Framework for Potential Analysis by Approximating Line-Less Assembly Systems with AutoML
Assembly systems are required to be more flexible due to increasing product variety. This is accomplished by breaking up the prevailing rigid linking of assembly stations in classic line configurations to line-less assembly systems (LAS). For a quantified potential analysis, it is necessary to assess the dependency of performance indicators on input parameters in a large number of production scenarios. Existing methods are either experience-based or computationally expensive due to full-factorial experiment plans. Therefore, the contribution of this paper is threefold. First, a seamlessly automated scenario analysis tool is developed, simulating a large set of assembly scenarios using a discrete-event simulation. Second, an artificial neural network (ANN) based approximation pipeline of the scenario analysis is implemented. An integrated AutoML pipeline for hyperparameter optimization (HPO) and Neural Architecture Search (NAS) allows for a faster potential approximation with sufficiently accurate prediction. Last, an integrated decision support system including the mentioned components is defined. It includes a priori planning of the overall system and allows the assessment of adaptions in reaction to the current system status.