A DIGITAL-AGE APPROACH OF MANUFACTURING OPTIMIZATION

Abstract

The problem of manufacturing process optimization is one of the most addressed issues across literature concerning the manufacturing field. Until now, this optimization has been based on indirect description of manufacturing, through prefabricated algorithmic models (be they analytical or numerical), as well as on information processing, by composing this kind of models. This is why the optimization is difficult and complicated, even in the case of the simplest processes. This paper presents a new approach of the manufacturing optimization concept, having as main defining aspects: i) The manufacturing process is regarded as decisional instead of physical process, ii) The optimization object is the manufacturing task, defined as product transition from current state up to final state, iii) The manufacturing model consists in a dataset, and, iv) The optimization problem solution consists in task configuring, task accomplishment, and task modeling. According to the new approach, the optimization result consists in the ensemble formed by an optimal structure (optimal configuration of the given task), an optimal process (optimal values of the process variables), and an optimal model (obtained by iterative rebuilding of the manufacturing model). The application of the proposed approach is sampled through a comparative illustration.