With a feasibility study, we clarify before implementation whether and under which conditions your inspection task can be reliably automated. To do so, we examine real sample parts, test suitable cameras, lenses and lighting, and develop a first image-processing approach.
Not every inspection task can be solved with the same camera, lighting or software. That is why every THOPinspect project begins with a feasibility study. Together we examine your application under real conditions and develop the optimal vision solution – before you invest.
Depending on the outcome of the feasibility study, we recommend the suitable system – this may be a THOPinspect system, an individual integration, or a standard system from Keyence, Cognex, wenglor or another manufacturer if that is the most economical solution.
For integration into machines and special-purpose equipment.
Learn more →For retrofitting existing production lines.
Learn more →For fully automatic inline inspection.
Learn more →For individual requirements and integrations.
Learn more →A feasibility study makes sense whenever it needs to be established, before implementation, whether an optical inspection is technically reliable and economically viable. This applies in particular to small or hard-to-detect defects, varying surfaces and materials, multiple product variants or short cycle times.
It also provides a sound basis for decisions when selecting camera and lighting, when comparing rule-based image processing with AI, when replacing a manual visual inspection, and when retrofitting existing production lines.
At the end, the customer receives a documented assessment of the inspection task. This includes the test setup, meaningful example images, the lighting and image processing used, as well as the inspection results achieved.
On this basis, THOP provides a concrete recommendation for the next steps and, where required, a sound quotation for the vision system, the machine integration and the software.
To start, THOP needs representative good, defective and borderline sample parts as well as a description of the inspection task. Also important are known defect patterns, material and surface characteristics, product variants, the target cycle time and part positioning.
In addition, we clarify the installation situation, existing machine and control technology, interfaces, requirements for HMI and operation, as well as the desired documentation and traceability. The required details are captured in a structured way using a shared checklist.
The suitable technology is assessed on the basis of defect patterns, part variance, data availability, cycle time and the required inspection reliability. The aim is not to use AI for its own sake, but to arrive at a robust, understandable and maintainable solution.