Natural Language Processing in Knowledge-Based Support for Operator Assistance

Abstract

Manufacturing industry faces increasing complexity in the performance of assembly tasks due to escalating demand for complex products with a greater number of variations. Operators require robust assistance systems to enhance productivity, efficiency, and safety. However, existing support services often fall short when operators encounter unstructured open questions and incomplete sentences due to primarily relying on procedural digital work instructions. This draws attention to the need for practical application of natural language processing (NLP) techniques. This study addresses these challenges by introducing a domain-specific dataset tailored to assembly tasks, capturing unique language patterns and linguistic characteristics. We explore strategies to process declarative and imperative sentences, including incomplete ones, effectively. Thorough evaluation of three pre-trained NLP libraries—NLTK, SPACY, and Stanford—is performed to assess their effectiveness in handling assembly-related concepts and ability to address the domain’s distinctive challenges. Our findings demonstrate the efficient performance of these open-source NLP libraries in accurately handling assembly-related concepts. By providing valuable insights, our research contributes to developing intelligent operator assistance systems, bridging the gap between NLP techniques and the assembly domain within manufacturing industry.