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Open AccessCognitive Computing in Manufacturing: Transformative Applications of Natural Language Processing for Human-Machine Interaction in Industry 4.0
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Manufacturing processes must use natural language processing (NLP) to provide a user-friendly interface for human-machine interaction. Natural language processing (NLP) presents numerous challenges in the manufacturing environments characterized by Industry 4.0, including language barriers, processing bottlenecks in real-time, and data security challenges. The research develops the Cognitive Language Real-Time Processing Optimization (CLR-TPO) method to address these problems with real-time processing limitations in Industry 4.0 human-machine interactions. The goal is to leverage parallel processing architectures and edge computing to increase communication speed. Using state-of-the-art edge computing and parallel processing architectures, CLR-TPO enhances real-time capabilities to ensure rapid and responsive machine-human interactions. Its adaptive learning abilities enable it to gain more language knowledge and adjust to different languages swiftly. Cognitive computing has the potential to fundamentally change several industrial fields, including intelligent process optimization, supply chain management, quality control, and predictive maintenance. This study explores many applications of CLR-TPO, with an emphasis on how it improves operational efficiency and manufacturing processes. The experimental results show that the proposed CLR-TPO model increases the performance rate of 98.6%, Adaptability Analysis of 97.6%, latency analysis of 14.3%, scalability ratio of 98.9%, and accuracy ratio of 96.7% compared to other existing models.
