Dimensional Variation in Automotive Body Assembly Student: Timothy Ian Matuszyk Academic supervisory panel: Prof. Michael Cardew-Hall Dr. Bernard F. Rolfe Dr. Paul Compston Funding: Australian Research Council Linkage Grant (#LP ) Industry Partner: Ford of Australia

Territory front-cross #10922 Front Cross member Front Cross member & Fender Front Cross / Fender / Hood

Improving manufacturing processes “In the future sustainable competitive advantage will depend more on new process technologies and less on new product technologies” (Thurow 1992)

Continuous quality improvement benefits Higher quality assemblies, Less warranty concerns, Reduced launch time

Rigid vs Non-rigid assembly Takezawa (1980) first showed that the additive theorem of variance does not hold for non-rigid assemblies, and that variation was in fact absorbable. Rigid assemblyNon-rigid assembly h2 h1 H

Assembly x 9 Component D x 9 Component C x 9 Component A1/A2 x 9 Component B1/B2 x 9 Observe and compare variation levels in components & assembly (9 samples) 38 points & 22 holes measured in final assembly Initial study

Industry study findings Looked at production assembly issues & identified areas of investigation, which included: Cases of variation levels decreasing over the assembly process Consistent positional shifts of holes from components to assembly Lower Variation Higher Variation

FE Assembly models A way of simulating process variation stack-up. Linear models are fast but lack accuracy. Non-linear models are more accurate but are slow and suffer from convergence issues. Thermo-mechanical approaches add even more complexity.

New data analysis possibilities Optical co-ordinate measuring machines have allowed for quick and detailed inspection. Shape characterization Regression modelling of responses Machine learning to deal with large data sets.

Aims This project aims to identify: How component variation propagates through an assembly process Which process changes can reduce overall variability in assemblies

Experimental vs FEM Actual process provides the best data Rapid prototyping Easy dimensional inspection ADVANTAGES Time consuming Resources Model assumptions = less accuracy DISADVANTAGES

How does part variation translate to assembly variation? Assembly shape? Bow Bow and Spring-back Twist

Do different processes affect final assembly variability? Comparison of final assembly shapes for 3 different clamp sequences given the same input part variability (bow in the hat).

Data reduction and patterns Component shapes Assembly shapes

Key steps 1. Understanding/modelling variation transmission. 2. Structured experimentation to identify the variation of alternative processes. 3. Classifying component shapes into groups that share the same optimal process.

An adaptable assembly process Imagine a process that can measure input components and select the optimal assembly approach for minimized variability in the final assembly. In-line OCMM