» TEST & QUALITY
» TEST & QUALITY ASSURANCE Source: Goepel electronic Integration of the AI advisor into a single production line The AI advisor assistance function utilised in the first stage (level 1) ultimately ensures that incorrect decisions are prevented during defect verification and that no detected defects are subsequently classified as ‘pass’. By constantly adding further data during operation and other training processes, the AI advisor becomes better and better at making classification decisions. At level 2, it has been trained to such an extent that all possible defects are reliably recognised, and verification can take place automatically. Integration of the AI advisor into several production lines Source: Goepel electronic The AI advisor makes decisions and classifies defects independently. Verification by the operator is then only necessary in exceptional situations, namely when the AI cannot make a safe classification decision. By enabling automated classification of almost all faults at level 2, the AI advisor significantly reduces an operator’s workload at a verification station. Good training as a basis Deep Learning (DL) can also be applied in other areas. In industrial applications of DL methods, it is essential to create a balanced and valid training database. This training database should also be constantly expanding during the system’s life cycle with new examples from ongoing production processes. As such, the AI advisor concept is flexible. Users can, on the one hand, start with a predefined database after installation, which is retrained during operation as it is supplemented with additional image data from the subsequent user decisions. The second option, however, is to develop a model without the use of a predefined database, and using only the classification decisions stored in the inspection system’s database. In this instance too, after initial training, image data attained from the relevant user decisions made throughout productive operation, are used to retrain the advisor. During operation, the AI software receives data from the inspection system, assigns it to the respective AI models, performs the inference and transmits its decisions to the verification software. 44 EPP Europe » 04 | 2023
Goepel’s AI software has been designed in such a way that models are created using failure images in a rule-based and thus completely autonomous manner. To ensure that a valid training basis is generated in this autonomous process and that it remains valid throughout the life cycle, various mechanisms have been integrated. These range from the monitoring or exclusion of image data too similar to that already present in the training set, to additional expert interviews (active learning) to train the system in individual new image data. The AI software ecosystem also includes an AI framework for the distributed training and delivery of Deep Learning models and their instances. This tool, which has a web-based, system-independent interface and can be accessed via a web browser, makes it possible to manage defect instances with the error images (samples) and their assignment (labels) or, if necessary, to delete samples from the training set. Integration into production AI solutions that are to be integrated into industrial production processes must fit seamlessly into the corresponding IT infrastructure. In many cases, production processes are completely sealed off from the outside world for security reasons, as a loss of sensitive data or manipulation of the production systems could cause enormous damage. Cloud solutions are often not possible and edge-based AI solutions are the only option. For this reason, the backend architecture of the AI advisor software covers all possible use cases. For electronics manufacturers who have only one production line, all AI software modules can be installed on the same PC as the verification station software. If, meanwhile, production consists of several lines, the AI can run on the company network on a separate AI PC. For users who do not want to install additional computing power, however, a cloud-based solution can also be provided. In the area of automated optical and X-ray inspection, AI can enable inspection systems to make better decisions or to simplify manual processes. It can also optimise production processes based on the data collected. Goepel electronic’s newly-developed AI advisor software module provides an AI-based assistance function for its PILOT Verify verification software, which ensures that incorrect decisions are prevented during failure verification and that no detected defects are subsequently classified as ‘pass’. Via the interaction of humans with AI, the classification process is optimised and the human workload lightened. As the AI model further develops, it becomes the decision maker, able to classify defects independently. Verification by the operator is only necessary in exceptional situations when the AI cannot provide a clear result. This automatic classification of almost all defects further reduces the operators’ workload. www.goepel.com Dr. Jörg Schambach is Product Manager Industrial Vision Solutions at Goepel electronic Philipp Drechsler is Senior Engineer Inspection Systems at Goepel electronic EPP Europe » 04 | 2023 45
