W ith the rapid digital transformation of manufacturing, including technologies like cloud solutions, digital twins, next-generation devices, automation, and AI, the role of metrology is poised for a transformative evolution. What can we expect from new quality systems, and how is the metrology office’s purpose changing to tackle industrial opportunities and challenges? 

Manufacturers are under pressure to bring better products to market more quickly and more sustainably than ever. Improving the quality of new products faster is a top priority.

But quality challenges remain prevalent. Unanticipated quality issues and poor product quality are common issues. This shows that the traditional quality control methods are no longer adequate, and we must accelerate away from a reactive approach and toward a proactive position on quality. Other factors, like time to market and pace of innovation, are also important. 

In the context of digital transformation, smart manufacturing, data, connectivity, collaboration, and automation are central to any vision of the future of manufacturing, and are the biggest change drivers for enhancing quality, productivity, innovation, and sustainability. 

Data quality is key 

Everything starts with data. They are the new currency of the smart manufacturing world. Only when we have the right data can we make informed decisions and formulate the best quality management strategies. However, most manufacturers struggle with data-related challenges and are frequently hampered by the quality of their data, which are often incomplete, outdated, or inaccurate. 

From the metrology perspective, one of the most essential requirements is establishing data integrity and best practices across manufacturing by moving from a reliance on individual knowledge and siloed expertise (“tribal knowledge”) to improved compliance with industry standards from organizations such as ASME and ISO.

That’s why Hexagon, along with other technology providers, is focusing on software and hardware innovations that deliver rapid, reliable, and repeatable measurement. More data in less time with a higher degree of accuracy allow manufacturers to speed up their time to market without risking quality issues. 

An essential part of this is simplifying measurement and streamlining reporting and analysis workflows. Removing complexity from metrology systems to ensure they are easy to use, even for less experienced operators, is especially crucial, given the increasing skills gap caused by the retirement of highly skilled metrology engineers. 

From automation to autonomous 

Naturally, automation will play an increasingly central role here. On CMMs, we can already automate the creation of measurement programs for prismatic parts directly from computer-aided design (CAD) data. As the software becomes more capable—essentially with our digitized expertise and domain knowledge—the direction will be toward automating the entire measurement process as much as possible for any part, from programming to executing measurement routines, and then reporting and analysis. 

Portable metrology will also benefit from advanced automation. Although these devices are usually operated by a person, many devices are suitable for automation, and developments are moving forward at pace. Mentioning just a few examples, we’ll see more in-line systems and the expanding use and capabilities of laser tracker-guided robotic automation and AGVs (automated guided vehicles) in manufacturing workflows. 

The move from autonomy to autonomous is about adding deep-learning models to promote self-learning, self-thinking systems. The more data and learning experiences they have, the better and sharper these systems become as they develop deep expertise and domain knowledge. This digitized insight then enables a system to take intelligent, autonomous actions. 

Autonomous isn’t only about the measurement. It’s also about the closed loop back into the manufacturing process. 

Our ultimate goal for these self-learning systems is to move beyond predictive capabilities to be genuinely dynamic and prescriptive, where the systems understand the causes of nonconformance and other quality issues, and offer clear remedial guidelines or make the necessary changes autonomously. This is where the intelligence takes over, and it will make a huge difference. 

Of course, not everything can or should be automated. Some measurement needs will always be best met with manual inspections using, for example, portable measuring arms or handheld scanners. But here, too, deep learning and AI will play their roles in making measurement better, easier, and faster. 

Quality depends on connected data and collaboration 

To make better products in better ways—to lift quality to new levels—we need to connect data from across the value chain for a more integrated, collaborative approach to manufacturing. 

For data and collaboration to deliver maximum value, they must span the design, manufacturing, and inspection phases—and even beyond to our customers and their suppliers. It means we need connected, integrated systems that unite data, allowing them to be shared and put to work. 

The greatest immediate opportunity exists in connecting insights and boosting collaboration between quality control, design, and production teams.  

This requires the sharing and harnessing of accurate metrology and other quality data across each stage of manufacturing. Every CMM, portable measurement device, sensor, and piece of software in a manufacturer’s facilities needs to be connected, with data as the connecting thread. 

In this respect, metrology offices will evolve toward being innovation hubs and drivers of continuous improvement, tasked with reshaping measurement and quality control processes into fully digitized end-to-end processes. 

They’ll be responsible for ensuring an integrated technology stack of systems and processes that allow the most effective use of metrology data by teams across the value chain, both downstream and upstream. 

Metrology tech suppliers, for their part, are hastening to help by developing unified, cloud-enabled hardware and software systems that connect data, people, and processes, and offer easier-than-ever integration into a manufacturer’s smart manufacturing environment. 

Shift left: Evolution from reactive to proactive measurement centers 

In a recent industry survey, when asked what would most benefit their manufacturing process and product life cycle, 43% of manufacturers reported seeing opportunity in prioritizing final quality and manufacturability earlier in the process. 

Metrology data will be increasingly central to this so-called “shift left” approach in manufacturing, which is transforming the way manufacturing quality is ensured. 

Shift left emphasizes moving quality assurance activities earlier in the product life cycle. Put simply, it’s the effort to detect, identify, and resolve potential quality issues as early as possible, which makes it easier and simpler to fix those issues and save resources. 

This approach is fundamentally different from the traditional reactive view of quality control, which sees the place of metrology equipment primarily as final checkpoint inspections at the end of the production process.  

Instead, metrology data will increasingly be applied to bring the design, make, and inspect phases of manufacturing closer together in connected digital systems that enhance quality at every stage of the manufacturing value chain. 

Metrology-informed simulation 

Some of the most transformative advances will come from integrating real-world measurements into digital design and simulation models that allow manufacturers to validate designs and improve production processes using virtual prototypes that more accurately predict and prevent quality issues. 

Here, metrology data are imported to improve and validate repeated process simulations to accurately evaluate part design and the aggregate effects of the manufacturing process to check their effect on quality. 

Adding AI to these simulation workflows enables closed-loop inspection, where data-driven AI decision-making gives us a dynamic understanding of what we should inspect. It ensures we measure only what we need to measure, saving time, effort, and costs. 

These techniques help manufacturers improve design for manufacturability and production, enabling engineers to predict and compensate for factors like part deformation and geometric tolerances before physical manufacturing begins. 

This fusion of the physical and digital accelerates prototyping, which in many cases will remain almost entirely in the virtual world, helping to optimize parts and processes for quality and speed while reducing material waste. 

There are notable examples of this now, like 3D printing geometry compensation and virtual assembly solutions. Their growing use in a variety of processes across industries in the coming years will bring unprecedented levels of control and efficiency to these phases of manufacturing. 

Structural integrity must also be assessed 

During the next 10 years, the metrology office will no longer focus only on dimensions and positive material identification (PMI). We need to know not only if a component is dimensionally correct but also if it’s structurally sound. 

Nondestructive evaluation techniques like CT scanning, ultrasonics, and surface roughness are therefore also a key part of the quality story. When worked into simulations, these techniques get not only the dimensions but also the internal characteristics and features we should be measuring for, as well as the tolerances we should be measuring against. 

This multilayering of dimensional and internal analysis is becoming increasingly important. 

Shift right–to customers and their suppliers 

Because quality is a whole-of-business issue, the quality benefits of sharing data and collaboration extend well beyond the designing and making phases and also beyond the immediate manufacturing facility. 

For example, a customer’s suppliers could be integrated into the quality process, so when a part or subcomponent is delivered to a manufacturer it will have already been confirmed that it’s fit for use. Supply chain, production, and metrology teams can also collaborate to address any supplier issues affecting product quality.  

The metrology office will work with an expanded value chain and much deeper integration of upstream and downstream workflows in the future. 

AI changes the landscape, but human expertise is essential 

The biggest game changers in quality control and assurance are big data analytics, machine learning, and AI. 

AI is a critical part of our metrology journey that’s necessary to deliver the capabilities we need to drive quality at the current speed of manufacturing. It’s the answer to leveraging value from the increasing volume of data now available, and to discovering previously unseen correlations that boost efficiency and competitiveness. If data are currency, we must invest in tools that grow the investment and put data to work in the most effective way. 

AI is going to transform manufacturing productivity through myriad innovations:
• It gives a brain to automated processes.
• It will capture and preserve knowledge, ensuring that critical skills and experience aren’t lost.
• It will help upskill the next generation. 
• It will usher in new, more flexible and intelligent modular software apps. 

AI systems will augment people’s skills. They won’t take over everything but rather serve as assistants to quality professionals who, as domain experts, will be freed to apply their expertise more strategically on value-adding activities. Human expertise will remain essential. People will help refine the models and will still need to say whether something makes sense or not.