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Key research themes
1. How do CAD/CAM integration and system architectures enhance precision and efficiency in Computer Aided Manufacturing?
This research area focuses on leveraging integrated CAD/CAM systems and advanced CAD/CAM architectures with novel geometrical representations and optimization algorithms to improve design productivity, manufacturing accuracy, and process efficiency. The work investigates how different CAD/CAM components—design, analysis, topology optimization, and manufacturing modules—can be unified to streamline additive manufacturing workflows, reduce errors typical of traditional boundary representations, and enable rapid production of optimized, manufacturable parts.
Key finding: Demonstrates that integrating CAD and CAM systems facilitates examining multiple design solutions and simulates manufacturing tasks effectively, leading to productivity improvements such as reduced design errors, better... Read more
Key finding: Introduces a novel CAD/CAM system using function representation (FRep) that overcomes limitations of boundary representation (BRep) in traditional CAD/CAM by guaranteeing geometric correctness, enhancing robustness during... Read more
Key finding: Presents a new STEP-NC based data model that extends the digital thread from CAD to machine control, enabling advanced, intelligent manufacturing for additive processes by integrating design, process planning, simulation, and... Read more
2. What machine learning and knowledge-based methods improve feature recognition and manufacturability assessment in additive manufacturing?
This research theme encompasses applying machine learning (ML) and knowledge-driven automated feature recognition from CAD models to assess and optimize manufacturability in additive manufacturing (AM). It addresses the challenges of interpreting complex geometries and predicting feasible buildability by embedding AM-specific knowledge into computational models. The approach leads to better design-validation cycles by quantifying manufacturability metrics related to geometric accuracy, structural integrity, and process efficiency without extensive trial-and-error printing.
Key finding: Proposes an automated methodology that recognizes key geometrical features relevant to AM manufacturability directly from CAD models (.stp format), embedding process knowledge to quantify manufacturability and guide design... Read more
Key finding: Reviews and identifies that machine learning techniques significantly enhance automated feature recognition and manufacturability analysis from CAD models by capturing complex parameter interactions and facilitating... Read more
3. How can additive manufacturing workflows be optimized for large-scale, customized production while addressing post-processing challenges?
This area investigates the methods to enhance additive manufacturing workflows to support rapid customization of large-scale products, including automation in design and manufacturing steps, and addresses post-process operations such as support removal. It explores process automation, design for AM (DfAM), model-based customization, and development of algorithms for efficient post-processing planning, improving scalability, cost-efficiency, and reducing manual labor in AM-based production.
Key finding: Develops a model-based methodology integrating design automation with user interfaces to support mass customization of large-scale plastic products through AM, demonstrating substantial improvements in design efficiency and... Read more
Key finding: Introduces a recursive algorithm to automatically generate optimal process plans for multi-axis machining-based support removal in AM parts to fracture contact regions efficiently, sequencing support component removals via... Read more
Key finding: Presents a system enabling open-ended, low-level programmatic control of FFF 3D printers from a creative coding environment, facilitating iterative material interaction and direct toolpath manipulation, which supports... Read more