Research Projects

The development of an accurate, reliable, and cost-effective non-destructive technique for the quality assessment of composite claddings is crucial. Laser cladding of composite coatings is an advanced material processing technology that has gained importance for hard surface coatings with customized surface properties. However, the deposition process of multi-layer composite cladding is complex and expensive, leading to non-homogeneous or hierarchical microstructures. Detecting and preventing defects during clad depositions is necessary for efficiency and cost-effectiveness. An intelligent cladding process monitoring system is proposed, integrating a multi-sensor system with artificial intelligence approaches to assess deposition quality in real-time. The system would consist of IR pyrometers, a thermal imaging camera, and acoustic emission sensors. Artificial intelligence approaches, such as machine learning and deep learning algorithms, will be used for defect detection, classification, and characterization, as well as prediction and optimization of process performance. The study consists of five objectives: studying possible microstructural changes and defects formation as a consequence of heterogeneous and repeated thermal cycles, evaluating the viability of implementing various processing strategies, establishing a multi-sensor system for online monitoring of melt pool dynamics, classifying defects and characterization of their evolution, and developing an intelligent AI-based trained system for defect detection, classification, and characterization. strategic guidelines for processing defect-free multi-layer composite claddings with homogeneous microstructure, hardness, improved abrasive and corrosion resistance will be derived.