PdM Meets Product Design?

EP Editorial Staff | May 1, 2024

Industry 4.0 technology is opening doors to using manufacturing data to improve product modification and design.

Industry 4.0 data and AI technology make it possible to elevate predictive maintenance beyond asset performance to informing product design.

By Miron Shtiglitz, QualiSense

Anyone familiar with Industry 4.0 has likely encountered the concept of predictive maintenance. This approach harnesses data from machine sensors and accurately predicts when maintenance activity is actually required. This is generally more cost efficient and effective than using preventive maintenance, which employs fixed schedules to regularly perform, often unneeded, maintenance.

Predictive-maintenance sensor data is not the upper limit. With AI (artificial intelligence), inspection-system data can be correlated with asset sensor data to develop a new level of predictive maintenance. For example, we might find correlations between the quality of a product and the last time scheduled maintenance was performed. To make this approach work, you need very large volumes of data. 

Data gathered from product-quality inspection systems and the software that supports them will not only enhance the power of predictive maintenance but will shape product design. By using data to make the correct decisions during the design phase, we can reduce the risk of defects further along.

Imagine, for example, you are able to analyze correlations between the 3D structure of a part, the processes that are used to manufacture the part, and the potential for certain defects to materialize during manufacturing. The resulting analyses can help mechanical engineers make optimal design decisions.

The data could also help design engineers explore different options and their suitability. For example, let’s say an engineer wants to design a part that is thinner in a specific area and use a specific material for this purpose. Using data from other inspected parts, you might extrapolate that using material x at this particular level of thickness leads to an increased incidence of defects, or using a particular process in combination with this material makes it more prone to break. 

Although further away, this is a possibility that engineers and AI specialists are already talking about. It is sometimes referred to as the ‘expert system’ and is similar to the Artificial General Intelligence (AGI) that you read about in media headlines. 

As we move beyond the first level of predictive maintenance toward multi-sensor use approaches, the world of big data will open exciting possibilities. However, this next step is not the final chapter in the story. While we keep one foot planted in the present, we can still imagine a future where intelligent systems harness data to optimize maintenance activity while fundamentally reshaping manufacture of the product itself. 

Miron Shtiglitz is Vice President for Product and Delivery at QualiSense, Tel Aviv, Israel (, a developer of artificial-intelligence software for the machine vision/visual inspection market.


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