This project is the first time senswork has implemented the new test equipment concept for highly integrated manual test stations. A high-resolution camera system measures pin positions of an MTD connector assembly, records connector coding and verifies housing dimensions within a tolerance range of 0.1 mm. A color dot spray system marks well-tested components.

Fakra connectors are used in automotive electronics for the transmission of signal data such as GSM, GPS or camera data. In order to meet the high quality requirements even in the fully automated production of high volumes, a leading supplier of these products relies on senswork‘s inspection technologies.

Enormous quantities of battery cases are produced every day in battery production for e-cars. Our camera system checks the battery case holder for possible damage, breaks and cases that have not been removed in order to be able to secure the subsequent fully automatic refitting.

Before individual components of an automotive assembly are assembled, they are charged with grease in an automated production line. Here it is important to ensure that the amount of lubricant is applied sufficiently and precisely.

With the manual mounting of assembly groups in particular, it often happens that individual components are missing or the precision of the assembly does not meet the specifications. An end-of-line test system from senswork checks the correct assembly and alignment of individual components.

Chocolate is difficult to describe completely with a rule-based algorithm, since no two chocolates are identical. The individual chocolates have a high natural range of variation.

The individual elements of a first aid kit must be identified through a transparent packaging. Since the packaging reflects light, the individual components of the packaging are difficult to recognize. ViDi Check inspects the complete first aid kit and determines whether all trained features are present.

Badly positioned, incorrect or bent components on a populated circuit board are difficult to detect with conventional image processing. Using Deep learning, classification and defect detection is much more reliable, even with a low even with a small amount of training data.

Before spark plugs are packed, they are inspected for completeness per layer. The space situation only permits the use of wide-angle optics, which leads to a strong distortion of the perspective. In this case, classic pattern searches can already reach their recognition limits. Using Deep Learning, however, the the inspection works very reliably.