| Title |
Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems |
| ID_Doc |
5435 |
| Authors |
Brad, E; Brad, S |
| Title |
Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems |
| Year |
2021 |
| Published |
Applied Sciences-Basel, 11, 10 |
| DOI |
10.3390/app11104446 |
| Abstract |
In the paradigm of industry 4.0, manufacturing enterprises need a high level of agility to adapt fast and with low costs to small batches of diversified products. They also need to reduce the environmental impact and adopt the paradigm of the circular economy. In the configuration space defined by this duality, manufacturing systems must embed a high level of reconfigurability at the level of their equipment. Finding the most appropriate concept of each reconfigurable equipment that composes an eco-smart manufacturing system is challenging because every system is unique in the context of an enterprise's business model and technological focus. To reduce the entropy and to minimize the loss function in the design process of reconfigurable equipment, an evolutionary algorithm is proposed in this paper. It combines the particle swarm optimization (PSO) method with the theory of inventive problem-solving (TRIZ) to systematically guide the creative potential of design engineers towards the definition of the optimal concept over equipment's lifecycle: what and when you need, no more, no less. The algorithm reduces the number of iterations in designing the optimal solution. An example for configuration design of a reconfigurable machine tool with adjustable functionality is included to demonstrate the effectiveness of the proposed algorithm. |
| Author Keywords |
reconfigurability; reconfigurable equipment; evolutionary algorithm; particle swarm intelligence; TRIZ; design optimization; industry 4; 0 |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000662598400001 |
| WoS Category |
Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied |
| Research Area |
Chemistry; Engineering; Materials Science; Physics |
| PDF |
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