Predictive Quality

Info-Text

WHAT IS PREDICTIVE QUALITY

Poor quality is expensive and hardly forgotten.

Conversely, good quality is when the customer returns, not the product. But how can manufacturing companies consistently maintain quality and reduce scrap?

Predictive quality, also known as AI quality assurance, is a data-driven strategy that strives to guarantee the quality of production processes and the final product.

DEFINITION OF PREDICTIVE QUALITY

With predictive quality, quality-related processes and products are continuously improved through data-based projections, from production scheduling to anticipated rejection.

To create predictions regarding future quality – and, if required, to start improving it – relevant elements are taken into account.

Predictive quality (AI Quality assurance) reveals patterns and correlations between quality data and production data that were previously unnoticed.

These results are utilized to develop prediction models using artificial intelligence (AI) and machine learning that predict product quality and suggest process parameter corrections.

HOW DOES PREDICTIVE QUALITY WORK

Whether by a sensor, quality inspection measures, or customer feedback documentation, quality data for predictive quality models are often accessible.

On the other hand, the quality of a product is affected by a wide range of distinct production process variables.

Problems with the product can even arise even within the single-part production’s set tolerance limitations.

In AI quality assurance or predictive quality, all manufacturing parameter values ​​are automatically tracked and used to predict the potential product quality.

Corrections can be made before completion, thus reducing scrap and helping the process stability and capability.

Production data and quality data is used to train algorithms in AI software, which subsequently generates predictions automatically.

High levels of computer power are needed for the data-intensive procedure.

For instance, scalable cloud infrastructure can offer the required capacity. Standard interfaces can be used to make predictions and corrections accessible.

Machine learning techniques are used in industrial AI to identify quality deterioration or deviations from the norm.

Process experts are essential for confirming the validity of the data analysis results in the context of predicting and improving quality.

IconPro AI software continuously modifies the AI ​​models and algorithms depending on the findings of earlier analyzes through repeated feedback loops.

Furthermore, it must be defined in advance exactly what data is necessary, how it must be processed, and what level of forecasting accuracy is needed to provide actionable judgments.

EXAMPLE: SCRAP REDUCTION IN PRODUCTION CHAINS

A producer of automotive lighting seeks to reduce production scrap and improve process stability and capability.

The best process parameters may be found by training and using predictive models to improve the prediction quality.

IconPro analyzes information about the goods and procedures on the assembly line, which is generally accessible through the manufacturing execution system (MES).

A predictive quality model is trained using the MES data collected across the production chain.

Finally, already at early process steps within the production chain, the model offers quality predictions and suggests process parameter corrections concerning individual parts in-process.

The primary influencing factors or process parameters, respectively, are determined for each sample, thus making predictions and corrections transparent and understandable for the user.

BENEFITS OF PREDICTIVE QUALITY

The precise strategy (process optimization, root cause analysis, or scrap forecasting) will rely on each firm’s data situation and unique objectives.

 However, the benefits of predictive quality may be summed up as follows:

• less rework and scrap
  
• better quality and process capability
• improved process stability
• Less waste of materials and energy
  
• enhanced planning reliability
• Reduced throughput times
• increased customer satisfaction

CONCLUSION: PREDICTIVE QUALITY AS A COMPETITIVE ADVANTAGE

Customers are more often satisfied with products of obviously higher quality.

AI quality assurance or predictive quality uses machine learning to automatically decrease scrap, rejections, resource consumption, and rework in production while improving the product quality.

IconPro AI software is tailored to specific corporate conditions, depending on the IT infrastructure, data situation and use-case objectives. Through industrial AI, significant value can be added.