VDA 5 Part 1 Traceable Inline Measuring Technology

About This Publication

Given that a new edition of VDA Volume 5 “Measurement and Inspection Processes” was published in July 2021, the obvious next step was to review the “supplementary” VDA Volume 5.1 as well. As a result of the complete revision, the volume is no longer limited to geometric metrology in car body manufacturing. Many examples are still explained based on the latter to ensure that they are easily understandable. However, the methodology can generally also be applied to other areas within the scope of inline measuring technology. There has been a trend whereby measurements are increasingly taken by means of traceable inline measuring systems rather than offline in measuring rooms. Proofs of capability for inline measurement processes in accordance with VDA Volume 5 are thus becoming more and more significant. In practice, the “traditional” use of measuring rooms is fading into the background. Nevertheless, having measurement options that are independent of the production line is still helpful, especially when conducting analyses (e.g., when there are process variations). As was the case in the revision of the main volume, our focus during the revision of VDA Volume 5.1 was on the clarity of the methodology to ensure that it is easily applicable in practice. Based on an eight-step model, inline measuring systems were considered from a holistic perspective, from inspection process planning to the end of use. The proofs of capability thus obtained are part of the system approval. However, the latter also comprises further aspects that have not been considered in VDA Volume 5.1 (e.g., occupational safety). Moving beyond the focus of VDA Volume 5.1, measurement data is being collected based on significantly larger sample sizes because of the above-mentioned trend, leading to substantially larger amounts of data regarding process variation. Thus, developing and using intelligent evaluation methods or systems to adapt reaction speed to the increased amount of data is recommended.