1. 3D geometric inspection and predictive monitoring of non-rigid parts
Product and structure quality significantly affect manufacturing companies' sustainability by deploying non-destructive evaluation (NDE). NDE provides tools to inspect for flaws, imperfections, malfunctions, and anomalies without damaging parts. The 3D geometric inspection aims to verify the manufactured products' conformity and monitor structures' 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. As a result, 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. This project aims 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 concerning the impact of measurement noise generated by 3D scanners. The project comprises fixtureless CAI of N-RP/L-SS under large deformation using robotized 3D scanners for geometric data, validation and verification of developed methods, smart and automatic defect detection in real-time, and geometric anomalies prediction.
Research area, student roles & skills
Research area: A specialist in intelligent manufacturing, Dr. Sattarpanah Karganroudi conducts research based on strategies specific to Industry 4.0, particularly non-destructive evaluation and 3D metrology of mechanical components and structures. Also involved in R&D, he has used experimental approaches to develop engineering, precision, and materials processing methods and laser welding. Computer-aided design, manufacturing and inspection, optimization processes, finite element analysis, augmented reality, and digital simulation are frequently used in his work.
Student roles:
First and foremost, the student is expected to possess a solid understanding of the principles and concepts related to 3D geometric inspection and predictive monitoring. This includes knowledge of geometric modeling, computer vision, machine learning, and data analysis techniques. The student should be able to apply this knowledge to design and implement algorithms and methodologies for the inspection and monitoring of non-rigid parts.
The student will be involved in collecting and analyzing data from various sources such as sensors, cameras, or 3D scanners. This requires proficiency in data acquisition techniques, data preprocessing, and data fusion methods. The student will need to develop skills in handling large datasets, managing data quality, and extracting relevant features for inspection and monitoring purposes.
Additionally, the student will be responsible for designing and conducting experiments to validate the proposed algorithms and methodologies. This involves setting up experimental protocols, selecting appropriate test cases, and performing statistical analysis on the results. The student should possess strong analytical and problem-solving skills to interpret the experimental findings and draw meaningful conclusions.
Moreover, the student is expected to stay updated with the latest research and technological advancements in the field of 3D geometric inspection and predictive monitoring. This involves conducting literature reviews, attending conferences or workshops, and collaborating with other researchers in the field. The student should proactively identify research gaps and propose innovative solutions to address them.
Communication skills are vital for the student's role as they will be required to present their findings through research papers, conference presentations, or technical reports. Clear and effective communication of the research outcomes is crucial for sharing knowledge, obtaining feedback, and fostering collaboration with other researchers.
Skills required:
The subjects require a knowledge of 3D geometric metrology and computer-aided inspection, finite element analysis (FEA), and strong interest, knowledge and skills in computer programming, numerical and data analysis.