CIRP CAT 2026
Welcome
College International pour la Recherche en Productique (CIRP) and the University of Alberta welcome you to the 19th CIRP Conference on Computer Aided Tolerancing which will be held from 15th-17th June 2026 in Edmonton, Canada
Scientific Themes
Digital twin for geometric quality
Quality control & production metrology
Sensor-fusion enabled single and multi-sensor in-situ modelling and measurement of geometrical deviations
Robust design in geometry assurance
Machine learning for production metrology
Tolerance analysis and variation simulation
Tolerances for freeform geometries
Tolerancing through NDT methods
Tolerancing for additive and hybrid manufacturing processes
Data-driven tolerance analysis/assembly simulation
Tolerances for micro-structured geometries
Tolerance design
Uncertainty in geometrical metrology
Specification and standardization
Multiphysics-based tolerancing of advanced manufacturing processes
The Venue
University of Alberta
Edmonton, AB, Canada
The University of Alberta, located in Edmonton, Canada, is one of the country’s leading research institutions, renowned for its contributions to engineering, science, and innovation. Edmonton, the capital of Alberta, is a vibrant city known for its rich cultural scene, beautiful river valley, and technological advancements. As a hub for academia and industry collaboration, it provides an inspiring setting for the CIRP conference
Keynote Speakers
The 19th CIRP CAT Conference brings together leading experts to bridge the gap between theoretical research and practical application. Meet the keynote speakers who are driving the latest technical advancements in the field.
Dr. Qiang Huang
Epstein Department of Systems and Industrial Engineering
University of Southern California
Dr. Qiang Huang is a Professor at the Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles. His research, detailed in his monograph "Domain-informed Machine Learning for Smart Manufacturing", has been focusing on machine learning for smart manufacturing and quality control for personalized manufacturing. He is an IISE Fellow, ASME Fellow, and a senior member of US National Academy of Inventors. He holds eight patents related to quality control in additive manufacturing. He has served as the Senior Editor for IEEE Transactions on Automation Science and Engineering and the General Chair of 2025 IEEE International Conference on Automation Science and Engineering (CASE 2025).
Title: Automated Geometric Qualification of 3D-Printed Products
Geometric qualification of a product is typically performed by specifying features or regions of interest (ROIs) during design, conducting shape registration to establish correspondence between the inspected product and its design counterpart, and measuring discrepancies for compliance assessment. For complex freeform products, the qualification often requires human intervention to ensure accuracy, particularly in personalized manufacturing through 3D printing. However, geometric variety and complexity can induce operator-to-operator variability due to heterogeneous spatial distributions of geometric distortions. To enable automated product qualification, we propose to specify ROIs as surface patches defined by geometric descriptors indicative of intrinsic deviation patterns. Utilizing these descriptors, ROI specification via shape space dimension reduction, non-rigid intrinsic shape registration, and intrinsic deviation representation can therefore be conducted automatically for product qualification. Finite types of ROIs or surface patches can be extracted based on their intrinsic deviation patterns, independent of covariates such as size and location. A software demo has been developed to implement the qualification process.
Dr. Stefan Goetz is a senior researcher and lecturer at the Chair of Engineering Design at Friedrich-Alexander-Universität Erlangen–Nürnberg (FAU), Germany, where he leads research in digital engineering and tolerance management. His work focuses on computer-aided tolerancing, geometry assurance, and uncertainty-aware virtual product development, with particular emphasis on digital twins, simulation-based tolerancing, MBSE, and AI-supported decision-making. He received his PhD in 2022 with a dissertation in the field of early integrated tolerancing and has published on tolerancing frameworks, uncertainty modeling, and data-driven methods. Dr. Goetz currently leads the Design Society Special Interest Group on Engineering Design for Robustness.
Friedrich-Alexander-Universität Erlangen–Nürnberg
Dr. Stefan Goetz
Title: Tolerancing in the Context of Industry 5.0: Comprehensive Overview and New Challenges
Tolerancing decisions strongly influence functional performance, quality, and reliability of technical systems and must consider diverse sources of variation and uncertainty. Over recent decades, tolerancing has evolved from primarily design-centric specification toward computer-aided and data-supported approaches that increasingly connect design, manufacturing, and inspection. In parallel, the emerging Industry 5.0 paradigm expands the perspective beyond manufacturing efficiency toward sustainability, resilience, and human-centric decision-making across the full product lifecycle. Thus, this paper synthesizes (i) the characteristics of tolerancing in the Industry 5.0 era, (ii) enabling technologies – particularly digital twins and AI-based modeling, (iii) key potentials such as integrated quality–cost–sustainability–risk trade-offs and circular strategies, and (iv) major challenges, including increasing model complexity, real-time feasibility, as well as trustworthy and explainable modeling for AI-supported and self-regulating cyber-physical systems.