GermanyManufacturing2019 – ongoingSiemens
AI quality prediction at the Amberg electronics plant
Siemens' Amberg plant predicted solder defects from process data and avoided an additional X-ray machine
Problem
The Siemens Electronics Works in Amberg makes about 17 million Simatic control products a year in more than 1,000 variants. Automated X-ray inspection of solder joints on circuit boards had become the bottleneck of the line, and a second X-ray machine would have cost about €500,000.
Approach
Plant engineers trained a model on process parameters from the soldering line. Running on an edge device on the shop floor, it predicts whether a board's solder joints are free of faults and so whether the board needs the X-ray test at all. The plant uses similar models elsewhere, for example to warn of a milling-spindle failure 12 to 36 hours in advance.
Results
Takeaway
The model selected units for the slow inspection step, avoiding an equipment purchase. Siemens did not quantify the reduction in X-ray inspections; the 40% reduction in testing and downtime from prediction models reported by ROI in 2019 has not been independently verified.
Categories
- Size
- Large enterprise
- Industry
- Manufacturing
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Built in-house
- Evidence
- Company's own claim
Sources
Source summaries
Siemens reports a quality level of 99.9990% at Amberg and annual output of about 17 million Simatic products in more than 1,000 variants. It describes replacing a planned €500,000 X-ray machine with an AI model, trained on process data and run on an edge device, that predicts whether solder joints are fault-free. It also describes a spindle model that warns 12 to 36 hours before a failure, without giving overall savings.
The consultancy ROI ran the Industry 4.0 Award that Amberg won in 2018. ROI reports a production quality of 99.999% and output of up to 16 million products a year, and says prediction models already flag process deviations early and have cut testing and downtime by 40%. It describes AI-based pattern recognition as a next step.
Full details are in the original sources.
