Get real-time predictions about scrap including
prediction confidences.
Get to know the most relevant process influences on
scrap automatically and as a report.
Get to know how to prevent scrap by optimized
process parameter recommendations.
Understand and improve
process influences
Get quality and scrap
predictions + root causes
Get and configure optimal
process parameters
Monitor and control
software & resource usage
Visualize the directional influence and importance of every single process parameter, create and share reports and derive improvements.
Know about the quality of a part already before inspection and find hidden root causes for quality issues. Make quality assurance & control efficient.
Continuously get recommended configurations for your process – live and taking into account any process changes. Increased reliability and repeatability.
Always know and administrate who is using the software with which resources. Scale the software internally easily, securely and stably.
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Data-based evaluation of the wire electrical discharge machining process
Companies & Partners
WBA Aachener Werkzeugbau Akademie GmbH, Mak…
Project Subtitle
Industrial Reinforcement Learning for the Quality Control of Metal Forming Processes
Companies & Partners
Mubea, Tailor Rolled Blanks Gmb…
Project Subtitle
Quality Control of Vehicle Assembly using an Ultrasonic Imaging Sensor with Embedded Artificial Intelligence
Companies and Partners
Tessonics…
Understand your use case and requirements
Know if your current data is suitable
Collect & structure meaningful data
Extract the maximum data potential
Know the feasibility for your use case
Know the return on investment
Get roll-out roadmap after proof of concept
Get to know concrete user scenarios and added values through
Predictive Quality and Process Optimization in production.
What is the definition of Predictive Quality?
Predictive Quality is critical to any manufacturing operation because it helps ensure that products meet the desired quality and minimize waste.
Traditional quality assurance methods such as sample inspections or 100% inspections can be time-consuming, costly, and prone to human error. At the same time, they do little to identify causes of quality problems or even eliminate them in-process. For this reason, many companies are turning to Predictive Quality.
Predictive Quality uses machine learning algorithms to detect and prevent product defects before they occur. It uses data from a variety of sources, including material protocols, supplier documents, production equipment, and inspection process results, to identify patterns and predict the likelihood of defects or automatically optimize manufacturing parameters for the best possible product quality.
How does Predictive Quality work?
What are the benefits of Predictive Quality?
What is an example of a Predictive Quality use-case?
For example, a manufacturer could use Predictive Quality to analyze data about its welding processes. The model would analyze data on parameters such as welding speed, temperature and pressure to detect patterns and anomalies. Suppose the model finds that a particular welding process repeatedly produces defective welds. In this case, it can alert the production team to investigate and fix the problem before it leads to significant scrap.