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Would You Trust The Algorithm?

Would You Trust The Algorithm?

Imagine if you could automate some of the day-to-day operational decisions in your organization, so that your employees could focus on strategic projects, like developing new product lines or expanding the business. How good would an artificial intelligence (AI) model need to be, before you give it control? Would it, for example, need to equal the performance of human engineers, or demonstrate better performance? What if an error could cause significant financial losses or even human injury, how would this change your response?

A survey put scenarios like this to 515 senior leaders from the industrial world (including the energy, manufacturing, heavy industry, infrastructure and transport sectors) as part of a research into the uses, benefits, barriers and attitudes towards AI. Their responses offer a unique insight into the future of AI in industrial enterprises.

Heavy industry and heavy consequences

In these industries, many use cases for AI are expected to help avoid disasters and make workplaces safer. This is important because while AI methodologies are similar across industries, the consequences of failure are not. In many industrial organizations, bad decisions can leave thousands of people without a train to work; millions of dollars can be lost if machinery overheats; slight changes in pressure can lead to an environmental catastrophe; and innumerable scenarios can lead to loss of life.

It is therefore significant that a large set of respondents (44%) believe that, over the course of the next five years, an AI system will autonomously control machines that could potentially cause injury or death. Even greater numbers (54%) believe that AI will, within the same period, autonomously control some of their organization’s high-value assets.

To give AI such responsibility, industrial AI will need to become more sophisticated, and often this will be driven by new approaches to the way data is managed, generated, represented, and shared. For example:

  • Contextual data and simulations: Already today we see AI applied to data sets created and organized in new ways to enhance insights and understanding. Examples include knowledge graphs, which capture the meaning of – and relationships between – items in diverse data sets, and digital twins, which provide detailed digital representations and simulations of real systems, assets, or processes.
  • Embedded AI and big picture insights: Internet of Things (IoT) and Edge technologies are giving rise to diverse machine-generated data sets which can support new levels of situational awareness and real-time insights in the cloud or directly in the field.
  • Data from beyond the walls: Improved protocols and technologies for sharing data between organizations could support the development of AI models that simultaneously draw from the data of suppliers, partners, regulators, customers, and perhaps even competitors.

Context changes meaning

To take one example from the above, there is enormous potential in using industrial knowledge graphs to enhance AI models by combining different datasets. “Knowledge graphs add context to the data you’re analyzing,” explains Norbert Gaus, Head of R&D in Digitalization and Automation at Siemens. “For example, machine data can be analyzed in the context of design data, including the tasks the machine is made for, the temperatures it should operate at, the key thresholds built into the parts, and so forth. To this we could add the service history of similar machines, including faults, recalls and expected inspection outcomes throughout the machine’s operational life. Knowledge graphs make it much easier to augment the machine data we use to train AI models, adding valuable contextual information.”

The survey explored the kinds of contextual data that leaders believe would be most useful today. Data from equipment manufacturers came out on top, with 71% rating this as a major or minor benefit. This was followed by internal data from other divisions, regions or departments (70%), data from suppliers (70%) and performance data from sold products in use with customers (68%).

A company that uses knowledge graphs to bring different kinds of data together – such as product history, operational performance, environmental conditions – would be able to create a single AI model that drives better predictions, useful ideas, new efficiencies, and more powerful automation.

Building faith in algorithms

Ever more powerful applications will no doubt raise new challenges. It will require trusting AI with responsibilities that were only ever given to humans. In these cases, AI applications will need to win the confidence of decision-makers, while organizations will need to develop new risk and governance frameworks.

To explore these issues, the survey asked respondents to imagine several scenarios like the one at the start of this article. For example, 56% decided to accept the decision of an impressive AI model over an experienced employee (44%), where the decision would have major financial consequences. Is 56% high or low? One might think it is low considering respondents were told that the AI model had outperformed the organization’s most experienced employees in a year-long pilot. It suggests that the other 44% could have a bias towards human decisions, even when the evidence favors AI. You can read more about these and other important issues in the next-gen industrial AI research report.

Challenges aside, the research suggests an optimistic outlook for AI. As AI grows more sophisticated, leaders expect fewer harmful cyberattacks, easier risk management, more innovation, higher margins, and safer workplaces. Overall, with the promise of such a diverse and important range of positive impacts potentially on the horizon, there will be no shortage of motivation to overcome all challenges on the path to next-gen industrial AI.

 

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TRUMPF AI Assistant Optimises Sorting Process

TRUMPF AI Assistant Optimises Sorting Process

TRUMPF has developed a solution that makes life easier for machine operators by helping them sort sheet metal parts from a laser cutting machine. The Sorting Guide uses an AI solution to identify which part the operator has removed from the machine and displays all the required information on screen. This includes information such as the next step in the process chain, for example. The Sorting Guide improves efficiency at the interface between the laser cutting machine and intralogistics, especially when dealing with sheets containing parts from multiple different orders.

It helps machine operators optimise their workflows, for example by highlighting all the parts from one order in the same color on screen. At the same time, it enables the operator to keep tabs on the machine’s status when working at the pallet changer – and, as an option, even to keep an eye on the interior of the machine. That makes it easier for the operator to react quickly to any problems that might arise. The Sorting Guide automatically registers each part in the system as it is removed.

This eliminates the need to re-check the number of parts and manually update the part count in the control system. The digitally recorded data in the control system thus represent the real production progress in real time. The operator can see at a glance which parts are ready for further processing and where he may need to initiate post-production. The result is higher productivity on a day-to-day basis and more efficient overall use of the laser cutting machine.

Image Processing That Learns From Data

The Sorting Guide works using artificial intelligence. It consists of a camera and an industrial PC equipped with intelligent image processing software. Its neural network detects which parts the machine operator has already removed from the sheet. Unlike conventional image processing techniques, this network can be trained and improved on the basis of the data that is collected continuously. In addition, the database serves to open up further optimisation potential for users in the future. The Sorting Guide is currently available on systems running TruTops Fab or TruTops Boost software.

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TRUMPF And Fraunhofer IPA Research Alliance Ramps Up AI For Industrial Use

TRUMPF And Fraunhofer IPA Research Alliance Ramps Up AI For Industrial Use

TRUMPF and the Stuttgart-based Fraunhofer Institute for Manufacturing Engineering and Automation IPA have formed a research alliance that is set to to introduce artificial intelligence (AI) solutions for connected manufacturing on an industrial scale. Running until 2025, ten employees from TRUMPF and Fraunhofer IPA are involved in the project, which will receive some two million euros of funding spread over the next five years.

“TRUMPF’s mission is to further extend its AI leadership in sheet-metal fabrication. To that end, we have already started investing in the kind of future technologies that will drive major efficiency gains within our company and boost our competitiveness,” says Thomas Schneider, managing director of development at TRUMPF Machine Tools.

Five years have passed since TRUMPF and Fraunhofer IPA joined forces to work on smart factory topics, and the two partners will be continuing to pursue these existing projects within the framework of their new research alliance.

“TRUMPF has been working with us on connected manufacturing for years because they share our view that Industry 4.0 developments represent a major opportunity. Everything depends on what happens over the next few years – so these are exciting times! We expect the coronavirus pandemic to act as a kind of catalyst: those who are well prepared will be perfectly placed to exploit the huge opportunities that lie ahead. Soon we’ll see whether we have laid the right foundations for the future in our joint projects,” says Professor Thomas Bauernhansl, director of Fraunhofer IPA.

Future projects aim to make AI explainable

One of the goals TRUMPF and Fraunhofer IPA hope to achieve over the next five years is to develop solutions for better data quality in manufacturing. This reflects the crucial importance of high-quality data when it comes to achieving efficiency gains with AI. To address this need, the two partners will be increasing their research activities in the field of explainable artificial intelligence, or XAI. Their goal is to make the operation of neural networks interpretable. New findings in this area are of great benefit to sheet-metal fabricators. The results of this kind of data analysis can boost the quality of manufacturing, save time and cut costs.

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Siemens Revolutionizes CAD Sketching With AI Technology

Siemens Revolutionizes CAD Sketching With AI Technology

Siemens Digital Industries Software has launched a new solution for capturing concepts in 2D. The new NX Sketch software tool revolutionizes sketching in CAD, which is an essential part of the design process. By changing the underlying technology, users are now able to sketch without pre-defining parameters, design intent and relationships.

Using Artificial Intelligence (AI) to infer relationships on the fly, users can move away from a paper hand sketch and truly create concept designs within NX software. This technology offers significant flexibility in concept design sketching, and makes it easy to work with imported data, allowing rapid design iteration on legacy data, and to work with tens of thousands of curves within a single sketch. With these latest enhancements to NX, Siemens’ Xcelerator portfolio continues to bring together advanced technology, even within the core of modelling techniques, helping remove the traditional barriers users have experienced to dramatically improve productivity.

“The ability to make intelligent changes to 2D entities that one imports into the new sketcher is astounding,” said Steve Samuels, CEO of Design Visionaries Inc.

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With these latest enhancements to NX, Siemens’ Xcelerator portfolio continues to bring together advanced technology, even within the core of modelling techniques.

With these latest enhancements to NX, Siemens’ Xcelerator portfolio continues to bring together advanced technology, even within the core of modelling techniques.

Analysis has shown that in an average day or workflow, around 10% of a typical user’s day is spent sketching. In addition, within current design environments most concept sketching is happening outside of the CAD software due to the level of rules and relationships that must be decided on and built into the sketch by the user up front. Often designers in concept design stage do not necessarily know what the final product may be, which requires a sketching environment that is flexible and can evolve with the design. NX offers the flexibility of 2D paper concept design within the 3D CAD environment, as the first in the industry to eliminate upfront constraints on the design. Instead of defining and being limited by constraints such as size or relationships, NX can recognize tangents and other design relationships to adjust on the fly.

“Sketching is at the heart of CAD and is critical to capturing the intent of the digital twin,” said Bob Haubrock, Senior Vice President, Product Engineering Software at Siemens Digital Industries Software. “Even though this is an essential part of the process, sketching hasn’t changed much in the last 40 years. Using technology and innovations from multiple past acquisitions, Siemens is able to take a fresh look at this crucial design step and modernize it in a way that will help our customers achieve significant gains in productivity and innovation.”

 

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Top 21 Global Risks That Threaten The Next Decade

Top 21 Global Risks that Threaten the Next Decade

Frost & Sullivan has released a report providing a compelling analysis that will help stakeholders understand the impact of future risks through the short, medium and long terms, and equip them to act on clear growth opportunities. The study, Global Future Risks—Future-proofing Your Strategies, 2030, presents over 80 growth opportunities arising from 21 risks, which will help policymakers, businesses and individuals take immediate tactical and strategic actions and draw plans to lower impact.

Unpreparedness to address these challenges, which are looming across the globe, will adversely impact businesses, societies, economies, cultures, and personal lives.

“Risks are now increasingly interconnected, which, if amplified, can trigger a ripple effect across industries, regions, and diverse stakeholder groups. Therefore, mitigating them will involve measures to quantify relevant risks, estimate the probability of occurrence and the adoption of a cohesive, interdisciplinary, and multipronged approach,” said Murali Krishnan, Visionary Innovation Group Senior Industry Analyst at Frost & Sullivan.

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Krishnan added, “With an estimated 200 billion connected devices by 2030, the risk of sophisticated cyberattacks will escalate. Therefore, companies should invest in future-casting tools that help identify the impact of such risks to their business, thereby detecting vulnerabilities and avoiding disruptions.”

“Over 40 million Americans are at risk of flooding from rivers, nearly 300,000 have lost their lives to the COVID pandemic, and close to $1 billion in costs will be borne annually by economies as they become victims to cyberattacks. The stakes are high, claiming both lives and resources. Businesses must look to invest in early warning systems that help future-proof them against the known and the unknown. Understanding risks and its levers can become a growth opportunity if used as a tool to create value,” noted Archana Vidyasekar, Visionary Innovation Group Research Director at Frost & Sullivan.

To tap into growth opportunities emerging from the short-, mid- and long-term risks, organisations can focus on the following areas:

Short-term risks

  • Privacy and disinformation risks: Organisations must adopt a “privacy-by-design” approach, which goes beyond accepted privacy standards and assumes global regulatory compliance.
  • Rise of infectious diseases:Advanced technologies such as next-generation sequencing (NGS) and CRISPR-based diagnostics will enable new approaches in the treatment of infectious diseases.
  • Water crisis:Innovation in agricultural practices such as vertical farming and optimised crop selection can play an important role in reducing the ongoing water crisis.

Mid-term risks

  • Urbanisation: Urbanisation stress will trigger the demand for smart solutions such as intelligent grid control and electrification, smart buildings, and smart storage solutions.
  • Climate change risks: Organisations will emphasise investments in buildings with net-zero energy consumption, and greenhouse gas emissions will increase minimally.

Long-term risks

  • Artificial intelligence (AI) as a threat: AI can be a strong reason for the polarisation of jobs across the world, but investing in collaborative computational capabilities can help allocate an ideal division of tasks between humans and robots based on their distinct capabilities and deployment costs.
  • National identity crisis:Great business opportunities exist in providing digital authentication of eGovernment services such as digitisation of birth, marriage, and death certificates, passports, and driving licenses.

 

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C-Com Projects With MARPOSS And Oerlikon Balzers To Introduce Intelligent App For Tooling

c-Com Projects With MARPOSS And Oerlikon Balzers To Introduce Intelligent App For Tooling

Step-by-step networking for in-house manufacturing, involving suppliers and customers and efficiently using data together – the digital services provided by c-Com, a member of MAPAL Group, make it all possible. However, the start-up isn’t just developing its own applications. It’s also generating added value for customers by working closely with cooperation partners.

Cooperation with MARPOSS: reduced setup times and maximum tool service life

The optimal and longest-possible use of tools represents a vital cost factor for machining companies. But compromises are often necessary – particularly in series production and as part of automated processes. Tools with a defined tool life are replaced as soon as the specified tool life has come to an end. In many cases, though, the tool has not truly reached the end of its tool life and replacement is not yet necessary. However, companies play it safe to avoid quality issues and the risk of producing items that later need to be rejected.

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This is one of the elements addressed by the ARTIS GENIOR MODULAR module by MARPOSS. The fully automatic tool- and process-monitoring system has been an established feature of the market for many years. It works by recording various measurements and assessing them on the basis of several criteria.

MARPOSS recently launched a collaboration with c-Com GmbH and its c-Com open cloud platform to provide module users with additional value: the ARTIS GENIOR MODULAR module and c-Com are set to exchange data. Once the defined tool limits have been reached, the staff member responsible receives a notification on their mobile terminal – which is made possible by the cooperation with c-Com. As a result, operators can react more quickly and boost the efficiency of their manufacturing processes.

Cooperation with Oerlikon Balzers: transparency and sustainability thanks to digital processing for coating

Many tools are re-sharpened and re-coated to make production as cost-efficient as possible and to use raw materials sustainably. This procedure is very complex for everyone involved – from the machine operators to the staff members carrying out the re-sharpening and coating. If a staff member responsible for re-sharpening sends a tool for coating, this staff member is often not aware of corresponding order status. This results in frequent queries. In some cases, the number of re-sharpening processes is simply marked on the tool shank. Overall, the total benefit is reduced by the very high investment of time and effort required.

READ: Coating Processes with Increased Material and Energy Efficiency

In cooperation with Oerlikon Balzers, c-Com has developed an application that enables significantly more effective and transparent order processing. The prototype was showcased at EMO Hannover. The only prerequisite to benefitting from the advantages of digital processing for coating is identifying all tools with a unique ID.

The c-Com application exchanges data with the myBalzers customer portal run by Oerlikon Balzers. This way, the entire order process is digitalised, and all receipts are available online. It is easy to share documents such as delivery slips, invoices or order confirmations, and the status of each coating order can be viewed in real time. There is no longer a need to ask for order updates – a quick glance at the application provides the user with all the information they need. On top of this, machine operators have access to all the important information about their tool at all times. Thanks to the collaborative approach by c-Com, they can access all data via the cloud.

The c-Com wear detection app: a technical advisor in your pocket

c-Com has developed a wear detection application to provide answers to these questions. The prototype for the application was presented at EMO Hannover. The application is very simple to use: first, the worn blade is documented using a smartphone and a conventional auxiliary lens for zooming in. The app then identifies the type of wear and suggests corresponding recommended actions. This allows users to prevent this type of wear in future.

The application is based on machine learning, a sub-category of artificial intelligence. This means that the application uses datasets to learn. Together with tool specialists at MAPAL, c-Com has compiled and categorized hundreds of images. Effectively, the algorithm was trained by being shown what different types of wear look like, allowing it to assess whether or not a blade is in good order.

 

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Artificial Intelligence In Bending

Artificial Intelligence In Bending

Manufacturers are now adopting artificial intelligence (AI) to further create value for the customers. But how would AI be applied to sheet metal bending? In this article, Melvin Tham, Regional Technology Expert – Bending, for TRUMPF, explains.

Using conventional press brakes to achieve high accuracy for sheet metal is challenging due mainly to the property of the material, where its elasticity varies according to its composition and grain direction. Therefore, the process would usually take a longer time as it requires more knowledge and skill in order to achieve higher accuracy.

In today’s industrial environment, machines are loaded with functions to ensure that the manufactured parts are precise and consistent with minimal human/operator intervention, and manufacturers are now adopting artificial intelligence (AI) to further create value for the customers. But how would AI be applied to sheet metal bending?

Automatic Set Up

Given the current high-mix, low-volume market demand, the system must be easily set up within minutes to cater for a job change over. Therefore, a self-centring tooling system would be most ideal. With an automatic tool changer, there is no longer a need for alignment as the tools are automatically placed in position and integrated into the machine. It has three to four times more storage capacity than the machine’s bending length, all just to ensure a quick changeover and without the hassle of tool shortage.

Positioning and Angle Accuracy of Part

Since the bending process is now automatic, the quality of the parts has to be checked automatically as well. Such system would require high dynamic functions such as the backgauge. The backgauge with an axis tolerance of ±0.02 mm and the angle sensor tool with tolerance at ±0.5 deg are required to ensure that the part is placed accurately in position and angle tolerance is achieved by an angle checking device.

Sensors of the backagauge are necessary for the identification of the part in position. Without this, the part would not be able to achieve its desired flange length.

An automatic detection of the angle needs to be equipped to determine the correct angle to be achieved for each bend. With Automatic Controlled Bending (ACB), the total completion time to bend, calculate and adjust will take less than a second!

Identification of Parts and Positioning Compensation

The system must be able to detect the correct part to pick up and automatically determine the datum point to compensate positioning error. It is important to define the datum point so that all bending sequence and positioning accuracy can be referenced.

Although a structured stand that pre-fixed the part datum point can be achieved, the best possible solution will be with a high-resolution and precise camera profile detection that is flexible and automatic. This camera device could detect the sheet stack, height and fine profile of the part for single sheet without the need to specifically prepare sheet in a fixed position. With such function, a lot of time is saved from the preparation for defining, picking and loading of parts.

Gripper Technology

The grippers picking up the parts are of critical importance as well. Our grippers are designed with the concept of holding the parts as firmly as a human hand would. The gripper can be used for multiple parts and the suction cups can be pneumatically turned on or off to cater to different profiles and gripping area.

CAM-assisted Offline Programming

Software plays a very important role in automation. It should be able to strategically control all movement offline with intuitive graphical teaching.

In the past, robot movements are codings that are entered line by line in order to perfect a smooth travel path. With advanced software like TruTops Bend Automation, not only are we are able to graphically teach the movement from one point to another, we can also teach the robot to flip, load and unload the part. The software enables us to run a simulation prior to the actual process.

Robotic Movement and Payload

There are many robotic equipment in the market, with some having more than eight axis of movement and payload of more than 1,000 kg! So how do we know which is suitable?

In bending, it is always the working area within the press brake and robotic system. The bigger the working capacity means there is a better flexibility on the type of profile that can be bent.

The longer the trackway of the robot arm, the more parts can be prepared for loading and unloading. This is to ensure that the machine is always filled with part for continuous production and not idling or waiting for parts. There are also possibilities that the finish part can be stacked in cage or drop box.

The higher the payload means a bigger robot arm would be required. When the arm gets too big, there is a minimum distance of limitation due to the kinetic movement, therefore small parts cannot be picked up. Hence, it is important to define the size of the product before the selection of the automatic bending cell. This will make it easier to select the type of press brake and robotic arm for the job.

With all the necessary functions that are in place to ensure the output quality of the parts, the production is all ready for artificial intelligence bending!

 

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Enterprise Artificial Intelligence Revenue Will Reach $107.3 Billion Worldwide By 2025

Enterprise Artificial Intelligence Revenue Will Reach $107.3 Billion Worldwide By 2025

Artificial intelligence (AI) is rapidly entering a new phase within the enterprise market, with an increasing number of businesses leveraging AI to turn the massive amounts of process, operational, and transactional data being collected into actionable insights that can improve the way they run their businesses as well as improve customer interactions, according to a new report from Tractica.

Based on the number and variety of pilot programs, proof-of-concept (PoC) demonstrations, and commercial deployments of AI technology already being publicised by enterprise customers around the globe, it is clear that AI is not a fad, but a key part of the technology landscape of today and tomorrow. Tractica forecasts that annual revenue for enterprise applications of AI will increase from $7.6 billion worldwide in 2018 to $107.3 billion in 2025.

“No longer is the discussion of AI limited to science fiction, autonomous vehicles, or Siri; AI is being deployed across a multitude of industries and use cases with enterprises leading the way,” says principal analyst Keith Kirkpatrick. “Thanks to the use of template-driven AI platforms, even a small pilot program can demonstrate real-world benefits. As enterprises are realising, the benefits of AI are even greater when the technology is scaled across the entire organisation.”

Tractica’s report, “Artificial Intelligence for Enterprise Applications”, examines the practical application of AI within commercial enterprises, providing a comprehensive analysis of use cases, business models, market drivers and barriers, technology issues, and the evolving market ecosystem.

 

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Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

According to Tractica, global revenue for digital twins will increase to $9.4 billion in 2025, up from $2.4 billion in 2018. A digital twin is a digital representation that provides the elements and dynamics of how a device or ecosystem operates and lives throughout its life cycle. Digital twins are useful for simulating the capabilities of machine tools in a safe and cost-effective way, as well as identifying the root causes of problems occurring in physical tools or infrastructure.

The digitisation of nearly every industry type is helping to fuel the demand for twinning platforms, as is the desire to monitor, control, and model the future behaviour of real-world equipment, systems, and environments. Manufacturing, aerospace, connected vehicles, smart cities, retail, healthcare, and industrial IoT are key sectors for digital twins market adoption. Asia Pacific is one of the largest geographic regions for digital twins, forecasted to generate $11.2 billion in cumulative revenue.

“Like any technology, digital twins must be understood and accepted by several different stakeholders, from the operations workers up to the C-suite,” said Principal Analyst Keith Kirkpatrick.

“Vendors are highlighting their expertise in analytics and demonstrating domain expertise with specific industry verticals. Some are also spotlighting their experience with incorporating artificial intelligence (AI) and machine learning (ML) technologies, which can provide the ability to model future behaviour via digital twins. These technologies are anticipated to drive the functionality of digital twins beyond simply being enhanced analytics tools,” he added.

 

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Gripping And Clamping Solutions For Process Automation

Gripping and Clamping Solutions for Process Automation

In this interview with Asia Pacific Metalworking Equipment News (APMEN), Vincent Teo, general manager of Schunk, talks about the gripping and clamping challenges that their customers are facing, and how they are helping them address these issues. Article by Stephen Las Marias.

Schunk is one of the leading providers of clamping technology and gripping systems worldwide. Founded in 1945 by Friedrich Schunk as a mechanical workshop, the company has grown to become what it is today under the leadership of his son, Heinz-Dieter Schunk. The company is now under the leadership of siblings Henrik A. Schunk and Kristina I. Schunk, the company founder’s grandchildren.

Schunk has more than 3,500 employees in nine production facilities and 34 subsidiaries as well as distribution partners in more than 50 countries. With more than 11,000 standard components, the company offers the world’s largest range of clamping technology and gripping systems from a single source. In particular, Schunk has 2,550 grippers—the broadest range of standard gripper components on the market—and its portfolio comprises more than 4,000 components.

Based in Singapore, Vincent Teo is the general manager of Schunk, where he is responsible for the Southeast Asia market, including Singapore, Indonesia, Thailand, Malaysia, Philippines, and Vietnam. In an interview with Asia Pacific Metalworking Equipment News (APMEN), Teo talks about the challenges that their customers are facing, and how they are helping them address these issues. He also talks about the trends shaping the clamping and gripping market, and his outlook for the industry.

APMEN: What is your company’s ‘sweet spot’?

Vincent Teo: Schunk understands the needs of manufacturing companies, which have assembly, handling and machining processes. Our products can apply in multiple manufacturing sectors.

APMEN: What sort of challenges are your customers facing?

Teo: Today, businesses face the challenge of getting skilled workers—and staff retention for many industries is becoming a struggle. This is even more severe for countries such as Singapore, which depends on foreign workers. If automation can help reduce these problems and improve work conditions, then more high-value jobs can be created.

APMEN: How is your company helping your customers address their problems?

Teo: We work together closely with our partners such as robot manufacturers and system integrators, and we aim to reach out to more customers to help them see the benefits of automation.

APMEN: What forces do you see driving the industry?

Teo: Collaborative robots, or cobots, have revolutionized many applications that were impossible to think of over a century ago. Less complicated programming equates to less man-hour training, making it cheaper for businesses to adopt robotics. This is game changer, and Schunk is working with the major players in this new era of robotics.

APMEN: What opportunities you are seeing in the Asia market for robotic clamping industry?

Teo: The trend towards automated loading on machining by robots is picking up in recent years. The company is well-positioned to support this growing demand with immediate solutions.

APMEN: What about the challenges in the region? How do you see the trade war between China and the US affecting the manufacturing industry?

Teo: There has been increased investments towards Asia. This is a good problem, where we see customers valuing more our solutions to help them to increase their productivity and capture more businesses.

APMEN: What are the latest developments in robotic clamping/gripping?

Teo: We constantly develop new products in anticipation of the needs of our customers. One example is our latest product, the VERO S NSE3 clamping module, which improves set-up time and has a repeatability accuracy of <0.005mm.

APMEN: How do you position yourself in this industry? What sets you and your solutions apart from the competition?

Teo: Schunk is a unique company, having clamping technology (CT) and gripping systems (GS) solutions. With more than 11,000 standard products, no other company has a comparable scale and size across the range of products. With integrated solutions for both, we provide our customers the best opportunity to automate their processes.

APMEN: What advice would you give your customers when it comes to choosing the correct robot clamping/gripping solution?

Teo: For the machining industry, some customers often invested in clamping solutions and realized later that they need to automate their processes. When they started to review, they will realize that their investments may not be future proof. This may further discourage them towards the automation idea. Our comprehensive CT products allow our customers to later upgrade with our GS products, as both offers seamless integration.

APMEN: The trend is toward smarter factories now, with the advent of Internet of Things (IoT), data analytics, etc. Where does Schunk come in in this environment?

Teo: Schunk sees the need to embrace new technologies. iTENDO, our intelligent hydraulic expansion toolholder for real-time process control, records the process directly on the tool, and transmits the data wirelessly to a receiving unit in the machine room for constant evaluation within the closed control loop. With iTENDO—the first intelligent toolholder on the market—Schunk is setting a milestone when it comes to digitalization in the metal cutting industry.

APMEN: What is your outlook for the robotic clamping/gripping industry in the next 12 to 18 months?

Teo: We understands our partners’ and customers’ needs. For gripping, we have come out recently with new products to address the growing demand for collaborative robot (cobots). For clamping, our latest NSE-A3 138 is specifically designed for automated machine loading. It has a pull down force up to 28kN with integrated bluff off function and media transfer units.

 

 

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