Resilient way of acquiring, transferring, synchronizing and preprocessing data is a mandatory prerequisite for all real-world intelligent solutions. In industrial setting they come in various shapes and forms. Data can be generated by both humans and machines and can vary from distributed, low level sensor data all the way to the records from centralized company level information systems. It is often a non-trivial and tedious task and hence a typical reason for failing in the solution deployment and operation.
We provided a forestry company with an automatic raw material processing QA system that monitors the material quality in production visually, using AI based and classical machine vision algorithms. The solution can identify raw material features to qualify for production process, identify quality problems on different production phases, and perform quality check on the final product. We analyzed the production process and proposed the overall solution, starting from sensor and hardware selection and placement for optimal operation, all the way to the machine vision software implementation, testing, integration, and deployment.