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 Heat-treatment processes with data analytics
Sophisticated production processes

Improving quality in heat treatment processes with data analytics

In this case study, you will learn how Bosch applies data analytics to achieve unrivaled quality in coating processes.

Data analytics in heat treatment

Bosch’s top priority is achieving unrivaled quality in sophisticated production processes, such as heat treatment and hardening. To do so, Bosch applies its specialized data analytics expertise in its own factories.
The parameters that affect the quality of a heat treatment process are generally diverse and variable. Today, we use complex correlation analyses and other analytics methods based on vast amounts of process data to quickly identify cause-and-effect relationships (big data in manufacturing).
Applying predictive models in real time means you can directly intervene in the long-running process flows typically found in heat treatment, and therefore avoid waste and reduce failure costs.
This is how you can continuously improve quality and efficiency in heat treatment in the face of rising complexity.
What data analytics can do for heat treatment:

  • Analyze the relationship between system parameters and quality target figures.
  • Identify process parameters critical for quality.
  • Apply algorithms to real-time data.
  • Promptly detect deviations from the expected quality and avoid corresponding quality losses.

Sample project: how data analytics helps achieve top quality in coating processes at Bosch

The parameters crucial to the quality of hardening processes are diverse and variable. Today, when problems occur, complex correlation analyses are conducted.

Physical vapor deposition (PVD) coating Icon lens
Physical vapor deposition (PVD) coating

Background and objectives: optimize a PVD coating process by gaining insights into correlations among process and machine parameters.
Procedure and results: apply data analytics to create an algorithm in three steps that triggers a machine alert in the event of a deviation.
Methodology: how do you search huge volumes of data to find out which process parameters determine the quality of the results? The following three-step methodology has proven to be successful in a wide range of analytics projects. The final product is a clear set of features that have an unmistakable correlation to the process result.

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Benefits of data analytics at a glance

Clear identification of the process parameters that impact quality and of the previously unknown cause-and-effect relationships.
Major improvement in coating quality by applying the developed predictive models to real-time data.