Manufacturers are under pressure to bring better products to market quicker than ever and more sustainably. Today — in the context of the digital transformation of the industry — 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.

Everything starts with data

In the era of smart manufacturing, data quality is crucial. It’s the foundation for more effective quality management. But in the current landscape, many manufacturers face persistent challenges stemming from incomplete, outdated or inaccurate data, which hinders informed decision-making and effective quality strategies.

In term of measurement, one of the vital shifts in metrology is the move from the unsustainable reliance on “tribal knowledge” — siloed expertise held by individual experts — to full adherence to recognized industry standards, such as ASME and ISO.

Making measurement, reporting and analysis more intuitive and accessible, even for less experienced users, also helps address the growing skills gap. Source: Hexagon

To this end, technology providers like Hexagon are responding by developing innovative software and hardware solutions that more efficiently deliver rapid, reliable and repeatable measurements. Generating more data in less time with better accuracy enables manufacturers to accelerate their time-to-market while safeguarding product quality.

Simplifying the complexity inherent in metrology systems is crucial to this effort. Making measurement, reporting and analysis more intuitive and accessible, even for less experienced users, also helps address the growing skills gap due to the retirement of experienced metrology professionals.

Driving quality with connected data and collaboration

Improving products and processes to achieve higher quality requires that we connect data across the value chain. Source: Hexagon

Improving products and processes to achieve higher quality requires that we connect data across the value chain to foster a more collaborative approach to manufacturing.

For collaboration and data to be most effective, they must encompass the design, manufacturing and inspection phases — and even extend further to include customers and suppliers.

The most significant immediate potential lies in breaking down silos between quality control, design and production teams by sharing and leveraging accurate metrology and quality-related data at each stage.

It means we need connected, integrated systems that unite data. Every CMM, portable measurement device, sensor and every piece of software in a manufacturer’s facility or facilities needs to be connected, with data as the connecting thread.

To propel this change, metrology offices are set to transform into innovation hubs, driving continuous improvement by reimagining measurement and quality control processes as fully digitized, end-to-end workflows.

They'll play a crucial role in the quality digital thread by setting up an integrated technology stack of systems and processes that enables teams across the value chain to make the best use of metrology data.

Toward autonomous

Automation is already a key enabler and will play an ever-expanding role. On CMMs, for example, as software capabilities advance toward smarter, more autonomous solutions, the trajectory is toward automating the entire measurement process for any part across all stages, from programming and executing measurement routines to reporting and analysis, minimizing manual intervention and enhancing efficiency.

Many portable devices can also share in the benefits of automation, paving the way for diverse, innovative applications.

The shift from autonomy to "autonomous" centers on integrating deep learning models to create self-learning, self-adapting systems. As these systems process more data and gain experience, they progressively develop deeper expertise and domain-specific knowledge.

But more than this, the concept of autonomous extends beyond the measurement processes; it's also about creating a closed loop that feeds insights back into the manufacturing workflow.

The ultimate goal for self-learning systems is to progress beyond predictive capabilities toward being dynamic and prescriptive. Such systems will not only find the causes of non-conformance and other quality issues but also provide clear remedial actions or implement the necessary adjustments autonomously. This level of intelligence will unlock huge improvements in quality management.

Naturally, not all measurements can or should be automated; certain tasks will always best suit manual inspections. Even in these cases, however, deep learning and AI will contribute to making the measurement process better, easier and faster.

Shifting left: Enhancing early quality and manufacturability

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

This so-called “shift left” approach is transforming the way manufacturing quality is ensured. It emphasizes moving quality assurance activities earlier in the product lifecycle and as such is fundamentally different to the traditional reactive view of quality control that sees the place of metrology equipment primarily at final checkpoint inspections at the end of the production process.

Instead, metrology data will be increasingly applied to bring the design, make and inspect phases of manufacturing closer together in connected digital systems to detect, identify and resolve potential quality issues.

Metrology-informed simulation

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

Here, metrology data is imported to improve and validate process re-simulations to accurately evaluate part design and the aggregate effects of the manufacturing process to check their impact on quality.

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

Growing use of such methods in a variety of processes across industries in the coming years will bring hitherto unprecedented levels of control and efficiency to these phases of manufacturing.

Not only dimensions and PMI

In the next 10 years, the metrology office will no longer focus only on dimensions and PMI. Structural integrity must also be assessed. We need to know not only if a component is dimensionally correct but if it is structurally sound as well.

Non-destructive evaluation techniques, like CT scanning, ultrasonics and surface roughness are therefore also a key part of the quality story. This multi-layering of dimensional and internal analysis is becoming increasingly important.

AI is transformative but human expertise remains vital

Big data analytics, machine learning and AI will, of course, be some of the most transformative forces in quality control and assurance.

AI is a critical component of the metrology journey, poised to transform manufacturing productivity through a wide range of innovations. It’s essential to providing the capabilities required to drive quality at the speed necessary. AI helps use the immense volume of data now available and discover hidden correlations that will boost efficiency to keep manufacturers competitive.

AI systems will enhance individuals' abilities, yet human expertise will remain crucial. People will aid in refining the models and will still need to judge if something is sensible. AI will support quality professionals who, as domain experts, will have the time and capacity to use their abilities more strategically and concentrate on activities that bring the greatest value.

About the author

Peacock

Gary Peacock joined Hexagon in 2019 and leads the Metrology and Data Management software business unit. He has over 30 years of commercial, operational and management experience having worked with leading providers of technology solutions across many manufacturing, metallurgical and chemical industries. Gary is based out of Cobham, United Kingdom.