Scalable Development and Deployment of LLMs in Manufacturing: Leveraging AI to Enhance Predictive Maintenance, Quality Control, and Process Automation
Keywords:
Large Language Models, predictive maintenanceAbstract
Other sectors including manufacturing evolved using LLMs. Scalability of LLM development and use for manufacturing predictive maintenance, quality control, and process automation is covered in this paper. LLMs may manage many information systems and enormous amounts of data in data-driven enterprises. Research shows that when coupled with advanced machine learning and deep learning, LLMs can foresee equipment breakdowns, insure high-quality production, and automate complex processes better than traditional techniques. Still, LLM generation is challenging. Industrial data heterogeneous, legacy system integration, deployment efficiency, real-time processing. This work presents federated learning for distributed data processing, transfer learning for manufacturing job adaptability, and model compression for edge device deployment in order to extend LLMs.
The paper addresses predictive maintenance and manufacturing LLMs. Extensive sensor data allows LLM-driven predictive maintenance models to foresee equipment faults and maintenance needs. Whereas LLMs employ real-time data analytics to decrease downtime and costs, standard predictive maintenance makes use of past data. By use of NLP and computer vision, LLMs enhance anomaly detection, defect prediction, and production line quality. By providing contextual insights from unstructured data such as operator logs and inspection reports—something ordinary machine learning models cannot—LLMs help to enhance quality control.
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