Abstract
Industry 4.0 is the fourth generation of industry which will theoretically revolutionize manufacturing methods through the integration of machine learning and artificial intelligence approaches on the factory floor to obtain robustness and speed-up process changes. In particular, the use of the digital twin in a manufacturing environment makes it possible to test such approaches in a timely manner using a realistic 3D environment that limits incurring safety issues and danger of damage to resources. To obtain superior performance in an Industry 4.0 setup, a modified version of a binary gravitational search algorithm is introduced which benefits from an exclusive or (XOR) operator and a repository to improve the exploration property of the algorithm. Mathematical analysis of the proposed optimization approach is performed which resulted in two theorems which show that the proposed modification to the velocity vector can direct particles to the best particles. The use of repository in this algorithm provides a guideline to direct the particles to the best solutions more rapidly. The proposed algorithm is evaluated on some benchmark optimization problems covering a diverse range of functions including unimodal and multimodal as well as those which suffer from multiple local minima. The proposed algorithm is compared against several existing binary optimization algorithms including existing versions of a binary gravitational search algorithm, improved binary optimization, binary particle swarm optimization, binary grey wolf optimization and binary dragonfly optimization. To show that the proposed approach is an effective method to deal with real world binary optimization problems raised in an Industry 4.0 environment, it is then applied to optimize the assembly task of an industrial robot assembling an industrial calculator. The optimal movements obtained are then implemented on a real robot. Furthermore, the digital twin of a universal robot is developed, and its path planning is done in the presence of obstacles using the proposed optimization algorithm. The obtained path is then inspected by human expert and validated. It is shown that the proposed approach can effectively solve such optimization problems which arises in Industry 4.0 environment.
Highlights
It is highly desirable to prepare products to be served and used for customers with their own dominant needs, interests, energy and use standards
In the first set of optimization, the proposed XOR binary gravitational search algorithm (BGSA) is compared to BGSA [16], improved binary particle swarm optimization (IBPSO) [17], binary particle swarm optimization (BPSO) [15], binary grey wolf optimization (BGWO) [18] and binary dragonfly optimization algorithm (BDA) [19]
We show the fact that the existing acceleration and velocity term is not behaving as expected under certain conditions, and conclude that it is required to modify the acceleration term of BGSA
Summary
It is highly desirable to prepare products to be served and used for customers with their own dominant needs, interests, energy and use standards. The competition between different factories emerges mass production, while maintaining robustness and giving appropriate response to continuous design changes In such an environment, it is required for the production elements, including robots and other automated machines, to perform task planning automatically to minimize the costs and time consumed, while maximizing production rates and profit. The analysis in this paper shows that in the case of binary gravitational search algorithm (BGSA), the probability vector does not always behave as expected To alleviate this problem, the velocity vector and the probability of change is modified in this paper to include an exclusive or (XOR) operator. In real-world practice the path generated in the digital twin would be sent for human expert approval before transfer to physical robot Such an automated optimization task reduces the need for intervention, yet keeps human elements involved as a safety measure.
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