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Transitioning to Quality 4.0: 5 Ways New Technologies Can Transform Quality Control in Manufacturing

Written by Jana-Katharina Schueller and Jessica Dolan

During manual assembly and automated production steps, errors occur time and again, which lead to inferior product quality. Manual visual inspections are costly, and errors are rarely adequately documented. Furthermore, defects are often discovered too late, resulting in unnecessary waste and costs. This was recently the case for carmaker Mercedes Benz, who had to recall around 660,000 vehicles due to a suspected oil leak.

With rising safety standards, environmental regulations and customer demands for customisation, quality control is growing in importance. Luckily, new technologies enable a considerable increase in product quality, sometimes with the use of surprisingly few resources. Industry 4.0 is transforming how quality assurance is carried out in manufacturing, so much so that a sub-concept called “Quality 4.0” has emerged. This term refers to the application of Industry 4.0 technologies in the area of quality control.

In 2019, the Boston Consulting Group conducted a study in collaboration with the American Society for Quality (ASQ) and the Deutsche Gesellschaft für Qualität (DGQ) to find out more about attitudes towards Quality 4.0. The survey participants were executives and quality managers in manufacturing firms, who were mainly based in Germany and the US. One major finding was that they considered Quality 4.0 to be relevant at all stages of the value chain but particularly in manufacturing, with 74% rating it as extremely important or very important.

Importance of Quality 4.0 at each value chain stage

Another key takeaway from the study concerns the expected benefits of implementing new technologies in quality control. As you can see below, the professionals surveyed expect the greatest potential for improvement in areas like defect rate, cost of quality check, customer satisfaction and product-related complaints. Frontrunners represent those companies that have already implemented Quality 4.0 in their operations.

The benefits of Quality 4.0 are clear but its implementation is not always straightforward. In this article, ROKIN presents five innovative technologies that can help you to enhance optical quality control in your production environment.

1. IIoT radar-based optical sensors

Precise measurements are essential for producing high quality products. Optical sensors are generally a good option for automating such tasks. However, some of these solutions lack accuracy in close range, especially under the harsh environmental conditions presented by factory environments. Noise, dust, smoke, steam and fire make it difficult for optical sensors to accurately measure distances and detect defects in products.

OndoSense proxi optical sensors
(Image courtesy of OndoSense)

Developed by OndoSense, a Freiburg-based start-up, OndoSense proxi is a networkable sensor based on radar technology, which can not only withstand extreme conditions but also provide highly accurate measurements. The optical sensors achieve accuracy in the sub-micrometer range, specifically up to 10 micrometers. They operate in approval-free frequency ranges and can also look inside objects to recognise further inconsistencies such as foreign components. As there are up to four connections for measuring heads, multichannel measurement is also possible. Moreover, their software, OndoNet, enables the setting up of intelligent sensor networks. Therefore, OndoSense’s smart sensor solutions are perfect for automating quality assurance tasks in the metal industry, automotive industry and plant and mechanical engineering industry. The inline inspection of weld beads is just one of many specific applications for which the technology could be used.

2. Membrane-free acoustic sensors

While optical sensors convert light into electrical signals, acoustic sensors are acoustoelectric transducers, i.e., they convert acoustic signals into electrical signals. Unlike optical sensors, acoustic sensors are not affected by object colour, transparency or material, nor are they sensitive to dust, smoke or light. By sending out high-frequency sound waves beyond the range of human hearing, ultrasonic sensors can accurately measure distances and detect defects for the purpose of quality control. Usually, acoustic sensors require membranes or other moving parts to do this.

XARION optical microphone
(Image courtesy of XARION GmbH)

Vienna-based firm XARION have developed innovative laser-based acoustic sensors. Their optical microphone is unique, being the first of its kind with no mechanical elements. The patented sensor technology is able to detect sound by directly measuring the pressure waves via laser beams and can monitor the entire acoustic bandwidth of frequencies which can be physically transmitted through air. In fact, with the ability to cover a frequency range of 10Hz to 1MHz, it outperforms existing acoustic sensors by a factor of ten. Thus, with the Eta100 Ultra optical microphone or one of the other cutting-edge sensor solutions in XARION’s portfolio, contact-free inspection is possible. The resulting ultrasound images clearly display internal defects, ensuring the highest level of quality. The sensors are ideal for both in-line process control and non-destructive end-of-line testing.

3. Deep learning-based visual inspection software

Currently, quality inspection is primarily performed by human experts, which is both costly and insufficiently reliable, despite decades of accumulated experience. Is there a way to hold on to the expertise but lose the fallibility? Cue Deep Learning-based technology solutions. Deep Learning is a subset of machine learning in AI, in which artificial neural networks develop said experience in a dramatically condensed time frame. The deep learning algorithms repeatedly perform a task, modifying it fractionally each time to improve the outcome. This offers exponential learning capabilities that can be put to great use.

MoonVision Metal Scanner
(Image courtesy of MoonVision)

Founded in 2017, Vienna-based start-up MoonVision have developed AI-based software that specialises in quality control and assurance of assembly products and surfaces. Images and videos are collected and processed via a visual data lake using Deep Learning. Thanks to superior Few-Shot object detection, quality control can be automated in five key areas: the assembly line, metal processing, wood processing, welding and the paint shop. MoonVision AssemblyControl allows for individually manufactured components to be automatically checked for their composition, positioning and alignment, ultimately resulting in significantly shortened lead times. Two other products, the MoonVision Metal and Wood Scanners, facilitate automated quality inspection of metal and wood surfaces. According to MoonVision, their WeldScanner is even capable of reducing the time for measuring weld seams to less than one second. MoonVision’s products bring quality inspection to new levels through the fully automated detection and differentiation of defects on different surfaces in real-time, shortening lead times and boosting efficiency. In fact, Vodafone Germany already uses the MoonVision Toolbox for street surface inspection, defect detection and maintenance optimization.

4. 3D scanner with HD accuracy

Having an accurate 3D inspection process is another crucial step towards optimal quality control. It allows users to reliably measure sub-millimetre features and frees up employees from spending unproductive hours doing something the right device does quicker and better. Yet, the initial hurdle is to obtain an accurate 3D model. Using systems based on laser lines can be problematic, as the users’ hands’ tremor impacts the accuracy of the results, leading to unnecessary reworking of the 3D model or simply accepting a unsatisfactory result.

Scoobe3D Scanner
(Image courtesy of Scoobe3D GmbH)

The rising frustration with the lack of a fast, cheap and accurate way of creating a 3D model as a foundation for a 3D print inspired Augsburg-based start-up Scoobe3D to develop a unique technological solution which combines a Time-of-Flight Image and a polarized RGB image. Here, the hand’s tremor does not affect the accuracy of the scan, allowing a seamless scan from all angles to 0.1 mm accuracy, i.e., HD accuracy in 3 dimensions. A key advantage is that the Scoobe 3D scanner can be operated intuitively through the use of a touchscreen and creates a scan that can be immediately used without post-editing. Flawless 3D models, which can be directly imported into CAD, are a first step towards consistent and high-quality final products.

5. AR-based model tracking

Augmented Reality is another key enabling technology of Industry 4.0 and has the potential to revolutionise quality control. According to Darmstadt-based start-up Visometry, AR enables the checking of desired and actual construction states for inconsistencies, missing or wrongly installed parts while they are still in the prototyping phase. Intent on changing AR bottom-up, Visometry created VisionLib, a multi-platform library for enterprise AR applications, allowing, among many other things, the 3D object tracking that is so vital in quality control.

Visometry digital twins
(Image courtesy of Visometry)

By combining CAD and 3D data with image processing, VisionLib allows a real-time analysis against the original design data, making the checking process both accurate and fast. It also enables increased flexibility of test procedures. If changes occur, VisionLib simply accesses a different CAD database. According to Visometry, the use of VisionLib enables new procedures including existence checks, position and orientation inspection, virtual measurement, and variance analysis of desired vs. actual construction states. For the latter, VisionLib utilises CAD and computer vision to carry out multi-object tracking with mono-or multi-camera setups, allowing variance analysis at automated Inline Production or Assembling. With their innovative technology, it is also possible to create Digital Twins on mobile, handheld devices. Such methods can detect faults and inconsistencies as early as the prototyping phase.

If technological solutions like those mentioned above are implemented, real-time, flawless optical quality control in manufacturing can quickly become part of our day-to-day experience, making human visual inspection errors a thing of the past. The associated benefits are numerous, ranging from drastically shortened lead times, detection of errors at the prototyping stage and painless compliance with coveted industry standards to freeing up human brain power to be applied where it is most needed and to add real value in a way that machines cannot.

Still not sure how the latest technologies and Industry 4.0 innovations could help solve problems like quality control in your production environment? If so, feel free to send us a message at with details of your problem. Drawing on our industry expertise and AI-enhanced search methods, we conduct extensive research and suggest the best technology solutions tailored to your specific situation.


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Thomas Kinkeldei


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