Siemens Electronics Factory Erlangen Reduces Machine Learning Deployment Time by 80% with AWS and Siemens Industrial AI on Industrial Edge

Overview

Siemens Electronics Factory Erlangen, which manufactures PCBs and controllers such as SINAMICS converters and SINUMERIK CNC controllers, used computer vision models to spot anomalies in PCB assembly, but training and retraining ML models on premises was time-intensive and constrained by GPU bottlenecks. The factory adopted AWS services together with Siemens Industrial Edge and Siemens Industrial AI, sending shopfloor images via Edge applications to Amazon S3 before training via Amazon SageMaker or AWS Lambda; training results and edge prediction results are monitored through AI Model Monitor, with AI Model Manager providing central management of models on the shopfloor. This reduced time spent on model training and retraining by 80 percent (from about 30 minutes to roughly 5 minutes for retraining and deployment), cut costs by more than 90 percent compared to on-premises data storage systems, and reduced the false call rate by over 50 percent, while continuously preventing around 4 percent of PCB assembly errors compared to around 60 percent at peaks in the past.

The challenge

The factory's engineering team uses images as training data to create ML models to inspect and spot anomalies in PCBs during production. In the early days, engineers trained models locally on computers running on premises, but computing limitations with GPUs and a lack of elasticity became challenging, so the team needed a more flexible and faster way to train and retrain its ML models.

The solution

The factory adopted AWS services together with Siemens Industrial Edge and Siemens Industrial AI to cover the complete AI lifecycle from cloud training to shopfloor deployment: images from the shopfloor are sent via Edge applications to Amazon S3 before going through Amazon SageMaker or AWS Lambda for model training; training results from SageMaker and prediction results from the AI Inference Server on each Industrial Edge Device are sent to AI Model Monitor for observability; and AI Model Manager provides central management of models on the shopfloor.

Reported business value

The factory reduced time spent on model training and retraining by 80 percent — from about 30 minutes to roughly 5 minutes up to deployment — achieved cost savings of more than 90 percent compared to on-premises data storage systems, reduced its false call rate by more than 50 percent, and is continuously preventing around 4 percent of PCB assembly errors, down from around 60 percent at peaks in the past.