Advanced manufacturing that is adaptable to constantly changing product designs often requires dynamic changes on the factory floor to enable manufacture. The integration of robotic manufacture with machine learning approaches offers the possibility to enable such dynamic changes on the factory floor. While ensuring safety and the possibility of losses of components and waste of material are against their usage. Furthermore, developments in design of virtual environments makes it possible to perform simulations in a virtual environment, to enable human-inthe-loop production of parts correctly the first time like never before. Such powerful simulation and control software provides the means to design a digital twin of manufacturing environment in which trials are completed at almost at no cost. In this paper, ant colony optimization is used to program an industrial robot to avoid obstacles and find its way to pick and place objects during an assembly task in an environment containing obstacles that must be avoided. The optimization is completed in a digital twin environment first and movements transferred to the real robot after human inspection. It is shown that the proposed methodology can find the optimal solution, in addition to avoiding collisions, for an assembly task with minimum human intervention.
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.
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