Convincing Reference Projects

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OGP
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Solve the Challenges

Quality problems and inefficiencies are difficult to manage, especially when processes change dynamically under many influences.
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Predictive Quality & Process Optimization
for Your Use-Case

This is how you cost-efficiently realize a proof-of-concept
for Predictive Quality and Process Optimization in your production.

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Unleash potential

IconPro ARES enables you to predict and minimise production waste - in-process and in real time.

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WHEN will rejects occur?

You will receive real-time predictions about potential rejects per production unit or batch.

WHY does scrap occur?

You will find out the individual reasons for each scrap in the form of recognized process influences automatically or as a report.

HOW to prevent rejects?

You will receive optimization suggestions in real time to prevent or reduce rejects.

IconPro ARES

From data to information to added value

Quality Data

Process Data

Material Data

Sensor Data

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Process engineers

Understand and improve process influences

Quality Engineers

Control quality and rejects

Production workers

Configure process parameters
optimally

IT Administrator

Monitor software and resource
usage

From Data to Information

IconPro ARES for predicting and avoiding scrap
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Process Engineers

Complex processes
understand and improve

Visualize the impact and importance of each process variable. Share reports and derive improvements.

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Quality Engineers

Causes and rejects
identify and predict

Predict quality and find hidden causes of problems. Increase process capability & Quality rate.

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Production workers

Optimal process parameters
find and set

Automatically configure recommended process settings for higher reliability & Reproducibility.

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IT administrators

Software & resource usage
monitor and control

Find out who is using your software and with what resources. Scale the software internally easily, securely & stable.

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What are the benefits of Predictive Quality?

With Predictive Quality, production processes can be optimized from a quality-relevant point of view. In general, the following specific advantages result:

  • Less scrap: Optimized processes produce less scrap, which significantly reduces process costs through less rework and the use of materials resulting in a higher yield rate.
  • Less inspection effort: Predictive Quality enables an intelligent sample inspection by mostly testing only products for which a critical quality was predicted.
  • Higher quality assurance: By predicting product defects using Predictive Quality Analytics, the risk of poor quality upon delivery is also minimized for products that do not run through 100% inspection.
  • Less recall risk: The probability of recall risks and costs decreases drastically through the prediction of potentially defective products or through the realized process optimization.
  • More process insight: Predictive Quality Analytics offers process and quality engineers valuable data-based insights into the most important process influences and their interaction with the quality parameters.
  • Less waste: By reducing the number of defective products, Predictive Quality helps to conserve valuable resources, save CO2 and establish sustainable production.

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Case Studies

Leading
companies
use IconPro.

Energy – Minimize Consumption

Energy – Minimize Consumption

Project Subtitle
Automation of network edge infrastructure & applications with artificial intelligence

Companies & Partners
Opel Automobile GmbH, Frau…

Manufacturing – Ultrasonic Inspection

Manufacturing – Ultrasonic Inspection

Project Subtitle
Quality Control of Vehicle Assembly using an Ultrasonic Imaging Sensor with Embedded Artificial Intelligence

Companies and Partners
Tessonics…

Mechanical – Wire Eroding

Mechanical – Wire Eroding

Project Subtitle
Data-based evaluation of the wire electrical discharge machining process

Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Mak…

Energy – Minimize Consumption
Energy – Minimize Consumption

Project Subtitle
Automation of network edge infrastructure & applications with artificial intelligence

Companies & Partners
Opel Automobile GmbH, Frau…

Manufacturing – Ultrasonic Inspection
Manufacturing – Ultrasonic Inspection

Project Subtitle
Quality Control of Vehicle Assembly using an Ultrasonic Imaging Sensor with Embedded Artificial Intelligence

Companies and Partners
Tessonics…

Mechanical – Wire Eroding
Mechanical – Wire Eroding

Project Subtitle
Data-based evaluation of the wire electrical discharge machining process

Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Mak…

Minimize scrap rates and maximize quality of your production line.

Enter your Parameters

IconPro AutoML helps to realize savings of dozens of thousands of euros per year per production line. Process optimizations are derived from analyses or can even be integrated into the processes.

Yearly Scrap Costs of Production Line

1 k€

  • 1
  • 200

Scrap Rate

5%

  • 0.1
  • 30

  • Minimize your Scrape Rate to

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    2 %

  • Realize Annual Savings of

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    2 k€

How do you start with Predictive Quality?

In less than 6 weeks we will guide you through 6 systematic milestones to a cost-efficient proof-of-concept.

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  • Understand the use case, goals and requirements

  • Check whether and to what extent your data is suitable

  • Optimization of data quality and structure

  • Evaluation of data content and value potential

  • Determination of feasibility for your use case

  • Determination of return on investment

  • Roll-out roadmap after successful proof-of-concept

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How does predictive quality and process optimization work?

 

Predictive Quality Management uses machine learning algorithms to predict and prevent product defects before they occur. For this purpose, data from various sources is used, including material logs, supplier documents, interfaces to production facilities and quality assurance.

The quality data is correlated with the other data to predict the probability of defects or to determine optimal manufacturing parameters for the best possible product quality.

The following stages can be identified for the realization of predictive quality:

  • Data collection: The first step in implementing predictive quality is to collect data from relevant sources, such as machines, sensors, inspection processes or customer feedback documents.
  • Data analysis: The collected historical data is analyzed using machine learning. The algorithms look for correlations between quality data and the rest of the data.
  • Predictive modelling: Predictive models are derived based on the results of the data analysis. These models can process newly collected data to predict product failures before testing.
  • Predictive Monitoring: The predictive models are integrated into the manufacturing process. Predictions about product defects are now displayed and help to do intelligent sample testing or stop adding value.
  • Process optimization: The prediction models are used for the derivation and integration of optimization models. In this way, process parameters can be optimized in-process and in real time for less scrap.

 

Which companies benefit from Predictive Quality?

 

  • Predictive Analytics in Quality Assurance is of crucial importance for all manufacturing companies that want to ensure that products meet the desired quality and minimize waste.
  • Traditional quality assurance methods such as sample tests or 100% testing can be time-consuming, costly and prone to human error. At the same time, they do little to identify the causes of quality problems or even to eliminate them in-process.

 

For this reason, many companies are turning to the topic of predictive quality and process optimization. IconPro’s experts support you in evaluating your use case and using predictive quality to your advantage.

 

You might also be interested in this:

Machine Vision
Predictive Maintenance
Trend analysis & Prediction
Software Engineering
Data Screening & Analysis

 

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Our Offering

As simple, reliable and cost-effective as possible.
We offer the full range of industrial AI services.

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Our team will be happy to help you. Get in touch!
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FAQ

 

What is the definition of predictive quality?

Predictive quality is a method accordin to which quality and production data are correlated using machine learning. With the help of the derived models, the quality of products is already predicted during production. In addition, optimal process parameters can be determined for the best possible quality. Predictions and optimizations are used for minimizing inspection effort and scrap.

 

What are the advantages of Predictive Quality?

By determining and considering optimal process parameters during the process, scrap costs are dramatically reduced. Predictions about the quality during production allow dynamic and intelligent sample testing, which reduces the amount of testing required. Adding furhter value to a product with a high level of vertical integration can be intelligently stopped in case of a predicted high scrap probability.

 

What are use cases for Predictive Quality?

The application of predictive quality in production is possible for different industries. One example of the use of predictive quality is the production of pumps for dishwashers, where parameters such as the temperature during injection molding or screwing data were recorded along the entire manufacturing and assembly chain in order to predict quality or determine process corrections. Waste was thus significantly reduced.