When industrial manufacturing processes are audible, the quality of these processes can be determined by their sound characteristics. The experienced machine operator hears whether a manufacturing process is successful and is therefore an important part of quality control. However, increasing automation is creating new challenges for quality management in industry. Inspection tasks are becoming more complex and trained personnel are less in demand. How can manufacturing companies react to the advancing automation of their quality control without overloading employees? And how much AI expertise is needed to apply automated quality analysis and interpret the results correctly? Many industrial plants face exactly these questions.
IDMT-ISAAC - Industrial Sound Analysis for Automated Quality Control
With IDMT-ISAAC, Fraunhofer IDMT offers a tool for the automated analysis of industrial sounds using AI. The software framework contains pre-trained AI models and methods based on the analysis of acoustic sensor data. Even users without AI expert knowledge can use this audio analysis tool to achieve fast and reliable results for their quality assurance and to make prompter decisions regarding quality assessment.
IDMT-ISAAC is accessible through a browser and the acoustic sensor data chosen for analysis can be imported, simply and securely. Users can try out the pre-built AI models included in the framework, edit them via an intuitive interface and apply them to their own acoustic inspection tasks. Depending on the application, the well-structured dashboard provides an overview of your live data as well as incoming sensor data and helps with further analyses.