• Results
  • Demonstrator

SIEMENS SIMATIC Products Quality Improvements


At Amberg, Siemens’ Digital Factory Division is manufacturing its SIMATIC products. Core of the manufacturing process is the production of the circuit boards, which are later on assembled with the housing parts to form the final product.

The overall objective is the introduction of AQ control loops for testing improvement and thus for overall production efficiency, while at the same time further raising the quality rate of finished products. This shall be achieved by implementing a closed control loop approach via the deep analysis of process data and the implementation of digital twins for products and production. Data from control loops throughout the manufacturing lines are to be collected and analysed via data mining and machine learning techniques, allowing to systematically identifying faulty products and the respective failure causes, providing the base for the intended improvements. By means of quality forecasting and simulation, (production) control decisions shall be derived at an early stage if quality deviations occur, thus avoiding further added value in ok products.

Current state: In the considered production line, electronic components are manufactured automatically and tested for functional defects using multiple test stations, including optical inspections, inner circuit tests and X-ray machine. Significant process steps are the solder paste printing of the circuit board, the placement of the electronic elements and the (reflow) heating process (compare Figure 11) before assembly. For ensuring high product quality, several quality tests are integrated into the manufacturing line. Today, each of these quality tests is conducted in order to test the result of the previous manufacturing steps. From the overall setup of the manufacturing environment, ICT and X-Ray testing are placed in a testing area, decoupled from the actual manufacturing lines. The X-ray test has a comparatively high process time, is carried out as a batch process and represents the line bottleneck, since currently a 100%-testing is conducted. Since the purchasing of further X-ray machines is associated with high costs, other solutions and approaches have to be generated.

Future state: Current activities performed in the factory in Amberg are a collection of product and test data, storage and management of these data for quality scouting, with web service and graphical report extraction. Specifically, for this trial, the focus is on one specific manufacturing line including solder printing, placement of electronic components and reflow soldering. Within this manufacturing line several test stations

More information & hyperlinks
Country: DE
Address: Werner-von-Siemens-Stra├če 48-52, Amberg 92224
Geographical location(s)
Structured mapping
Unfold all
Fold all

Concerning the ecological and economic operation of a factory, data analytics tools in combination with simulation approaches can contribute to improved throughput, bottleneck-reduction, or both for the production line. Through the optimization of the processes, production execution on organization and logistic level can be optimized by reducing the amount of material within the system, the lead times, or both.


improve false positive rate by 20%.  Measured as false positives rate, actual value is considered confidential.


For this trial, the acquired test data will be analyzed regarding quality classification. In every test a part could pass or fail. Failed parts must be reworked, if possible, and brought back to the process. Sometimes parts are classified as failed even if they are good (false positive). This effect will be analyzed by machine learning algorithms and, if necessary, adopted in classification parameterisation. Additionally, the fact of 100% testing, means every panel is tested automatically, with bottleneck in out of the line test stations will be addressed in setting up failure prediction models for quality forecast. This will be supported by data analysis of pre reflow AOI (automated optical inspection). 

With all these data analysis and process optimization activities economical evaluation will be included to support decisions in-process and configuration changes. For the development of these applications, the main steps are data availability/access, data processing, and model development. The developed applications should be deployed on Edge devices.


Through innovative algorithms and statistical methods, possible data sources for predictive quality control can be identified and evaluated. Moreover, by cooperation of all project partners, the realization of data access and acquisition along the whole process chain can be realized. With a focus on algorithms and methodology, a use case-specific algorithm is going to be implemented and validated to maintain high prediction accuracy.

Data availability is a challenge: Limited access to measurement data (due to limited access to third-party systems)


By applying sophisticated algorithms and methods on the acquired data, systematic failure root cause detection supported by data analytics can be implemented. In addition, improved knowledge of machine states/maintenance requirements for neuralgic points can be implemented through the desired solution path within this pilot.


The production line in Amberg has a highly automated process with several test stations along the path.


The Amberg production line collects all the process and test data an a quality management system data base. This allows reporting and production KPI analysis as well as supply chain management.


Analysing the test results the production process can be adapted and optimized.


Analyzing the test and process data, specific machine parameters can be adapted and optimized.


With the quality management platform, optimization of production is enabled.

This items serves as a filter in support of selecting the case and demonstrators associated to the Digital Transformation Pathway Cases Catalogue (see ConnectedFactories Coordination and Support Action - Information sharing and analysis)