Monitoring and quality evaluation of production processes (machining processes, additive manufacturing processes, laser-based production processes)

Service description

AI-based monitoring and data analysis can improve the performance, quality and efficiency of manufacturing processes. This service supports in-process monitoring and evaluation of machining, additive manufacturing and laser-based production processes. It includes an initial analysis of the customer’s process, recommendation of suitable data acquisition (DAQ) systems and data-processing workflows, and subsequent evaluation of collected production data. Where appropriate, AI and machine-learning methods can be applied for quality assessment, anomaly detection and process control. The service helps identify process weaknesses and improvement opportunities before implementing automated quality assurance or feedback-control solutions.

Expected results:

Evaluation and validation of the monitored production process against agreed process and quality criteria

Identification of process weaknesses, anomalies and potential improvement opportunities

Assessment of the suitability of available data and monitoring infrastructure for AI-based quality assurance or process control

Recommendations for data acquisition, data processing and further implementation of AI-based monitoring or control methods

Technical report summarising the analysed process, applied methods, evaluation results, identified limitations and recommended improvements

Methodology:

Definition of customer requirements, production process and evaluation objectives

Identification of relevant process variables, quality indicators and available data sources

Customer provision of available production data, process documentation and information about existing sensors, DAQ systems and machine interfaces

Analysis of the existing monitoring and data-acquisition setup

Recommendation or configuration of suitable sensors and data-acquisition systems where required

Collection, preprocessing and analysis of production and sensor data

Real-time and/or historical data analysis using AI and machine-learning methods

Evaluation of process performance, quality indicators, anomalies and potential improvement opportunities

Where applicable, development or evaluation of AI-based quality assurance or process-control methods

Target:

Robot manufacturers, robotic cell providers and system integrators for manufacturing applications

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