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Mr. Ratnakar Lande is an Assistant Professor in the Department of Mechanical Engineering at G. H. Raisoni College of Engineering and Management, Nagpur, Maharashtra, India. He is engaged in teaching and academic activities in mechanical engineering. His contribution explores the use of multimodal AI for manufacturing inspection and industrial defect identification.
Quality inspection in manufacturing is an essential procedure for guaranteeing product dependability, operational efficiency, and adherence to industry regulations. Conventional inspection methods usually depend on manual examination, rule-based image processing, or individual sensor measurements. By combining complementing information from several modalities, intelligent inspection systems can improve flaw identification, categorisation, location, and quality assessment. This chapter describes a comprehensive framework for intelligent industrial quality inspection employing multimodal AI-based fault detection. It examines industrial data gathering, preprocessing, modality-specific feature extraction, multimodal fusion, defect categorisation, anomaly detection, and automated inspection. The section also examines real-time implementation, edge artificial intelligence, elucidative assessment, digital replicas, and amalgamation with Industry 4.0 frameworks. Challenges related to limited defect samples, class imbalance, sensor synchronization, domain adaptability, false detection, and processing requirements are addressed. The suggested framework establishes a basis for creating precise, scalable, and intelligent systems for manufacturing quality inspection.