Bolt Preload Unlocked: From Mechanistic Models to Gaussian Processes

In this episode we dive into a research paper that tackles predicting bolt preload—the clamping force when you tighten a bolt—by marrying a physics-based mechanism model with data‑driven Gaussian process regression. We explore how friction, thread geometry, and torque variability complicate predictions, how parameter sensitivity analysis pinpoints the key factors, and how the authors built software that provides engineers with both a predicted preload and a confidence interval. With real‑world data and applications to safety‑critical joints (bridges, aircraft, cars), we discuss the impact of this approach and what it could mean for other industries. Part two is coming up next, where we go deeper into the advanced concepts.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

Sponsored by Embersilk LLC