The use of Digital Intelligent Assistants (DIAs) in manufacturing aims to enhance performance and reduce cognitive workload. By leveraging the advanced capabilities of Large Language Models (LLMs), the research aims to understand the impact of DIAs on assembly processes, emphasizing human-centric design and operational efficiency. The study is novel in considering the three primary objectives: evaluating the technical robustness of DIAs, assessing their effect on operators' cognitive workload and user experience, and determining the overall performance improvement of the assembly process. Methodologically, the research employs a laboratory experiment, incorporating a controlled setting to meticulously assess the DIA's performance. The experiment used a between-subjects design comparing a group of participants using the DIA against a control group relying on traditional manual methods across a series of assembly tasks. Findings reveal a significant enhancement in the operators' experience, a reduction in cognitive load, and an improvement in the quality of process outputs when the DIA is employed. The article contributes to the study of the DIA's potential and AI integration in manufacturing, offering insights into the design, development, and evaluation of DIAs in industrial settings.

中文翻译：

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装配制造中基于大语言模型的数字智能助手的评估


在制造中使用数字智能助理 (DIA) 旨在提高性能并减少认知工作量。通过利用大型语言模型 ( LLMs ) 的先进功能，该研究旨在了解 DIA 对装配流程的影响，强调以人为本的设计和运营效率。该研究的新颖之处在于考虑了三个主要目标：评估DIA的技术稳健性、评估其对操作员认知工作量和用户体验的影响以及确定装配过程的整体性能改进。从方法上来说，该研究采用了实验室实验，结合受控环境来仔细评估 DIA 的性能。该实验采用受试者间设计，将使用 DIA 的一组参与者与依赖传统手动方法完成一系列组装任务的对照组进行比较。研究结果表明，使用 DIA 后，操作员的体验显着增强，认知负荷减少，过程输出质量提高。本文有助于研究 DIA 的潜力和 AI 在制造业中的集成，提供有关工业环境中 DIA 的设计、开发和评估的见解。