Sudarsan Rachuri

OntoSTEP: OWL-DL Ontology for STEP

The Standard for the Exchange of Product model data (STEP) [1] contains product information mainly related to geometry. The modeling language used to develop this standard, EXPRESS, does not have logical formalism that will enable rigorous semantics. In this paper we present an OWL-DL (Web Ontology Language Description Logic) [2] version of STEP (OntoSTEP) that will allow logic reasoning and inference mechanisms and thus enhancing semantic interoperability. The development of OntoSTEP requires the conversion of EXPRESS schema to OWL-DL, and the classification of EXPRESS instances to OWL individuals. Currently we have considered AP203 [3] the most widely used Application Protocol (AP) for the exchange of Computer-Aided Design (CAD) files and STEP Part 21 [4] CAD files CAD files conformant to the data exchange format defined in Part 21 for schema level conversion and instance level classification respectively. We have implemented a web application to demonstrate OntoSTEP. We are currently extending OntoSTEP to include information such as function, behavior, and assembly requirements. Keyword: STEP, OWL, ontology, reasoning, semantics

Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This paper presents the domain-specific knowledge that the approach should employ, the formal workflow of the approach, and a milling process use case to illustrate the proposed approach. We also discuss potential extensions of the approach.

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

Abstract 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.