Top quality in coating processes

at Robert Bosch GmbH

Data analytics in process engineering

www.bosch-si.com

Software Innovations bosch-si.com

Data analytics in process engineering

Top quality in coating processes at Robert Bosch

The parameters that affect the quality of a sophisticated production process (e.g. PVD and CVD techniques, heat treatment) are so diverse and variable that expert experience alone is not enough to quickly identify and eliminate the root causes of problems. For a long time, years of operational experience and an intuitive grasp of correlations were used in finding problem causes. Today, however, production experts also have access to complex correlation analyses based on objectively collected data, which in day-to-day operations can be used as a core component of the problem-solving process.

To use available data in the most effective and efficient way, Bosch draws on its data science expertise in manufacturing. The company offers support to both users and providers – whether in process engineering or numerous other manufacturing processes – in analyzing

cause-and-effect relationships in huge volumes of process

data.

The principle behind this approach is as follows: data is collected from multiple related process steps (predominantly time series of the actual process parameters) and analyzed for correlations with the process’s individual quality attributes. On this basis, the machine operator can initiate the appropriate improvement actions on the machinery and the process to optimize their quality over the long term.

In addition to our analytics services, we also help our customers apply specially developed predictive models to live data, e.g. as an algorithm in machine control. Then, should problems occur in the PVD coating process, those in charge can intervene early on in what is typically a long-running process flow, and therefore avoid waste and reduce failure costs.

By using Manufacturing Analytics, Bosch has been able to continuously improve quality and efficiency in this and many other manufacturing processes, despite rising complexity.

2 | Manufacturing Analytics tools & services

Big Datain Manufacturing

Data visualization Process parameters vs. quality

Feature extraction 374 features

Modeling / prediction

Predicted quality

Act

ual q

ualit

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Approach / solution

Sample project: Coating machinery

Background & objectives

Optimize a PVD coating process by gaining insights into correlations among process and machine parameters, in order to ▶ Improve production quality ▶ Shorten process times ▶ Reduce machine downtimes.

The Manufacturing Analytics project: Procedure & results

Step 1: Initial insights Define a limited data set (e.g. data from 20 closed batches) of the actual process parameters and analyze it with respect to the relevant quality criterion (e.g. scrap rate).

Initial results were quickly available, with data visualized in the form of a correlation matrix, to answer the question: “Of the batches that produce large amounts of scrap, which ones have strong correlations with specific parameters?”

Bosch Software Innovations | 3

Step 2: Advanced intelligence Expanded analysis, especially the correlations between machine parameters and quality targets. This involves: ▶ Extracting 374 features from the characteristic curves of the actual values ▶ Focusing on just a few process phases that are relevant for quality ▶ Synchronizing all time series for the identified quality-critical points in time ▶ Working with the customer to jointly determine the features and/or anomalies that the correlation analysis is based on.

Result: Finalization of the key process variables (e.g. temperature, pressure, tension, duration) that are decisive for quality. Step 3: Modeling / prediction The algorithms and analyses examined are made available to the customer so that they may detect process deviations. They can then apply the insights to live data in parallel to the process flow.

Result: An algorithm integrated into machine control triggers an alert in the event of a deviation.

AsiaBosch Software Innovationsc/o Robert Bosch (SEA) Pte Ltd.11 Bishan Street 21Singapore 573943www.bosch-si.sg 09

/201

6 FE

AmericaBosch Software Innovations Corp.161 N. Clark StreetSuite 3550Chicago, Illinois 60601/USAwww.bosch-si.com

EuropeBosch Software Innovations GmbHSchöneberger Ufer 89–9110785 BerlinGermanywww.bosch-si.de

Manufacturing Analytics services: Methodology

How do you search through 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.

For example, we apply the three methods – correlation analysis, boosted regression trees, and deep neural networks – to analyze the data sets from the manufacturing process.

Benefits of data analytics in this project

▶ Clear identification of the process parameters that impact quality and of the previously unknown cause/effect relationships. ▶ The coating quality can be significantly improved by applying the developed predictive models to real-time data.

This allows us to successively refine the results. The final product is a clear set of features that have an unmistakable correlation to the process result.

These insights are available to the coating process experts at Bosch as software applied. With it, they can evaluate process data in real time with the help of the algorithms, trigger prompt intervention in long-running processes via the early warning system, and therefore ensure the product quality as agreed upon with the customer.

Methodology