AIDEAS
Use Suite

The AI-enabled quality monitoring tool for built machinery aims to provide valuable features for monitoring and ensuring the quality of manufactured products. It supports various standardized data modalities used in industrial product inspection across different manufacturing domains.

One of the key features of the tool is the AI-supported 3D analysis and comparison tool, known as ZG3D, developed by ITI. This tool enables the detection of differences between an expected or ideal 3D product model and the actual 3D model captured from the produced object. These differences can include geometric tolerances, shape deformations, and texture defects such as voids, cracks, or other surface anomalies. By comparing the captured model with the expected model, the tool can identify and highlight any discrepancies, allowing for effective quality monitoring and analysis.

In addition to 3D analysis, the tool also supports visual surface anomaly detection in 2D. This is achieved through unsupervised approaches developed by XLAB, which eliminate the need for laborious data collection and labeling of anomalous samples. Instead, the algorithms can be trained using defect-free samples, enabling straightforward adaptability to new product lines right from the start. This approach streamlines the process of detecting surface anomalies and ensures flexibility in monitoring product quality across different manufacturing scenarios.

This tool determines the capabilities of components by analyzing their features using AI regression algorithms from AIAD. It then combines these capabilities to derive the overall capabilities of the entire machine. The machine’s capabilities are subsequently aligned, to the best possible extent, through AI-based adaptive control, with the primary process parameters within the AIDEAS Adaptive Controller. Achieving this necessitates the implementation of Machine Learning Control techniques, enabling the adaptation of machine control to the assessed conditions using Reinforcement Learning Control. The controller will be trained against a machine model constructed from measurement data to identify a control solution that adheres to the limitations imposed by the process parameters.

The utilization of AIMC methodology will be employed by this tool for comprehensive machine anomaly detection, with a focus on evaluating component conditions. Relevant variables associated with key component features will be identified, followed by the extraction of these features from measurement data using knowledge-based processing and AI regression techniques for component condition evaluation. The collective component features will then undergo AI clustering techniques during training and AI classification techniques during implementation to identify machine anomalies. This will enable the AIDEAS Condition Evaluator to accurately assess the condition of individual components and the overall impact on machine performance in terms of anomalies within the context of AIAD.

The AI-based approach employed in this tool, as described in AIMC, enables comprehensive detection of machine anomalies by evaluating component conditions. Relevant variables associated with key component features are identified and extracted from measurement data using a combination of knowledge-based processing and AI regression techniques. Through the utilization of AI clustering during training and AI classification during operation, the aggregated component features are analyzed to detect machine anomalies effectively. As a result, the AIDEAS Condition Evaluator provides precise assessments of component-level conditions, while the implications of these anomalies on the overall machine performance are evaluated within the AIAD framework.

The purpose of this tool is to assist the end user of the machine during the initial calibration process at the customer’s factory, tailored to meet the specific requirements of each customer and factory using AI techniques. Calibration is a crucial step for every machine upon its arrival at the customer’s premises, posing challenges for both the machine provider and the end user. To address this, the proposed solution leverages AI to facilitate the initial calibration process.

The primary goal is to employ a supervised learning approach that learns from experienced users and identifies the most suitable calibration parameters based on the specific processing needs. By doing so, this tool will offer calibration capabilities to all AIDEAS pilots, ensuring accurate and efficient machine calibration.

The development of the Machine Passport for storing and sharing manufacturing data throughout the product life phases is a crucial aspect of ensuring efficient data integration, sharing, and exchange in the industry. The MP aims to establish data exchange protocols, standards, and interfaces that facilitate seamless communication between various computer-aided systems and manufacturing stages.