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Dr. Hirendra Singh is an Assistant Professor in the Department of Mechanical Engineering at UIET, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India. He is involved in mechanical engineering education and academic activities. His contribution addresses intelligent manufacturing quality assessment and automated defect detection.
Industrial machinery is essential in manufacturing, energy generation, transportation, processing sectors, and infrastructural activities. Unforeseen machinery malfunctions may lead to production delays, heightened maintenance expenses, safety hazards, and diminished operational effectiveness. Multimodal AI offers a sophisticated methodology by amalgamating diverse industrial data sources, such as vibration signals, acoustic measurements, thermal imagery, electrical parameters, pressure readings, operating logs, and maintenance documentation. Through the acquisition of complimentary patterns across many modalities, multimodal AI can enhance defect detection, equipment state evaluation, failure forecasting, and maintenance strategising. This section offers an extensive structure for the oversight of industrial machinery and anticipatory upkeep through the application of multimodal artificial intelligence. The section also examines real-time implementation, edge artificial intelligence, digital replicas, interpretable AI, cybersecurity, and data dependability. The suggested paradigm establishes a basis for creating intelligent, dependable, and context-sensitive industrial maintenance systems.
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.