A methodology for the semi-automatic generation of analytical models in manufacturing

Advanced analytics can enable manufacturing engineers to improve product quality and achieve equipment and resource efficiency gains using large amounts of data collected during manufacturing. Manufacturing engineers, however, often lack the expertise to apply advanced analytics, relying instead on frequent consultations with data scientists. Furthermore, collaborations between manufacturing engineers and data scientists have resulted in highly specialized applications that are not relevant to broader use cases. The manufacturing industry can benefit from the techniques applied in these collaborations if they can be generalized for a wide range of manufacturing problems without requiring a strong knowledge about analytical models. This paper first presents a model-based methodology to help manufacturing engineers who have little or no experience in advanced analytics apply machine learning techniques for manufacturing problems. This methodology includes a meta-model repository and model transformations. The meta-models define concepts and rules that are commonly known in the manufacturing industry in order to facilitate the creation of manufacturing models. The model transformations enable the semi-automatic generation of analytical models using a given manufacturing model. Second, a model-based Tool for ADvanced Analytics in Manufacturing (TADAM) is presented to allow manufacturing engineers to apply the methodology. TADAM offers capabilities to generate neural networks for manufacturing process problems. Using TADAM's graphical user interface, a manufacturing engineer can build a model for a given process to provide: 1) the key performance indicator (KPI) to be predicted, and 2) the variables contributing to this KPI. Once the manufacturing engineer has built the model and provided the associated data, the model transformations available in TADAM can be called to generate a trained neural network. Finally, the benefits of TADAM are demonstrated in a manufacturing use case in which a manufacturing engineer generates a neural network to predict the energy consumption of a milling process.