Four new articles presented by our tolerancing group at the Conference on Computer-Aided Tolerancing 2026 have been published. “Mapping Uncertainties in Tolerancing: An Industrial Design-Centric Perspective” (Stefan Götz) presents a systematic method for identifying uncertainties in the tolerancing process. “Leveraging Physics-Informed Neural Networks for Efficient Tolerance Analysis” (Jan Kopatsch) demonstrates how physics-informed neural networks can significantly reduce the computation time of tolerance analysis while maintaining comparable accuracy. “Assembly Sequencing using Sparse Batch Variation Data: A Process-Oriented Tolerancing Approach” (Stephan Freitag) describes a process-oriented approach to improving assembly quality based on sparse measurement data. “Tolerancing in the Context of Industry 5.0: Comprehensive Overview and New Challenges” (Stefan Götz) provides an overview of new requirements, technologies, and challenges in tolerancing within the context of Industry 5.0.
https://doi.org/10.1016/j.procir.2026.03.116
https://doi.org/10.1016/j.procir.2026.03.130
https://doi.org/10.1016/j.procir.2026.03.142
https://doi.org/10.1016/j.procir.2026.03.145
Publication of Papers from the 2026 Conference on Computer-Aided Tolerancing
