Increased process safety and efficiently through Automated Defect Recognition (ADR) in X-ray inspection

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1 Increased process safety and efficiently through Automated Defect Recognition (ADR) in X-ray inspection More info about this article: Abstract Lennart Schulenburg 1 1 VisiConsult X-ray System & Solutions GmbH Brandenbrooker Weg Stockelsdorf Germany Tel ; fax l.schulenburg@visiconsult.de Especially automotive manufacturers with a very high production volume need interaction free in-line systems with Automated Defect Recognition (ADR). This reduces personnel costs and lowers the chance of human errors during the inspection process. Automated inspection systems have to comply to international quality standards like ASTM, VDI, EN etc. and the demanding company standards of VW, BMW, Porsche etc. As the production process of such parts is already completely automated, systems have to be designed for 24/7 operation without human interaction. Additionally, offline inspection stations are used to train the inspection sequences or review the results. This leads to a completely decoupled procedure for 100% system uptime. Typical ADR applications are the detection of porosities, inclusions or cracks in casting parts. It is possible to define certain ROIs and check defect metrics like defect density, defect distance, defect size, defects per area and many more. Thresholds can be defined dynamically. Training of the system does not require any programming skills and can be done through level II or III personnel. This drives down production costs and reduces the inspection bottleneck, while increasing the reliability and process safety. An integration to Industry 4.0 factory solutions allows full traceability of the inspection process down to single part level. More demanding tasks like automated measurement, completeness or density checks can be performed through the unique VAIP (VisiConsult Automated Image Processing) module. Complex test sequences can be performed on static images or in real time. Example applications: The behavior of heat pumps under different temperatures and completeness checks of valves. Creative Commons CC-BY-NC licence

2 Introduction Automation is probably the leading development in manufacturing over the last decades. The reasons for this are mainly industry or application dependent. Cost reduction or demand for increased process safety are the strongest drivers of this movement. Automating NDT processes has always been challenging due to compliance and liability issues. With growing processing power, Automated Defect Recognition (ADR) is a rising technique in the field of X-ray inspection. Automated systems need to be qualified in order to provide reliable and reproducible inspection results and have to comply towards industry standards. This paper will give a brief overview about techniques and applications. Defect recognition Typical applications for defect recognition are casting parts. The ADR software should automatically detect classify typical casting defects like porosities and inclusions. Figure 1 shows the result of such a classification. On the left side is the evaluated part and defect list, while on the right one can see the original X-ray image. Different parts of the image allow different error thresholds. This can be defined by the Level III through inspection Region of Interests (ROI). Depending on the inspection guideline criteria for classification can be calculated defect depth (contrast), distance to surface or defect size or defects per area. Figure 1: Result of an automated defect recognition (ADR) As the ADR evaluation works on the original bit image it can operate in all different part thicknesses and detect even slightest defects. As humans can only perceive less than 100 grey values, only limited thicknesses can be evaluated at the same time without window leveling. This gives automatic evaluation a big advantage for complex parts like structured castings. Figure 2 shows the evaluation per ROI. For this part only indication inside the green ROI (center) are relevant and lead to a rejected part. All findings on the outside are omited. 2

3 Figure 2: ADR Evaluation of a ROI Geometric evaluations Even though most applications in NDT are looking for typical defects in produced parts it might be important to check for geometry, alignment, completeness or other aspects inside complex parts. Dedicated ADR software allows to perform these checks automatically. Figure 3 shows automatic evaluation of a flash-bang grenade. Checks performed are presence of the safety pin, length of the fuse delay, homogeneity of the explosive material, alignment of internal structures and much more. X-ray is the only technique allowing to perform these checks in a non-destructive manner. Figure 3: Automatic geometric measurements on a grenade 3

4 Another application could be the inspection of high voltage fuses as seen in figure 4. For exact fitting, they need to have a matching geometry without deviations and the right distance of the internal springs. All these checks can be carried out within seconds by a system seen in figure 5. Figure 4: Measurement and geometry check of high voltage fuses Figure 5: Example of an inline palette based system. The last application that should be presented is the evaluation of airbag igniters, which require a very demanding inspection. Humans regularly failed on this task and the manufacturer had to implement a procedure to increase the process safety. As a side effect the efficiency went up by a huge amount. Figure 6 shows the procedure to check to perform a best-fit evaluation of the curved section and the check of porosities in critical areas. 4

5 Figure 6: Different checks on an airbag igniter Part handling Apart from the challenges on the software side automation systems need to have a reliable design and high precision. As these systems are developed for 24/7 unmanned inspection every system halt will reduce the throughput and therefore it s efficiency. A broad variety of different handling concepts can be used for this task. Figure 5 already shows one possibility using industrial palettes. Other options are the use of conveyor belts, turntables, or robots. Figure 7 shows typical robot systems that provide the highest accuracy and efficiency. Figure 7: High end robot system for inline X-ray inspection 5

6 Conclusion This paper shows the different applications and use cases for automated systems and ADR in X-ray. The presentation will go into details regarding the feasibility of such a concept. Due to the high amount of parameterization required for an ADR it is only applicable for high volume inspection. Figure 8 shows a typical Return of Invest (ROI) curve for an automated system compared towards a universal DR system or even film. It is also required to distinguish between automated test sequences (ATS) and a completely automated evaluation. Figure 8: ROI on different system types 6