search method. This paper describes the details of the proposed branch-and-bound algorithm using a case study and experimental results are discussed.


2018 ◽  
Vol 10 (9) ◽  
pp. 3013 ◽  
Author(s):  
Manoj Paras ◽  
Lichuan Wang ◽  
Yan Chen ◽  
Antonela Curteza ◽  
Rudrajeet Pal ◽  
...  

The scarcity of natural resources and the problem of pollution have initiated the need for extending the life and use of existing products. The concept of the reverse supply chain provides an opportunity to recover value from discarded products. The potential for recovery and the improvement of value in the reverse supply chain of apparel has been barely studied. In this research, a novel modularized redesign model is developed and applied to the garment redesign process. The concept of modularization is used to extract parts from the end-of-use or end-of-life of products. The extracted parts are reassembled or reconstructed with the help of a proposed group genetic algorithm by using domain and industry-specific knowledge. Design fitness is calculated to achieve the optimal redesign. Subsequently, the practical relevance of the model is investigated with the help of an industrial case in Sweden. The case study finding reveals that the proposed method and model to calculate the design fitness could simplify the redesign process. The design fitness calculation is illustrated with the example of a polo t-shirt. The redesigned system-based modularization is in accordance with the practical situations because of its flexibility and viability to formulate redesign decisions. The grouping genetic algorithm could enable fast redesign decisions for designers.


Author(s):  
Maged R. Rostom ◽  
Ashraf O. Nassef ◽  
Sayed M. Metwalli

The cutting stock problem (CSP) is a business problem that arises in many areas, particularly in manufacturing industries where a given stock material must be cut into a smaller set of shapes. It has gained a lot of attention for increasing efficiency in industrial engineering, logistics and manufacturing. This paper presents a hybrid new 3-D overlapped grouping Genetic Algorithm (GA) that solves two-dimensional cutting stock problems for nesting the rectangular shapes. The objective is the minimization of the wastage of the sheet material which leads to maximizing material utilization and the minimization of the setup time. The model and its results are compared with real life case study from a steel workshop in a bus manufacturing factory. The effectiveness of the proposed approach is shown by comparing and shop testing of the optimized cutting schedules. The results reveal its superiority in terms of waste minimization comparing to the current cutting schedules and show that our approach outperforms existing heuristic algorithms. The whole procedure can be completed in a reasonable amount of time by the developed optimization program.


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