Fraunhofer Solution Days: AI-based software tool for automated quality control
Fraunhofer IDMT has developed a software tool for quality inspectors based on Artificial Intelligence (AI), which automates and simplifies the analysis of industrial sounds, for example in welding processes. Thanks to pre-built AI models, established methods for audio analysis and application-specific sensor data, companies can use it to improve their quality control decisively. Even without AI expert knowledge, statements can be made quickly and reliably regarding the quality of products and manufacturing processes during production and in the final quality inspection. The Fraunhofer Institute for Digital Media Technology IDMT will be presenting the new software at its virtual booth in the "Plant and Mechanical Engineering" area of the first virtual Fraunhofer trade fair, the "Fraunhofer Solution Days 2020".
Talking about joining methods such as welding, an important part of the weld seam inspection has so far been the experienced welder himself, who can assess the quality of the weld seam by just listening to process-typical sounds. With the increasing automation of their production facilities, companies are increasingly relying on smart inspection software for quality control of manufacturing processes and industrial products. By using AI, more precise analysis results are expected and a faster, more efficient and more cost-effective quality assurance is the main goal. However, how can the expert knowledge for the automated analysis of industrial sounds be transferred to the specific application in the company where AI actually brings the desired added value?
Using pre-trained AI models for own analyses
This is possible with the software "Industrial Sound Analysis for Automated Quality Control", ISAAC, developed by Fraunhofer IDMT. Quality inspectors can use the pre-trained AI models from the field of acoustic sensor data analysis to adapt them to their own measurement data. This does not require in-depth knowledge of machine learning methods. Users import their sensor data easily and securely into the software. With an intuitive interface, users can then try out, edit, and apply pre-built AI models included in the framework to their own acoustic test tasks.
Depending on the application, the well-arranged data view provides an overview of the sensor data involved in the analysis and helps with further analysis settings. The BMBF-funded research project "AkoS - Acoustic inspection of weld seams of safety-critical components as part of quality assurance" is currently investigating the extent to which information from acoustic sensors can be used for quality assurance purposes applying machine-learning methods. The research results obtained here are directly incorporated into the further development of ISAAC.
Direct integration into existing test software
Users can directly integrate ISAAC into the company-specific testing software. In addition to the analysis of acoustic data, information of other sensors, such as engine current data or optical sensor data, can also be included in the analysis. In the institute’s current research activities one further task is to compress the self-trained algorithms where they fit on mobile devices with less storage and computing power but still deliver reliable results at the same performance. For further development of the new software tool, Fraunhofer IDMT is looking for interested industry partners.
Technical information on ISAAC
- Import of acoustic time signals in various formats, e.g. as wave-file
- Configuration of specific extraction and model parameters
- Automatic pre-selection of suitable audio analysis tools and AI models
- Export of the created models for test hardware and software
- Detailed analysis results
- Hosted by Fraunhofer IDMT with secure data location
- Server software for internal operation
Register free of charge for the Fraunhofer Solution Days and learn how ISAAC improves quality control in manufacturing at the virtual booth of Fraunhofer IDMT. You will find our booth in the topic area "Plant and Mechanical Engineering".