Grant: Discovery Grants Program - Individual

The development level of a country can be represented by the quality of manufactured products, technology, and state of urban and industrial infrastructures, e.g., hydro towers and railroads. Increasing the products' geometric quality and maintaining the infrastructures in good condition conserve a top position for Canada among the most developed countries in technological and scientific advances of the 21st century. Meanwhile, the socio-economic importance of reliable infrastructure in people's lives and the significant effect of product quality on manufacturing companies' sustainability require deploying non-destructive evaluation (NDE). NDE provides tools to inspect for flaws, imperfections, malfunctions, and anomalies without damaging parts. In this regard, 3D geometric inspection aims to verify the manufactured products' conformity and monitor the infrastructures' geometric condition. Computer-aided inspection (CAI) applies 3D optical and laser scanners to digitize the geometric surface of parts and compare it with reference models to detect defects and geometric anomalies. Geometric inspection of non-rigid parts/large-scale structures (N-RP/L-SS), e.g., parts made of sheet metals in aircraft and vehicle fabrication, and metallic infrastructures, is largely performed in the industrial context. Due to the flexibility of N-RP/L-SS, the inspection is typically performed on physical fixtures to immobilize and constrain components in the required inspection position. Fixtures are robust and require low qualification, but they are dedicated to a single product inspection requiring modification and reconstruction for each change that imposes high costs. The long-term objective of the proposed program is to reduce the use of inspection fixtures by developing smart real-time fixtureless CAI of N-RP/L-SS under large deformation. Using advanced nonlinear modeling/simulation, robotized 3D scanners, optimization, and artificial intelligence tools, the geometric state of equipment/structures is monitored in real-time to detect defects of products in each manufacturing process to avoid processing a faulty part and eliminate poor quality assemblies. Meanwhile, it enables predicting undesired deformation and geometric anomalies of infrastructures to associate geometric changes with potential failures or breakdowns and raise the alarm before they occur. The performance and robustness of CAI methods will also be evaluated and analyzed in connection with the impact of measurement noise generated by 3D scanners. The originality of the proposal lies in 1) fixtureless CAI of N-RP/L-SS under large deformation using robotized 3D scanners for geometric data;2) the validation and verification of developed methods using statistical approaches;3) smart and automatic defect detection in real-time and geometric anomalies prediction;4) cost-effective method that increases the safety of inspection teams by removing direct manipulation of hazardous inspection situations.