Efficient Geometry Inspection and Off-line Programming
Volvo Cars is now implementing a new process and software support based on RD&T and IPS for Inspection Preparation and Automatic Off-line Programming of CMMs. The return of investment will be faster inspection preparation, programming and improved measurement equipment utilization. The implementation is based on validated research results from FCC, Wingquist Laboratory at Chalmers, SWEREA IVF, Volvo Cars, and Saab Automobile within the VINNOVAs MERA program.
Since variation is inherent in all production processes, consistent efforts in styling, design, verification and production aiming at less geometrical variation in assembled products, is a key to shortening development time of new products, as well as for choosing an efficient and resource-economic production process. The activities aiming at controlling geometrical variation throughout the whole product realization process are called the geometry assurance process. The figure shows a general model for product realization consisting of a concept phase, a verification phase and a production phase. In the concept phase the product and the production concept are developed. Product concepts are analyzed and optimized to withstand the effect of manufacturing variation and tested virtually against available production data often based on carry over type of inspection. In this phase, the concept is optimized with respect to robustness and verified against assumed production system by statistical tolerance analysis.
The visual appearance of the product is optimized and product tolerances are allocated down to part level. In the verification and pre-production phase the product and the production system is physically tested and verified. Adjustments are made to both product and production system to adjust errors and prepare for full production. In this phase inspection preparation takes place. This is the activity when all inspection strategies and inspection rules are decided. In the production phase all production process adjustments are completed and the product is in full production. Focus in this phase is to control production and to detect and correct errors by analyzing inspection data.
As we can see, it is necessary to feed the geometry assurance process with reliable inspection data in all phases which makes the inspection preparation and measuring an extensive and important activity. At Volvo Cars a new vehicle program is inspected with typically 700 inspection programs containing up to 25 000 features.
The inspection preparation contains three steps; (i) the inspection task is defined by breaking down product and process requirements to geometrical inspection features, e.g., a hole or a slot, on part and subassembly level, (ii) the inspection rules defines how a feature should be measured, i.e., number of points, local measuring coordinate systems, and allowed probe configurations, (iii) the final step is to program the motions and sequence of the Coordinate Measurement Machines (CMMs) that performs the actual measurement.
The automatic CMM programming contains three main math based algorithms for motion planning and combinatorial optimization. The first step is a feature accessibility analysis to find a set of probe configurations of minimum size that can reach all inspection points with collision free CMM configurations. This can be done by solving a binary LP problem.
The next technology used is Path Planning where the collision free CMM motions are generated by automatically finding via points and probe reorientations between the inspection features. Complete path planning algorithms, which always find a solution or determine that none exist, are of little industrial relevance since they are too slow. In fact, the complexity of the problem has proven to be PSPACE-hard for polyhedral object with polyhedral obstacles. Therefore, sampling based techniques trading completeness for speed and simplicity is the choice. Inspired by both the two most popular probabilistic methods FCC has since 2003 developed a novel deterministic path planning algorithm implemented in the IPS software.
© 2013 Fraunhofer - Chalmers Centre