Automatically reliableIntelligent evaluations and quality decisions

Classical algorithms for the evaluation and evaluation of measurement data in quality assurance are rule-based. They work well for the rule-based evaluation of quantified values and the evaluation of various repeatable scenarios. This applies, for example, to tactile coordinate metrology or quantifying image processing. However, if the data is subject to strong fluctuations, e.g. due to non-repeatable image effects, or if it is a question of recognition or decision making for data objects, which a human being typically has to make, rule-based algorithms often reach their limits.

IconPro implements deep learning algorithms for the automatic evaluation of image measurement data where classical image processing is inferior. Through the data-based training of cognitive or intelligent abilities, customers receive solutions in a comparatively short time that are more robust and capable compared to the state of the art or that would not be possible at all. Machine learning can also be used to automate general manual quality decisions that previously had to be made by people on the basis of data sheets.

Select your field of application

Your advantages

Reduced scrap
Reduced throughput times
Minimized inspection costs

Predictive Quality

Predictive quality management is about correlating process parameters from production with quality data. This makes it possible to predict the quality of components based on the recorded process data and to automatically analyze the cause of defects in the event of quality problems. The goals here are the optimization of processes in favor of quality as well as the reduction of inspection costs.

Due to the large number of process parameters and often non-linear correlations, the use of artificial neural networks is suitable for such complex regression and correlation analyses. These networks are trained on the basis of relevant data extracted from production and quality monitoring databases If such data is available in a sufficient quality and the data can be assigned to individual workpieces or workpiece batches, an analysis of this kind becomes possible

Best practices

Production of engine parts

Reduction of the effort required for testing rotationally symmetrical parts

  • Data sheet processing
  • Inspection decision

Production of Razors

Quality-oriented optimization of the etching process

  • Quality Prediction
  • Optimization of process paramaters


Fault cause analysis to reduce scrap during the production of pharmaceutical substances

  • Data sheet processing
  • Historical data correlation

Your advantages

Minimized error rates
Shortened testing times
Reduced process costs

Artificial intelligence for optical inspections

As a quality or production manager, take advantage of the benefits of artificial intelligence when evaluating your optical inspection processes. Deep learning procedures for image processing are particularly suitable for complex inspection procedures in production, which must be robust against fluctuations or anomalies in the image data and evaluate qualitative or discrete measured variables.

In addition, they convince by a fast implementation, for which only little know-how in classical image processing is necessary, as well as by short evaluation times. Neural networks can either be pretrained or trained with your data. They enable the automation of processes for with so far no or only overly complex solutions have been available. If necessary, these solutions can also be combined with classical methods. In this way, error rates can be minimized, and testing times shortened.

Best practices

Brake production

Check for completeness and component arrangement after final assembly

  • In-line
  • Test result

Production of rolling bearings

Scratch and defect detection during inspection of ground surfaces

  • Final inspection
  • Defect localization

Manufacturing of textiles

Inspection of fiber alignment and detection and localization of defects

  • In-line
  • Test result

Your advantages

Minimized error rates
Shortened testing times
Reduced process costs

Automated quality decisions

As a quality manager, you can automate manual processes for quality decisions based on data sheets, e.g. from suppliers or ERP systems. With the help of machine learning algorithms (e.g. decision trees), historical data sheets and the corresponding quality decisions can be used to collect the process and product know-how inherent to them.

The resulting intelligent systems then make the decisions for new data sheets automatically, even for complex relationships and products. In this way, you benefit from minimized error rates and increased repeatability when deriving quality decisions.

Best practices

Food production

Evaluation of the quality of material batches from suppliers

  • Data sheet processing
  • Inspection decision

Manufacturing of chemicals

Automation of manual quality assessments of flavors and fragrances

  • Data sheet processing
  • Inspection and rework decision

Steel plants

Automation of manual quality assessments of structural and industrial steels

  • Data sheet processing
  • Inspection decision

Our services

Checking data quality

After you have described your application and objectives to us, we check the quality of the data provided with regard to feasibility.

Application implementation

A positive proof-of-concept is followed by the implementation of the application, whose operation and interfaces are tailored to your technical constraints.

Preprocessing of data

The type of data format or database is irrelevant to us. We process all types of data structures and are completely oriented towards you.


We provide the final software as an executable file for the environment you specify and test it extensively beforehand.

Machine learning evaluation

After the rough selection of the procedure best fitting to the use case, we evaluate your data using the most modern machine learning algorithms.

Privacy Policy

The confidentiality of your data is a natural priority for us. The protection of sensitive information is an integral part of our service.

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