Hexagon Revolutionises Manufacturing Design Process
A global leader in sensor, software and autonomous solutions, Hexagon recently announced that complex CFD (computational fluid dynamics) simulations can now be completed with the help of the world’s fastest supercomputer, Fugaku. Before this breakthrough, CFD simulations were far too expensive and time-consuming to run. Now, however, engineers can use these high-detail simulations to explore new ideas, iterate their designs, and optimise next-gen aircraft and electric vehicle manufacturing.
Thanks to Hexagon, manufacturers can now analyse what they’re up against before starting their build process—with one-third the energy use of traditional simulations and a fraction of the cost. This is only the latest step in Hexagon’s mission to use design and engineering data to speed up smart manufacturing. As the company wrote: ‘The idea of putting data to work is part of Hexagon’s DNA’.
What Are CFD Simulations?
Simply put, they’re simulations so complex and powerful that engineers usually have to spend hours upon hours simplifying their designs. 90% of an engineer’s time can centre around this task—but not with Fugaku-powered simulations. Now, original designs can be fed into the simulation software, reaching a much closer approximation of reality.
With the ARM-powered Fugaku supercomputer, Hexagon’s Cradle CFD clients can now reduce simulation cost, conserve valuable energy, and integrate high-detail simulations into their daily operations. At a time when the automotive and aerospace industries are racing to bring safe and sustainable transport options to market, in fact, CFD simulations could be the key to success.
How Does CFD Change the Game?
As auto manufacturers transition to electric vehicles, they must understand how design adjustments will affect the vehicle in real-time. Instead of physically iterating their blueprints, they’d rather work it out in theory. With CFD, engineers can now pre-test critical safety, performance, and longevity features—for example, how aerodynamics will interact with energy efficiency, or how thermal management will operate under a range of parameters. Essentially, CFD simulations speed up the design process and cut down on costly mistakes.
Said Roger Assaker, President of Design & Engineering in Hexagon’s Manufacturing Intelligence division: ‘Simulation holds the key to innovations in aerospace and eMobility. Advances such as the low-power Fugaku supercomputing architecture are one of the ways we can tap into these insights without costing the Earth, and I am delighted by what our Cradle CFD team and our partners have achieved’.
How Did Testing Unfold?
- Prototyped a typical family car. This is only possible with enhanced computing power. The car model consisted of 70 million elements using 960 cores and was simulated until it reached a steady-state using the RANS equation over 1000 cycles.
- Simulated transonic compressible fluid around an aeroplane. Made up of approximately 230 million elements, the simulation used 4,000 nodes using 192,000 computing cores and relied on 48,000 processes via Message Passing Interface (MPI).
Tomohiro Irie, Hexagon’s Director of R&D for Cradle CFD, commented on the recent progress: ‘I expect that these technical developments will contribute to making the power of Fugaku more accessible for general use, bringing huge freedom and improved insights to engineering teams solving tomorrow’s problems today’.
Overall, Hexagon intends to continue driving product innovation forward, with smart manufacturing that adapts to conditions in real-time, pursues perfect quality, and optimises designs for zero waste. And there’s little doubt about it. With 20,000 employees in 50 countries, coupled with Fugaku’s supercomputing capabilities, Hexagon is uniquely poised to succeed.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.