# 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. natural language processing; NLP; part of speech; POS tagging; closed domain; operator support; assembly instructions; NLTK; SPACY; Stanford; benchmark +
Footnote †: journal: Applied sciences  
0024202430  
systems with digital technologies, enabling seamless communication and collaboration between humans and machines. Smart assistance systems leverage these principles to provide operators real-time support, context-aware guidance, and predictive maintenance insights [7]. These systems optimize production workflows, reduce downtime, and improve operational efficiency by harnessing data analytics and AI. Therefore, addressing the challenges in operator assistance systems is crucial for the manufacturing industry to leverage the full potential of Industry 4.0 and to remain competitive in the global marketplace. While existing support services for assembly operators provide procedural digital work instructions, there are notable limitations. Operators must often rely on their own knowledge or seek help from external experts when faced with unexpected challenges in the assembly process. For example, if a unique problem arises during an assembly task that deviates from the standard instructions, the current systems may not provide adequate guidance. This reliance on human expertise can lead to delays and reduced efficiency on the factory floor. The digitization of information presents an opportunity to address these limitations by gathering data from IT systems and providing more comprehensive support to operators [8]. To bridge this gap, adaptive assistance systems have emerged as a promising solution in manufacturing contexts. These systems encompass a range of technologies, including head-mounted displays [9], augmented reality [10; 11], tangible user interfaces [12], and motion recognition [13]. They aim to provide real-time, context-aware support to operators, adapting to the specific needs of each assembly task and helping operators overcome unexpected challenges more effectively. Most existing support systems in the assembly domain are designed around well-defined, pre-established questions and answers. These systems are effective when there is a direct mapping between a question posed by an operator and a known answer, and this mapping has been pre-defined within the system. However, a significant challenge arises when operators ask open-ended questions for which no predefined semantic links or specific answers exist. Moreover, these open questions are often formulated incompletely, making it even more challenging to interpret their correct semantic meaning [14].