The aerospace industry is growing at an exponential rate. In fact, by 2028 it is predicted that upwards of 38,000 aircraft will be in service, a vast increase from the 26,000 being used today. As a result, digitalisation is increasing the reliability and efficiency of aerospace systems across the world. Here, John Young, APAC director at automation parts supplier, EU Automation, explains how digitalisation is transforming the aerospace sector in the Asia-Pacific region.
Like many other industries, digitalisation is transforming the aerospace sector. Currently, there is already an uninterrupted flow of real-time information coming from aircrafts updating ground operations and the pilots on the status of systems, equipment and weather conditions. However, this is simply the beginning of what is possible with the integration of digital technology across the sector.
Across maintenance departments in the industry, data is being monitored and analysed by artificial intelligence (AI) and machine learning systems. In fact, airlines in Asia have already begun implementing AI tools for simulation and data modelling of aircraft.
This information can then be used to decide precisely when an aircraft’s components should be replaced or repaired and when other maintenance is required. This integration has helped to ensure that the lifespan and function of individual parts are fully optimised, and the overall aircraft systems are kept safe.
By using AI to monitor and predict requirements, it is possible to ensure that all required maintenance equipment and parts are ready for when the time is right.
In recent years, Virtual Reality (VR) alongside big data has pushed the boundaries of predictive maintenance. Since 2016, the aerospace company Airbus has been making use of this technology to help boost Asia’s maintenance, repair and overhaul (MRO) sector inside its Hangar of the Future initiative in Singapore.
VR and augmented reality (AR) technologies are disrupting traditional techniques of aerospace maintenance by allowing engineers to see maintenance activities from new and unexplored angles. This means that new data can be captured, and advanced simulations can be created to train maintenance teams for future procedures, as well as allowing personnel and pilots to view and test virtual replicas of the aircraft equipment before physically handling them.
One of the downfalls of rapid uptake in digitalisation is the risk of data security and breach of privacy. This uncertainty applies to the aerospace sector especially, where the increasing connectivity of systems is also putting aircraft at risk of hacking and attack from cybercriminals.
Countries in the Asia-Pacific region have been reported to be 80 percent more likely to be victims of cyber theft as a result of their lack of awareness. Leading suppliers, however, can offer cybersecurity services and build a safe environment of data security and trust, while also helping organisations to avoid and recover quickly from cyber-attacks.
There is no shortage of digital technologies being used in the aerospace sector. These new and rising innovations are disrupting traditional methods of maintenance, operations and repair by providing experts with more intel about vital parts and the mechanical needs of aircraft. However, much of the vast quantities of data that technology such as AR and VR are producing still need to be kept secure. Only then can the digitalisation of aerospace fully flourish and continue to grow.
In an interview with Asia Pacific Metalworking Equipment News, Uwe-Armin Ruttkamp of Siemens Digital Industries talked about how digitalisation is helping machine builders and users, the utilisation of data to improve manufacturing processes, as well as how umati will help push the metalworking industry forward. Article by Stephen Las Marias.
One of the highlights of Siemens’ booth at EMO Hannover 2019 is the latest generation of its Sinumerik One, the first digital native CNC aimed at driving the digital transformation in the machine tool industry. Siemens has also extended its Industrial Edge offerings for Sinumerik Edge to include more new applications to help machine tool users improve workpiece and process quality, increase machine availability, and further optimise machine processes.
With Sinumerik One, machine tool manufacturers can virtually map their entire development processes, significantly reducing the product development phase and time to market for new machines. This helps machine builders significantly reduce the duration of actual commissioning. Its virtual model opens up new possibilities for manufacturers and operators—machine concepts and functions can be discussed even before real hardware is available.
Sinumerik One enables machine users the programming of workpieces in the virtual environment and the setup and operation of machines completely on the PC. Employee training can also be carried out using the digital twin instead of the actual machine. These hardware and software innovations help machine builders and operators speed up processing steps significantly.
In an interview with Asia Pacific Metalworking Equipment News, Uwe-Armin Ruttkamp, Head of Machine Tool Systems, Motion Control Business Unit, Siemens Digital Industries, talked more about the benefits of these new technologies and how digitalisation is helping machine builders and users. He also discussed the utilisation of data to improve manufacturing processes, as well as how umati will help push the metalworking industry forward.
When we look at the current potential for these technologies and all that they involve, are they more suited to advanced markets such as Europe or the US?
Uwe-Armin Ruttkamp (UR): I wouldn’t say so. You have all kinds of industries also in Asian countries. Not everything is low-cost and price-driven; they are also technology driven, especially aerospace, automotive industries, or the upcoming additive manufacturing.
So, there’s a lot of technologies driving the industries. In addition to this, labour is not staying on this low-cost level—in Asian countries, people want to earn more money as well—so saving time, and saving cost by saving time, is also an issue for Asian countries.
How does this technology play out in the smart factory concept?
UR: It plays perfectly into that concept, because with our Digital Enterprise (DE) Portfolio we offer a holistic end-to-end solution including industrial software and automation that allows the use of a seamless value chain. This value chain consists of five steps for the machine user, and five steps for the machine builder. If you build a machine, you start with a concept, mechanics, you go to electrical design, you go to engineering, you go to commissioning, and sometimes, it also needs service.
For the machine user, there are also several steps needed to build a part. Get the machine on the shop floor, create a part, build the part, check it for quality, and ship it. And this complete concept is the basis for running a smart factory.
In a lot of these steps, Sinumerik One brings great benefits. For example, in machine engineering, people in the offices can engineer the machine. You don’t need to have a test rack next to your desk, and you don’t need to go to the shop floor to test the applications. You can do it all in the virtual world. That’s one perfect example of an Industry 4.0 application that people will get from our Sinumerik One concept.
How do you see digital twins being implemented by customers in Asia?
UR: I see a lot of customers thinking about it. We talk to many customers, including those in Asia. We, for example, are customers of our customers. We have factories ourselves. And we only buy machines where we can get a digital twin beforehand. We make it a prerequisite for purchasing a machine, that it comes with a digital twin. And I believe in future many other users are going to do the same. The benefits are huge. You can train the people, who are going to operate the machine, before the machine is even delivered. And even more, you can also do the run-ins, do the first test of the programs, and know the cycle time of the production, before the machine is delivered.
Does siemens have a benchmark so that when machine users’ data are analysed, they will determine whether they are doing okay or they are falling short?
UR: We offer from our service department a digitalisation check. Together with our customers we examine their factories and give them advise what digitalisation measures are in place to get to another productivity level. It’s a consulting approach not a benchmark.
More and more people are talking about the lights out factory. how are you helping customers go into that level of manufacturing?
UR: Lights out factories are not new. When you go into an automotive factory, for example they produce the same part over and over, it is relatively an automated production. So, what they have done, of course, is to use a CAD/CAM chain, which, out from the design of the piece, create the program to build the piece, download it into the machine, and run it. Of course, this is something we support with our DE portfolio. You can put a program into the machine remotely, and then run it automatically. But of course, it requires in-feed of the materials and taking out the material and the pieces produced. But then again, you need automation, and the complete tool chain and software, like NX for example, or TeamCenter, to have a data backbone for all the production information about the part. But there are other companies focusing on job shops, so they produce many different parts every day according to customer specifications. For them it does not make sense run a fully automated line. So, a lights-out factory for them is not possible.
One of the highlights of emo 2019 is umati. How are you supporting this initiative?
UR: We support it 100 percent. We are part of the initiative and helped it to get to the point where we are today. At Siemens our solution to serve a universal interface for machine tools is based on our industrial edge concept. Edge computing is the perfect solution for this. For example, one wants to have a central dashboard, which shows the amount of cooling liquid used per hour. Cooling liquid per hour is not stored as one piece of information in all the machines in the same way. You need to have some sort of programming that knows where that data is stored in the machine and sends it out in a uniform way. Our Siemens industrial edge concept is perfectly suited for this, because OPC-UA is built into our edge devices. This allows the machines to communicate the data provided based on OPC-UA, and the user can program a little piece of code into it to acquire the data out of the machine.
The specifications for umati is still being finalised. during its early development, what were the challenges that you experienced, and are they still a challenge now?
UR: From a technical perspective, it’s not difficult, because it is OPC-UA, and it is a definition of data. It is basically a companion stem based on OPC-UA. The difficult part was to get an agreement among all parties which data they want to support, or which use cases they want to support. Once umati defines which piece of information has to be programmed, it’s done. It’s relatively simple.
Having a predictive maintenance plan in place, powered by machine learning, will give you unprecedented insight into your operation and will lead to serious benefits in efficiency, safety, optimisation, and decision making. Article by Richard Irwin, Bentley Systems.
One of the goals of reliability is to identify and manage the risks around assets that could fail, causing unnecessary and expensive downtime. We know it is important to identify areas of potential failures and rate them in terms of likelihood and consequence. We have put good reliability strategies in place and have implemented proactive condition-based maintenance programs. Today, machine learning is helping maintenance organisations get to an elevated level of situational intelligence to guide actions and provide early warnings of impending asset failure that previously remained undetected. Machine learning is paving the way for smarter and faster ways to make data-driven decisions in predictive maintenance.
While machine learning has been researched for decades, its use in applying artificial intelligence (AI) in industrial plants and infrastructure asset operations is now advancing at a rapid pace. This influx of using machine learning is due to the growth in big data, the Industrial Internet of Things (IIoT), computing power, and the need for superior predictive and prescriptive capabilities required to manage today’s complex assets. While machine learning has typically been linked with industries such as transportation and banking (think self-driving cars and fraud monitoring, respectively), there are many uses for machine learning and predictive maintenance within the industrial sector. This article will focus on some of the principles within machine learning and industries that are primed to take advantage of the application of machine learning to maximise the benefits it brings to improve situational intelligence, performance, and reliability.
Before starting, it is important to point out that there are many options and techniques available to gain more insight and make better decisions on the performance of your assets and operation. It all comes down to knowing what the best fit is for your needs and what type of data you are using. Data comes in many shapes and sises and can consist of time-series, labelled, random, intermittent, unstructured, and many more. All data holds information, it’s just a case of using the right approach to unlock it, and this is where the algorithms used within machine learning help decision makers.
6 Questions to Answer Before Investing in Machine Learning
It is important to understand the complexity involved with machine learning before you make a decision on what is appropriate for you and your organisation. Here are some questions to ask yourself before implementing machine learning:
Question your data – What do you need to know, what are you looking for exactly? What do you want your data to tell you? What aren’t you seeing that you hope the data can provide?
Is your data clean? – Make sure your data is available, ready, and validated; the more data, the better and the more accurate the outcomes will be.
Do you have enough data? – For accurate predictions, machine learning needs lots of historical data from which to train, then it can be applied to data in real time.
Which ML platform do I choose? – Choose your machine learning platform by carefully considering interoperability.
Do I hire a data scientist, and how do they integrate? – With machine learning, there might be a need for a data scientist or analyst, but they shouldn’t be locked in a dark room.
Can I share the data output? – Knowledge gained through machine learning shouldn’t just be applied to one project at a time. Its scalability means it can and should be incorporated across the whole enterprise, delivering insight into any area rich in data. Plan to get the most out of machine learning.
The Route to Deeper Understanding
Machine learning makes complex processes and data easier to comprehend, and it is ideal for industries that are asset and data rich. In any industry, the ability to recognise equipment failure, and avoid unplanned downtime, and repair costs, among others, is critical to success. This is even more relevant in today’s turbulent times. With machine learning, there are numerous opportunities to improve the situation with predictive maintenance and the ability to predict critical failures ahead of time.
Predictive maintenance will be one of the most applicable areas where machine learning can be applied within the industrial sector. Predictive maintenance is the failure inspection strategy that uses data and models to predict when an asset or piece of equipment will fail so that proactive corrective actions can be planned in time. Predictive maintenance can cover a large area of topics, from failure prediction, failure diagnosis, to recommending mitigation or maintenance actions after failure. The best maintenance is advanced forms of proactive condition-based maintenance. With the combination of machine learning and maintenance applications leveraging Industrial IoT (IIoT) data, the range of positive outcomes and reductions in costs, downtime, and risk are worth the investment.
Whatever path is chosen, the benefits machine learning can offer to big data are only just being brought to fruition. Opportunity is rapidly developing with productivity advancements at the heart of the data rich industry in which you work. While healthcare, financial, automotive, oil and gas, electric and power, and water utility sectors are already advancing with machine learning, there’s another sector leading the way in this fast-moving digital transformation: manufacturing.
Manufacturing has always been the main industry when mentioned alongside machine learning, and for good reason, as the benefits are very real. These benefits include reductions in operating costs, improved reliability, and increased productivity—three goals that relate to the holy trinity of manufacturing. To achieve this, manufacturing also requires a digital platform to capture, store, and analyse data generated by control systems and sensors on equipment connected via the IoT.
Preventative maintenance is key in improving uptime and productivity, so greater predictive accuracy of equipment failure is essential with increased demand. Furthermore, by knowing what is about to fail ahead of time, spare parts and inventory can use the data to ensure they align with the prediction. Improving production processes through a robust condition monitoring system can give unprecedented insight into overall equipment effectiveness by monitoring air and oil pressures and temperatures regularly and consistently.
Early Case Study Example: Process Manufacturing and Condition Monitoring
This example is centred around a steel manufacturer who routinely shuts down operations to perform maintenance on its assets, which is very costly. The steel output can sometimes warp or “crimp” during the production process as it travels through different stages. These failures can only be corrected every six months (as well as monthly for smaller fixes) during planned—and very expensive—maintenance that involves long periods of downtime. The main goals of applying machine learning here were to: reduce defects and locate root cause; identify key variables that matter the most; and prioritise assets during shutdown.
The first part of the machine learning process was to sort the data into a self-organising map using neural networks to organise data into 10 distinct classes based on parameters of the steel, such as thickness and weight, as they entered each manufacturing stage. Other techniques included decision trees to learn the pattern of data and to identify which features were important in those patterns; asset health prioritisation to provide ranking; asset health indexing to determine the health of the assets; principle component analysis to reduce the dimensionality of the data; and clustering/anomaly detection, which highlights how each stand deviates from its normal operating mode.
What developed was a method for dealing with different types of products, the ability to identify the top variables associated with production defects, and a process for applying anomaly detection to equipment in an industrial plant. It was shown that these processes could reduce the need for extensive analysis of equipment and give operators better tools and more insights to make maintenance decisions. A significant amount of time is spent locating the cause of the issues and performing maintenance. The new algorithm can be run before planning the shutdown, and it can identify which stand to prioritise during shutdowns through analysis of the asset anomaly charts. Focusing on assets that are the most at risk optimises the shutdown, as it is only conducted for a limited time.
Digitalisation and Transformation with Machine Learning
Early adopters of machine learning are already reaping the benefits of predictive maintenance in the speed of information delivery, costs, and usefulness. This gives you more information and insight to make smarter decisions. Bentley Systems’ users are combining machine learning with Bentley’s other digitalisation technologies to make this process even more beneficial—by making it model-centric and adding visualisation dashboards, cloud-based IoT data, analytics, and reality modelling to machine learning, the result is a complete solution for operations, maintenance, and engineering. Machine learning can also be leveraged within digital twins to provide even more predictive insights.
Having a predictive maintenance plan in place, powered by machine learning, will give you unprecedented insight into your operation and will lead to serious benefits in efficiency, safety, optimisation, and decision making. The digital transformation for industry is now at a tipping point, with technologies all converging at the same time—a predictive maintenance approach to reliability and asset performance means that root cause analysis (RCA) could be a thing of the past. Machine learning takes into consideration the whole history of failures and identifies the signs of failure in advance.
Commvault has announced that Emirates Steel has implemented Commvault HyperScale to support its digitalisation ambitions and safeguard its manufacturing operations.
Headquartered in Abu Dhabi, Emirates Steel is wholly government owned. At full capacity, its 11 plants produce 3.5 million tons of steel products, such as sheets, beams, and reinforced bars, every year for the construction industry. The company’s digitalisation efforts include moving its SAP modules to the cloud through Microsoft public cloud solutions.
“Backup was a challenge with underlying technology scattered across different environments and running on aging Dell hardware,” said Mohammed Azam, IT Infrastructure Head at Emirates Steel “I initially liked Rubrik’s simple interface but realised, at the Commvault GO event, that Commvault HyperScale proved a more effective solution with an interface that was just as user-friendly but with the critical difference that we installed it easily and it works perfectly across our complex environment.”
Commvault HyperScale and Commvault Complete Backup & Recovery protect 400 terabytes of data hosted across SAP systems, and including SQL databases, email archives, and 20 virtual machines. “Commvault HyperScale is easy to install and use,” said Azam. “Interoperability with both public cloud and on-premises environments means we can make IT investment decisions that boost our competitive advantage without having to worry about backup.”
Two Commvault HyperScale clusters replicate data between the company’s data center and disaster recovery site to provide robust business continuity capabilities. “Commvault gives us confidence that we can recover rapidly from any scenario, including potential ransomware attacks,” said Azam. “We can now restore a critical database in less than 90 minutes compared with three hours previously.”
Commvault also helps accelerate Emirates Steel’s digital roadmap by making it simple to add new services and datasets. “Any disruption to our operational systems and the production of steel would have a national impact. By maximising data availability, we can boost efficiency and safeguard the manufacturing supply chain,” concluded Azam.
“We are proud to expand our longstanding relationship with Emirates Steel by adding our latest HyperScale functionality and flexibility,” said Wael Mustafa, Area Vice President Middle East, South Africa & Turkey at Commvault. “Our HyperScale solutions are offering increased data availability and business continuation assurance to many of the largest organisations across the region.”
Makino Asia, a leading provider of machine tools used across various industries including automotive, aerospace, medical, semiconductor and electronics, has recently showcased its smart factory at its regional headquarters in Singapore. The facility is designed to meet the growing demand for high-quality products and sophisticated precision engineering capabilities in Asia by adopting Industry 4.0 and the principles of Industrial Internet of Things (IIoT). The smart factory consists of an assembly factory and state-of-the-art machining factory, leveraging seamless automation and digital technologies to achieve high levels of productivity and connectivity between its robots, machines and other peripheral systems.
The combined facility is expected to increase machine production capacity to almost double its previous capacity. The new machining factory and existing assembly factory are connected by a link bridge for staff, and a canopy area for the transfer of materials between the two factories using automated guided forklifts (AGF).
The facility is also fitted with energy-saving and efficient solutions: green energy from installed solar panels within the compound helps to generate about 2,400 megawatt hours of energy annually. This is equivalent to taking 200 cars off the road, avoiding 1,000 tonnes of carbon dioxide equivalent emissions over the same period. In the machining factory, a chilled ceiling system is used to ensure maximum energy efficiency of its air-conditioning system while maintaining high quality, reliability and optimum performance of Makino Asia’s manufacturing operations.
Neo Eng Chong, CEO and President of Makino Asia said, “Makino strives for a ‘Quality First’ mindset across the organisation, from the manufacturing of our products to the development of our people and the business. We are extremely proud of our expanded smart facility in Singapore that will enable Makino Asia to better support our Customers in the region and Singapore’s vision to become a global Advanced Manufacturing hub.”
He added, “The automation and digitalisation of the entire facility serves as a way for us to achieve increased productivity, capacity or energy efficiency. More importantly, it embodies our vision to provide more than just machines for our Customers, by providing the most effective and efficient solutions that meet their needs. The establishment of the IoT Centre to provide real-time support is another milestone to enrich partnerships with our valued Customers.”
The monitoring and tracking of machine conditions in real-time enables Makino Asia to provide proactive and predictive services to Customers. This ensures optimum machine performance at all times so that Customers are able to consistently deliver high quality products.
Lim Swee Nian, Assistant Managing Director of the Singapore Economic Development Board said, “Global precision engineering manufacturing leaders are accelerating the adoption and deployment of Advanced Manufacturing technologies from Singapore, to better serve the evolving needs of their Customers. We are pleased that Makino will be deepening its 45-year presence in Singapore through the launch of its digital transformation journey. As Makino Asia focuses on building its Industry 4.0 capabilities to develop and scale new solutions, we are confident that it will create value-added roles and upskilling opportunities for Singapore to succeed in the digital manufacturing economy.”
Makino Asia embarked on its digital transformation journey in 2016 with a plan to invest around S$100 million over five years to expand and boost the capabilities of its facility in Singapore. The company also established two new departments focused on automation and digitalisation to catalyse digital transformation in the company.
Besides having “smart” machines and solutions, Makino is committed to upskilling all its employees to keep up with fast and ever-changing developments in the manufacturing landscape. Makino Asia’s new and current employees undergo a Workforce Transformation program focused on equipping them with automation skills, digital literacy skills and safety skillsets. The courses are mandatory for all employees to keep abreast of the digital technologies being used to manage automated equipment.
The manufacturing sector in Singapore remains a key pillar of Singapore’s economy. It accounts for around 21 percent of Singapore’s nominal Gross Domestic Product (GDP) and 14 percent of the total workforce. Rapid technological advancements and digitalisation are changing the face of manufacturing. Developments in Advanced Manufacturing presents opportunities for companies to leverage on new technologies to drive productivity and growth.
Asia Pacific Metalworking Equipment News is pleased to conduct an interview with Wagner Turri, Sales Leader Southeast Asia & Taiwan at Hypertherm, regarding current trends in the metal cutting industry.
Could you provide us with an overview of the trends that are shaping industrial cutting in the metalworking industry?
Initial industrial trends could foresee more challenging times for the regional metalworking industry, in which competitiveness will be driven by customer’s needs and prompt feedback for opportunities and improvements. In this scenario, industrial automation and digitalisation will be the key drivers of change, and it would be more demanding in Asia Pacific due to the future economic growth and competitive landscape. It will push the metalworking industry to new arenas, where product quality is considered a ‘standard’ feature and customers’ requests are influenced by positive experiences in their interaction with these products, services, or solutions.
In this full perspective, industrial automation and digitalisation will help the metalworking industry understand and improve the performance of any equipment throughout its life cycle. This includes production effectiveness leverage based on new sets of equipment and technologies—which can provide real-time feedback on performance and propose necessary adjustments.
Over the last few years, we have seen a growing number of solutions that encourage the introduction of automation and digitalisation to the metalworking industry. Technologies that are related to the Industrial Internet of Things (IIoT) are enabling companies to build up smarter job shops, and allowing the industry to establish a smart machinery eco-system.
What are the latest technologies developed by Hypertherm to keep up with these trends?
Automation and digitalisation embedded on the IIoT platform have given manufacturers enhanced equipment and process capabilities, while staff aim to improve production effectiveness with additional cost management. In the last three years, Hypertherm has been addressing these industrial demands and trends with the development of a new set of plasma source and controllers, and by improving on-time operations support to customers. Our new solution — the X-Definition plasma source and NC industrial controller—provides real-time feedback on performance to job shops via a WiFi connection. This WiFi connectivity enables metalworking job shops to connect to these machines with a single device (e.g. smart phone or computer) to collect data on machine performance and maintenance. In addition, our new set of NC industrial controllers can receive cutting nesting jobs through WiFi. Furthermore, Hypertherm employs the most advanced communication protocol (i.e. EtherCAT) to provide faster information flow when our products are integrated with an automated solution, such as a NC plasma machine, for straight or bevel cutting, or a plasma robotic arm for 3D cutting or pipe cutting.
These continual technological advances elevated Hypertherm’s plasma cutting capabilities. Furthermore, our wide range of solutions for automation and portability include new sets of our robotic cutting tools and applications, delivered by our new rotary sleeve mechanical solution and the introduction of our off-line robotic software. It is relevant to highlight that all these new technologies rely on plasma source architecture. In this way, Hypertherm offers unmatched cut quality and precision (up to ISO 9013 Range 2) through our latest X-Definition class plasma system. This solution offers users reduced operation costs with its new electronic feature that extends consumable life, avoiding premature damage or misuse. With its new process technologies that deliver high cutting performance at optimal costs, the X-Definition plasma system is a stellar example of how we are able to address the changes ahead for manufacturers.
What are some challenges faced by this industry?
The traditional metalworking industry is in the throes of digital transformation, which is accelerated by exponentially growing technologies on a smarter machinery eco-system. These new eco-systems are covered by offerings or needs such as smarter robots, predictive analytics, additive manufacturing, artificial intelligence, predictive maintenance feedback, and collaborative manufacturing. These companies and their industrial processes must adapt to this rapid consolidation that has been happening the last few years. The industry needs to unleash new possibilities offered by the IIoT platform. This usage will transform operations and processes into new ways of conducting business, such that it becomes more scalable, profitable, sustainable, and environmentally-friendly.
The rising expectations and demand for better customer experience is also another challenge that the metal cutting industry must face. More and more, product quality is becoming a given, or a ‘standard’ feature. Customers’ expectations are shifting and they are beginning to value the experience delivered over their project life cycle. Soon enough, this will become a crucial priority and businesses will redirect their focus from merely selling products and services to creating an exceptional overall customer experience.
How can they be overcome?
To achieve their growth targets in a more complex and competitive environment, the metalworking industry will increasingly see the need to prioritise their capital expenditures, to spend on technology that will enable their businesses to be more agile — by increasing productivity, speed, responsiveness, and connectivity.
These capital expenditures must be followed-up with a compelling analysis of operational expenditures, which needs to bring operational costs reduction to justify investments on automation and digitalisation. In this perspective, Hypertherm is totally aligned to metalworking industry trends. We are a company focused on helping our customers reduce operating costs with additional cutting performance improvements. That way, they can enhance their profitability and business sustainability. Our continued investment in research and development is part of our mission to bring more breakthrough technologies to the market, so that we will keep delivering with new launches in coming years.
Moving forward, what do you think is the outlook of the metal cutting industry in the next five to 10 years?
Automation and digitalisation will definitely still play a big part in bringing the metal cutting industry to new heights. An increasing number of manufacturers will develop, adopt, and implement technologies in their industrial processes, where their equipment can effectively interact in a collaborative and smart eco-system. Customers will see more usage of software solutions in order to keep their hardware in a high-performance state. To achieve growth, manufacturers need to become digitally savvy and develop new, successful innovations in the ever-changing landscape of the metal cutting industry.
Leveraging intelligent manufacturing as the key to Taiwan’s economic revitalisation, Grundfos introduced iSolutions – a product range with a focus on digitalisation and connectivity – at this year’s Taipei International Machine Tool Show (TIMTOS).
In line with the government’s ambition to transform Taiwan into a global manufacturing hub by pushing industry adoption of smart machinery, Grundfos showcased a number of machine tool products under its iSolutions range. These products are intuitive and connected solutions that feature intelligent monitoring and adjustment features, which in turn optimise performance of the entire water system.
A key product of the iSolutions range is Grundfos’ E-motor, which comes with a built-in frequency converter and sensors that enable the motor to intuitively control pressure from the pump to match the system’s demand, ensuring optimum levels of operation at all times. This drives significant financial and energy efficiencies, in contrast to conventional systems that tend to run at constant speed and pressure throughout their operations, regardless of fluctuating demands.
With strengthening competitiveness and sustainability being key to the local machining industry’s projected growth of eight percent each year, Eric Lai, Grundfos’ Global Business Director, Machining Industry, said, “Grundfos is committed to helping Taiwan achieve its goals for intelligent machinery to enable manufacturers to offer more value-added services at greater productivity and environmentally friendly levels. The move towards Industry 4.0 is picking up pace, and it is crucial to consider new innovations that can greatly improve efficiencies.”
“With pumps accounting for 10 percent of the world’s electricity consumption, the opportunity to leverage Industry 4.0 to integrate intelligence into pump manufacturing and reduce both financial and environmental costs is unprecedented,” Eric Lai added.
According to Grundfos, the potential of intelligent pumps in the iSolutions range can help companies save energy by 40 to 50 percent
Grundfos Taiwan’s General Manager, Shih Hung Lin, said, “As global demand for Taiwan exports continue to rise, we anticipate smart development to be Taiwan’s next growth engine. iSolutions will be a key driver in the machining industry’s efforts to transform production to perform more effectively and efficiently.”
Grundfos’ CM-L pump, the latest variant the compact CM range was also launched. The new CM addition operates without a shaft seal, effectively eliminating the cause of pump downtime that could occur due to leakages as a result of mechanical seal failure. With the wear-and-tear of mechanical seals being one of the top causes for leaking pumps, the CM-L pump is expected to drastically reduce incidences of downtime, repair and maintenance.
It is worth noting that the CM-L is also a low-noise pump, due to the removal of the traditional motor fan and is ideal for Laboratories, IT servers and Data Centers. This feature also has strong appeal to a wide range of applications in the industrial sector, such as wire cutting.
Asia Pacific Metalworking Equipment News is pleased to conduct an interview with Hendrie Viktor, Regional Director at ZEISS Southeast Asia regarding current trends in the manufacturing and metrology industry.
1) Could you provide us with an overview of the current trends regarding the manufacturing industry in Asia?
In an attempt to soften the effects of globalisation, productivity and quality gain drives are most evident. Competing with neighbouring companies are no longer enough to secure one’s business interests. Through globalisation and commoditisation to some degree, the bar on price and quality has been raised exponentially. As a result, some manufacturing industries were adversely affected by consolidation. In my opinion, Asia in particular has been subjected to this harshly but responded well over the past decade—a great example are the quality gains on “Made in China” over the last few years. The relentless expectations on price competitiveness and quality standards has reached a point where traditional, incremental cost and quality gains are no longer enough and reaping the benefits of smart manufacturing or industry 4.0 is crucial.
2) To keep up with these manufacturing trends, what are the newest developments or technological advancements in ZEISS’s metrology solutions?
We address our customer’s ever-increasing productivity and quality requirements through solutions that enable manufacturers to inspect or measure faster and more frequently than before. Gone are the days of random sampling in a quality lab. In-process inspection and shop floor metrology have brought significant time savings and quality gains. Multi-purpose measuring instruments have replaced the need for multiple set-up’s, and workflow solutions have brought insights into manufacturing processes and quality that were previously unseen.
ZEISS Industrial Quality Solutions has been and still is at the forefront of the inspection and dimensional metrology transformation and plan to keep it this way moving forward. We continue to make significant investments, at least 10 percent of our revenue, into R&D annually in order to continue to deliver market-shaping innovations.
3) With increasing digitalisation of the manufacturing sector, what are the main challenges faced by the metrology industry?
Firstly, the sudden shift can be overwhelming and we’ve seen countless processes being digitalised for the sake of it—with huge amounts of digital data being collected, but not put to good use. Determining where, when and how frequently digital data needs to be collected as well as how it will be put to valuable use is crucial but it remains a great challenge for many since skill shortages in the field of digitalisation exists. There is also data and platform incompatibility, or rather standardisation hurdles to overcome as suppliers mostly develop their own Industrial Internet of Things (IIoT) platforms. Lastly, data handling and security still deters many companies from taking that leap.
4) How do you think these challenges can be overcome?
Relevant education and continued learning will go a long way towards addressing hesitation and will help ensure digitalisation efforts pay off. I see the need for industry and universities or technical schools to work hand in hand. That will stimulate the need for faster adoption. Alliances between machine manufacturers can address platform and standardisation issues to unlock IIoT benefits. Such an example can be seen in the recently founded ADAMOS alliance, of which ZEISS is a founding member of.
5) Moving forward, where do you think the industry is headed in the next five to 10 years?
With the pace of today’s change, it would be difficult to even predict this with some degree of certainty. I think the value-add from productivity and quality gains through digitalisation and new manufacturing technologies such as 3D printing is going to be tremendous that consolidation is going to happen on a much broader scale. I see low volume, high mix through flexible manufacturing becoming a norm and thus bringing manufacturing closer to the end user, further reducing non-value-added costs. This will call for a very different approach to metrology.
The world is going digital. That doesn’t just change how we work and learn and shop, or what we do for fun; it changes how products are manufactured. Even the smallest machinery suppliers have to be able to create modular, customisable, multi-function designs that can be tailored to exactly what their customers need to build their products. Article by Frans Adamowicz, Solutions Director for Industrial Machinery and Heavy Equipment, Siemens PLM Software.
But that level of flexibility makes it hard to reuse designs because the requirements for each customer will be very different, and very specific each time, turning every project into a custom, one-off design. How do you get that flexibility without raising costs?
Industrial machinery manufacturers also have to be able to show customers, even before they deliver new machinery, how it should work and how it will integrate with other systems; that way, they can prove it will have the high ROI and low total cost of ownership product manufacturers are looking for. They have to comply with ever more regulations that cover the entire lifecycle of machinery, from energy efficiency in use right through to the eventual cost of reuse, recycling or disposal – and document that all the relevant regulations were met. And they have to be able to build and deliver these designs faster than ever to compete with new low-cost suppliers around the world, while still taking the time to understand exactly what the customer needs and how they’re going to use it.
To cope with these pressures from customers, competitors and regulators they need integrated tools that get rid of siloes because there just isn’t time to design, build and commission a machine as separate steps that move through different departments. A sequential process like that isn’t just slow; it runs the risk of losing information and introducing errors every time a design moves from one department to another. And it doesn’t reflect the realities of modern manufacturing.
Designs are getting more complex to accommodate; they might have sensors to monitor machine performance and output, and networking to connect multiple machines into a unified manufacturing system, alongside both software-based and physical controls. That complexity affects the physical design. You can’t afford to waste time at the end of a project redesigning a part or an assembly plan because the wires and cables can’t be connected correctly. When you’re testing the physical machine is far too late to discover that, for example, the control software doesn’t take mechanical limits into account.
Solution To Complexity of Designs
In fact, with so much software, automation and electronics in modern machinery, getting a design right needs a mechatronic design platform that handles much more than just mechanical CAD because tackling a problem will often require being able to work in multiple disciplines at the same time. The solution is investing in new digital technologies to create a digital thread of information that connects all the departments involved in a project and runs through every stage of gathering requirements and creating specifications, through design, development, production and commissioning, to delivery, support and monitoring in production.
This digitalisation lets you create digital representations of the smart, connected, custom machinery that customers are demanding. These ‘digital twins’ are immediately useful because you can use them to sell new machinery even before it’s built. Once you’ve made the sale you can hand over a digital copy of the real machine, so customers can prepare to install and integrate it with their existing systems, while you use the digital twin to build the new machines faster and with fewer mistakes or delays in commissioning, because you have a functional model that mechanical, electrical and automation teams can all work from together.
Creating that digital twin requires next generation design tools that go well beyond mechanical CAD to support a multi-disciplinary process that includes modelling, mechatronics, simulation, testing and performance validation. The right design tools can also make it easier for manufacturers to reuse existing modules for these very specific new orders.
Technologies To Simplify
New modelling technologies like generative design and topology optimisation find the best designs for components using constraints like maximum stress, size, weight and displacement, improving the performance and reliability of machinery. Single parts created with additive manufacturing could replace complex assemblies of precision components, as well as saving on weight and materials costs. Synchronous Technology makes it faster and simpler to create and change geometries while preserving design decisions, like keeping mounting points aligned or having the outer surfaces of a part stay parallel. Convergent Modelling Technology lets you work directly with facet and mesh models, without reverse engineering, alongside traditional CAD geometries. Rather than redesigning similar parts from scratch every time and increasing the amount of inventory you need to hold, you can reduce costs by incorporating existing components in a new design or designing a replacement that can be used in multiple projects.
Use mechatronic design alongside these modelling tools and you can validate design ideas quickly. Experiment early in the product development cycle, confident that you can see not just how a design looks but also how it will work, using physics-based interactive simulations of joints, actuators, motion, collision behaviour and other dynamic and kinematic properties.
For example, the faster operating speeds customers are asking for can actually lower production capacity if vibration at these faster speeds causes process inconsistencies or shortens the life of key components. Fully simulating operation of the machine running at higher speed will reveal the problem before the production line fails, and engineers can redesign parts to control noise and vibration, rerun the simulation to ensure the new design can run at full speed – and pass the details to the automation engineers to validate their machine operation routines without waiting for the new physical part. Integrated tools that use the digital thread allow you to take steps to improve the design of the machinery without slowing down the overall project.
Digital twins continue to pay a dividend once the machinery is completed and delivered, because manufacturers can rely on the accurate digital representation to deliver any necessary after sales support more cost effectively. Streaming information from sensors in the machinery to monitor performance is an opportunity to deliver improvements later on, building brand loyalty, improving service revenue with predictive maintenance and uncovering needs customers don’t yet realise they have. Taken to the next step, the digital twin can lead to a whole new business model as a consulting solution provider where you make sure customers get the value from the expensive, custom machinery you’ve created for them with remote diagnostics, software maintenance and process optimisation, offering them the complete solution they’re looking for rather than an isolated machine.
The benefits of digitalisation can add up to a significant increase in production, creating more (and more efficient) machines with the same resources and either lowering costs or increasing output at the same cost. Think of it as a digital productivity bonus adding up to anything from 6% or even close to 10% of annual revenue. Investing in the next generation design tools needed to create custom machinery is the way to outpace commodity suppliers who can’t move this fast or deliver exactly what customers want, giving you a loyal customer base.
Asia Pacific Metalworking Equipment News is pleased to conduct an interview with Quah Beng Chieh, Head of Marketing (Asia Pacific) at FARO Technologies regarding FARO’s achievements for 2018, the company’s aims for 2019, and the trends that will shape the industry in 2019.
1. Can you sum up your company’s focus and achievements in 2018?
FARO is well-attuned to the industry’s trends and demands, and we continually invest efforts into developing new 3D measurement technology to cater to our customers’ needs. In 2018, FARO launched several cutting-edge measurement solutions that were developed with our customers’ challenges in mind. The 8-Axis Quantum FaroArm, the world’s only eight-axis portable metrology arm solution, seamlessly integrates with any FaroArm to enable operators to rotate a part in real-time, relative to the arm. When used in conjunction with the newly launched Prizm Laser Line Probe, which scans objects in high-resolution 3D color, users can speed up and simplify the inspection of dimensional and surface character quality issues for molded parts due to the Prizm’s true-to-life functionality. Another significant product launch is the introduction of the 6Probe for the FARO 6DoF Laser Tracker — a fully integrated hand-held probe for easily probing hidden, hard-to-reach features. Together, the patented FARO Super 6DoF and 6Probe total solution addresses a wide range of large scale metrology applications across a variety of manufacturing focused industries, including automotive, aerospace, construction, heavy equipment and shipbuilding. All these have contributed to significant revenue growth on over 2018, despite a poor economic environment.
2. What are your expectations on the regional economy in 2019?
According to a report by Grand View Research, the 3D metrology market is gaining importance due to an increasing demand for improved products and services across end-use sectors such as industrial, automotive, and power generation. This rise in demand can be attributed to growing adherence to international quality standards across the entire industry domain which has also encouraged greater demand for metrology equipment and services. Likewise, we are also expecting the Asia Pacific 3D metrology market to grow significantly due to continued economic growth in emerging countries like China, India and Southeast Asia.
3. What business trends in Asia capture your interest for growth next year?
The 3D measurement industry is constantly evolving due to increasingly complex market needs and requirements, and thus requires constant innovation to ensure a steady introduction of varied solutions. Digital transformation of the manufacturing industry continues to gain prominence, urging manufacturers to look for solutions that will allow them to digitise information and digitalise processes in order to improve their organisation’s response to market changes. Solutions with advanced technology that empower customers to tap on data-driven collaborations for improved productivity are also expected to rise to prominence in the market.
In addition to solutions that enable manufacturers to efficiently digitise product designs and relevant 3D measurement data, FARO will also continue to introduce solutions like the FARO Visual Inspect — which offers companies new opportunities for enhanced collaboration across departments and production processes. Using complex 3D data previously unavailable in a production line, and an augmented reality function that is suitable for all working environments, 3D measurement technology like the Visual Inspect can help manufacturers streamline their processes to be more flexible and nimble, while taking into account increasing cost pressures.
4.What do you think is the key industry trend to watch out in 2019?
Over the last decade, manufacturers’ measurement needs have grown to become increasingly complex as the designs of their products have become more complicated. This will likely continue to be true as manufacturers push boundaries in the product development process. Effectively, we expect that customers will require even more innovative, advanced technologies that meet their sophisticated measurement needs.
Manufacturers’ preferences are also shifting from off-line quality inspection to near-line or in-line measurement techniques in order to enable higher sampling rates and shorter inspection times. This will drive growth in the integration of CMMs and optical scanners with assembly lines for greater effectiveness, efficiency, and improved quality control.
5. What potential and opportunity do you see in the industry next year?
The manufacturing industry is ever-evolving. Customers today are much more aware of what they want and need, demanding improved efficiency and innovative products, and this trend is catalysed by the accelerated development in technology. Organisations, regardless of their size and shape, can survive and grow if they adapt quickly and stay abreast of the current manufacturing industry trends. To better equip our customers to do just that, FARO is actively working to offer solutions with advanced technology that allow them to enjoy greater efficiency and convenience. As the economy continues to grow in Asia, companies will seek to expand their operations, optimise to reduce cost, and expand capabilities to capture new markets. We expect a rise in manufacturers’ demand for measurement and imaging solutions to tackle their evolving metrology needs, and our team will be ready to respond by educating users across Asia, about our solutions and how their businesses can benefit from incorporating our technology.