Abstract
Literature shows that reinforcement learning (RL) and the well-known optimization algorithms derived from it have been applied to assembly sequence planning (ASP); however, the way this is done, as an offline process, ends up generating optimization methods that are not exploiting the full potential of RL. Today’s assembly lines need to be adaptive to changes, resilient to errors and attentive to the operators’ skills and needs. If all of these aspects need to evolve towards a new paradigm, called Industry 4.0, the way RL is applied to ASP needs to change as well: the RL phase has to be part of the assembly execution phase and be optimized with time and several repetitions of the process. This article presents an agile exploratory experiment in ASP to prove the effectiveness of RL techniques to execute ASP as an adaptive, online and experience-driven optimization process, directly at assembly time. The human-assembly interaction is modelled through the input-outputs of an assembly guidance system built as an assembly digital twin. Experimental assemblies are executed without pre-established assembly sequence plans and adapted to the operators’ needs. The experiments show that precedence and transition matrices for an assembly can be generated from the statistical knowledge of several different assembly executions. When the frequency of a given subassembly reinforces its importance, statistical results obtained from the experiments prove that online RL applications are not only possible but also effective for learning, teaching, executing and improving assembly tasks at the same time. This article paves the way towards the application of online RL algorithms to ASP.
Highlights
Sometimes good things have to be broken in order to be rebuilt even better, a process referred as disruption
Sequence planning (ASP) seems to require it because of the recent introduction of the paradigms of Industry 4.0 and smart manufacturing [1] that ask for manufacturing systems and, assembly lines to be adaptive to changes [2,3,4,5], flexible [6], evolvable [7,8], resilient to errors [9] and attentive to the more knowledgeable operators’ skills and needs [10,11]
Reinforcement learning (RL) is a machine learning method [13] that deviates from the idea of training learning algorithms only on all the available data, by accepting the possibility that the training could continue over the execution phase, when the algorithm is applied
Summary
Sometimes good things have to be broken in order to be rebuilt even better, a process referred as disruption. Sequence planning (ASP) seems to require it because of the recent introduction of the paradigms of Industry 4.0 and smart manufacturing [1] that ask for manufacturing systems and, assembly lines to be adaptive to changes [2,3,4,5], flexible [6], evolvable [7,8], resilient to errors [9] and attentive to the more knowledgeable operators’ skills and needs [10,11]. Even though RL adaptable systems have been suc cessfully developed for other manufacturing purposes [16], the approach is not yet feasible in assembly, as simulating the overall complexity of an assembly line and the interaction with humans is still license (http://creativecommons.org/licenses/by/4.0/)
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