ZEISS Group and Microsoft Corp. has announced a multi-year strategic partnership to accelerate ZEISS’ transformation into a digital services provider that is embracing a cloud-first approach. By standardising its equipment and processes on Microsoft Azure as its preferred cloud platform, ZEISS will be able to provide its customers with enhanced digital experiences, address changing market needs more quickly and increase its productivity.
Leveraging Azure high-performance compute, AI, and IoT services, ZEISS will work with Microsoft to provide original equipment manufacturers (OEMs) with new quality management solutions, enable microchip manufacturers to build more powerful, energy-efficient microchips and deliver new digital healthcare solutions for improved clinical workflows, enhanced treatments, and device maintenance. Furthermore, ZEISS will create a seamless experience for its customers through one digital platform and manage all digital ZEISS products through one cloud-native platform to enhance continuous and agile product development.
Initially, ZEISS will enable its solutions in the Industrial Quality & Research segment to be run on a connected quality platform built on Azure, allowing direct integration into the customer’s production process. The platform will help gain business insights and foster collaboration across domains, assets and processes that have traditionally been managed in siloed, proprietary systems.
ZEISS provides metrology and quality assurance solutions delivering meaningful information on parts dimensions, component behavior and defect detection. Real-time and large-scale analysis of data that is collected at all stages of the manufacturing process is key to efficient and effective quality assurance, tightly integrated with today’s and tomorrow’s IoT-enabled production processes.
Quality is also a key objective of a new ZEISS audit trail solution, initially focused on highly regulated manufacturing industries, such as medical technology which is particularly sensitive to quality assurance. The solution will allow customers to identify root causes and react quickly on quality issues to reduce down-time and keep productivity up. The software will allow customers to track, trace, visualize and analyze process and product data with the help of Azure AI services to identify failure root causes more quickly.
Data-driven healthcare solutions improve patient care
ZEISS Medical Technology provides comprehensive solutions for ophthalmic professionals and microsurgeons, consisting of devices, implants, consumables and services. Through the partnership, ZEISS will connect its medical technology to Microsoft’s cloud and leverage Azure AI and IoT technologies for new digital services such as improved clinical workflows, enhanced treatments, and device maintenance in a secure environment that enables compliance with regulatory requirements in the health industry. These solutions will help improve the quality of life of patients and drive progress, efficiency and access to healthcare.
Metalworking has traditionally been slower than other sectors in its uptake of automation technologies. John Young, APAC director at automation parts supplier, EU Automation, looks at three key areas where current and future trends suggest metalworking is increasingly ready to shake off this reputation.
Automation and 3D printing
Not so long ago, 3D printing was mostly associated with rapid prototyping. In more recent years we have seen significant investment in additive manufacturing in the metalworking industry, driven by the demands of the defence and aerospace sectors.
This trend is set to increase. As was reported in last month’s edition of APMEN, recent research has estimated that the global market for 3D printing in metalworking is set to reach $5.51 billion by 2027. With a predicted CAGR of 31.7 percent, the Asia Pacific region can anticipate higher growth rates than any other region in the world.
The pros and cons of this disruptive innovation are relative to the application at hand, but as the technology constantly improves, its benefits are increasing. The advantages include the ability to manufacture more complex and lightweight parts and offer the design flexibility that is necessary to compete in today’s highly competitive markets.
The drawbacks include the high costs and lower surface quality. However, the cost barrier is being lowered and combining both additive and subtractive machining, sometimes known as hybrid manufacturing, can help manufacturers exploit the unique benefits of 3D printing and CNC machining. We can already see the development of hybrid machines that combine these two contrasting processes into a single footprint.
3D printing or additive manufacturing (AM) is itself a form of automation. If we are talking about automation in AM though, we typically mean something more. Experts in this area are now focused on the integration of 3D printers into fully automated production lines with the assistance of the latest AM hardware and software.
A common trend is for the automation of post-processing functions and systems such as powder removal, part finishing and part cleaning. Automating these parts of the production process is allowing some manufacturers to achieve higher levels of productivity and repeatability.
If advocates of greater automation in 3D printing are correct, it is automation that will lead to a higher rate of adoption of AM across the metalworking industry. More automation, it is argued, will bring down the cost per part and lower the reliance on manual labour, making AM a more competitive mode of manufacturing to more companies in the metalworking industry.
Robots and Cobots
The metalworking industry is witnessing the increasing use of robots as the cost of the technology gradually diminishes and as manufacturers begin to see automation as a solution to skills shortages. The adoption curve is notably higher for cobots. In contrast to larger industrial robots that are built to act autonomously and are typically housed behind safety cages, cobots are designed to operate safely alongside human workers.
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?
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.
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.
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.
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.
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.”
Hyundai Motor Company will establish a Hyundai Mobility Global Innovation Center in Singapore (HMGICs) to accelerate its innovation efforts and transformation into a smart mobility solution provider. With support from the Singapore Economic Development Board (EDB), the new 28,000 sqm innovative lab will be located in Singapore’s Jurong Innovation District and is set to be completed in the second half of 2022.
The lab will explore business ideas and technologies to revolutionise a value chain encompassing R&D, business and production for future mobility solutions and eventual expansion into global markets. Combining Hyundai’s open innovations efforts with Singapore’s fertile atmosphere, HMGICs will validate concepts including multi-modal mobility service.
The lab will also spearhead efforts to reach new markets and customers with cutting-edge technologies that will transform automotive R&D, production and sales. Combining AI, Internet of Things (IoT) and other advanced technologies, the lab will create a human-centred smart manufacturing platform that will be validated through a small pilot EV production facility.
In conjunction with the platform, an innovative product development process and on-demand production system will be tested and proven. Hyundai also aims to study new methods of vehicle development conducive to smart manufacturing while further increasing use of virtual reality (VR) technology in the vehicle development process.
Furthermore, HMGICs will facilitate collaboration opportunities with competitive local partners and educational institutions such as the Nanyang Technological University by conducting joint projects to pursue open innovation.
“The Hyundai Mobility Global Innovation Centre is an exciting addition to Singapore’s growing Mobility ecosystem. Its focus on innovative business concepts and the development of a smart manufacturing platform, leverages the research and innovation capabilities, and the value that Singapore provides to companies that want to develop, testbed and create new solutions for the world,” said Mr Tan Kong Hwee, Assistant Managing Director, EDB.
As part of its Smart Nation initiative to drive the adoption of digital innovation across industries, Singapore is actively fostering the use of digital technologies such as AI, digitalisation, and smart urban mobility. With a strong track record for open innovation, Singapore is an ideal location for Hyundai to test its innovative ideas such as HMGICs.
Solutions for suppliers seeking ways to meet new productivity challenges, including increasing demand and shorter lead times. Article by Michael Palmieri, Makino.
Aerospace and defence (A&D) suppliers are feeling the heat.
Over the next five years, original equipment manufacturers (OEMs) are expected to increase commercial aircraft production by 21 percent. The ramp-up means suppliers face unprecedented challenges. They must find ways to satisfy demand for more components while OEMs place more pressure on them to decrease lead times and prices.
Industry 4.0 technologies, including the Internet of Things (IoT), automation and advanced machine-tool capabilities, such as 5-axis machining centres, could become more common on A&D shop floors as suppliers seek ways to keep pace with OEM demands.
These technologies can help the A&D suppliers respond to market needs faster without expanding their workforce. This white paper will explore some of these trends and the solutions that A&D suppliers need to remain competitive.
Enable Faster Throughput for Complex Designs
Modern aircraft designs are forcing suppliers to rethink their current production capabilities. Older machine tools may not be equipped to manage lighter-weight, heat-resistant materials, such as titanium. Modern machining centres that are purpose-built for aerospace applications can reduce set-up times, increase accuracy and improve throughput on less-conventional designs.
Titanium vs. Aluminium Considerations
Aluminium makes up about half of the aerospace materials market by volume. But titanium use is increasing as manufacturers seek ways to reduce weight for components in next-generation planes. Titanium is lighter than structural steels historically used and almost as strong. Aluminium and titanium present different challenges that manufacturers must take into consideration when selecting machine-tooling solutions. Aluminium requires more horsepower and high rpm while titanium requires high torque at low rpm.
Suppliers need access to a variety of machine tools that can perform fast removal rates on a wide range of materials, including aluminium, stainless steel and titanium. Several key advancements in machine tooling are helping A&D suppliers address different material requirements. Some of the key technologies developed to increase productivity for titanium machining include:
Autonomic spindles that protect the spindle from excessive forces damaging the bearings. This can reduce unplanned downtime related to machine damage—which, in turn, optimizes productivity.
High-pressure, high-flow coolant systems deliver large volumes of coolant directly to the cutting zone for faster chip evacuation, increased production, and tool life.
Vibration damping systems that adjust frictional forces based on low-frequency vibration sensing, avoiding chatter and cutter damage from structure resonance in real time. Vibration damping enhances depth of cuts, which results in higher removal rates.
Developments in aluminium machining are also helping A&D suppliers increase productivity. This includes greater spindle power to improve processing speeds, improvements to acceleration and cutting feed rates, and large-capacity automatic tool changers that are capable of holding more than 100 tools and automatic pallet changer—which can reduce changeover and set-up times significantly.
In both aluminium and titanium, 5-axis capability is a key advantage by providing an efficient way to produce typical, complex, A&D part geometries. In addition, large-capacity tool changers and pallet changing automation can allow for unattended machining, which means less operator labour cost per part. These system features reduce machine downtime between parts and part handling between set-ups, which also lowers labour costs. The ability to reduce handling time, including moving parts from machine to machine or resetting them on new fixtures, also helps increase throughput and shrink production lead times to enable faster deliveries.
Maximise Productivity to Avoid Costly Delays
Many A&D suppliers are struggling to meet demand. For instance, in November 2018 Boeing reported decreases in 737 deliveries due to supplier delays. The lead time in A&D manufacturing is already longer compared to other industries, which means suppliers can’t afford machine failures or any other issues that could result in downtime. Suppliers may need to place a greater emphasis on predictive maintenance and automation to maximise productivity.
Why Reliability Matters
On-time delivery issues are urgent enough that Boeing and Airbus are working with suppliers to ensure they’re equipped to meet expectations. In addition, unplanned downtime costs manufacturers about $50 billion annually, and equipment failure is the cause of downtime 42 percent of the time.
Manufacturers are implementing automation and Industry 4.0 technologies to gain visibility into machine performance issues before they lead to major repairs or failures. In the A&D sector, Industry 4.0 is bringing predictive insights to operators and technicians in several ways, including:
The ability to access charts that display alarm events, so operators and technicians can observe trends and implement corrective measures.
Access to spindle and axis monitoring technologies that record and display axis forces, loads and speeds. This data can then be used to fine-tune processes for faster cutting speeds and greater depths of cut. In addition, manufacturers can monitor critical tool data for multiple machines from one centralised location. Operators can use this data to make adjustments for enhanced tool performance and lifespan.
Camera monitoring capabilities that capture an internal view of a machine’s work zone, making it easier to solve processing errors before they impact part quality. Technicians also can receive email and text notifications of alarms, including images of the work zone. This helps service staff immediately address maintenance issues before they become costly problems.
According to Deloitte, manufacturers that implement predictive maintenance technologies typically experience operations and MRO material cost savings of 5 percent to 10 percent, reduced inventory carrying costs, equipment uptime and availability increases of 10 percent to 20 percent, reduced maintenance planning time of 20 percent to 50 percent and overall maintenance cost reductions of five percent to ten percent.
A&D suppliers also are realising enhanced performance through automated machining solutions, such as pallet-stacking systems. The Makino Machining Complex (MMC2) is an automated material handling system that links Makino horizontal machining centres, pallet loaders and operators. The system provides a constant flow of parts to the machining centres, so it can operate for extended periods unattended, including overnight and on weekends. The ability to automate manual processes reduces the need for time-consuming manual tasks and increases flexibility to meet OEM demands.
Bridging the Workforce Skills Gap
As machine tools become more technologically advanced, the A&D industry must confront another persistent challenge: the lack of skilled workers. In a recent industry workforce survey, 75 percent of respondents said they are concerned with the availability of key skills. “The need for talent will become even more critical in the next few years, as the baby boom generation moves beyond traditional retirement age – and the unavoidable loss at some point of their expertise and knowledge,” according to Aviation Week’s “2018 Workforce Report.” Machines that are equipped with IoT, artificial intelligence (AI) and other smart capabilities can enhance productivity for existing employees and minimise the learning curve for new hires.
The Case for a Connected Workforce
Voice-assistant technology common in the consumer world, such as Alexa and Siri, are now making their way into modern machine tools. In fact, more than 80 percent of A&D industry executives say they expect their workforce to be directly impacted by an AI-based decision within the next three years, according to an Accenture report. Voice-activated commands reduce manual interaction with the machine and helps operators translate and analyse big data. These digital assistants typically work through the use of headsets. Operators speak commands into the headsets, such as “turn the machine’s lights on,” “change tools,” or “show set-up instructions.” These voice-actuated capabilities simplify machine operation by reducing the time operators spend searching for information or performing manual tasks.
AI also serves as a coach for operators who may not be familiar with various operating procedures, such as how to perform different maintenance tasks. For example, a worker can ask the voice assistant how to change a filter. In many cases, these intelligent machines are not replacing operators but helping the existing workforce perform their tasks more efficiently.
They’re also allowing workers to move easily from one type of machine to another without a significant learning curve because they’re not reliant on an unfamiliar machine interface. These intelligent machines may help A&D manufacturers identify and onboard skilled workers with greater ease because they require less training and experience than more traditional technology.
Looking Ahead: What’s Next for A&D Machining
High-tech machining solutions are advancing at a rapid pace. The availability of new technologies comes at a critical point for the A&D industry. Suppliers must continue to improve productivity and reduce costs amid a constantly changing environment. In addition to OEM demands, the industry faces new competitive challenges, including potential price increases for materials. For instance, A&D manufacturers are still uncertain how U.S. tariffs on aluminium and steel imports could impact prices. The potential for higher material prices puts additional pressure on suppliers as they try to meet increasing demands for lower costs per part and delivery.
Suppliers need equipment that can reduce downtime, increase productivity and minimise labour costs. Manufacturers should consider machine-tool providers with a broad portfolio of equipment built specifically for the aerospace industry. The latest machining centres can perform high-precision tasks faster than ever. Vendors with experience in the aerospace industry can help A&D suppliers evaluate their needs and select a solution that is appropriate for specific applications. Makino is continuously updating its machines with the latest technologies, including automation and IoT capabilities, to help the industry produce accurate structural and turbo machinery parts faster with less variability and at the lowest cost.
How ATEP Slashes Titanium Machining Costs
Arconic Titanium & Engineered Products (ATEP) in Laval, Quebec, Canada, needed titanium-machining solutions to meet customer demands to lower costs and shrink delivery times. ATEP specializes in assembly and precision machining of various titanium aircraft components, including wing attachments, seat tracks and doorframes. Standard machine platforms couldn’t provide the rigidity, flexibility or control the company needed to meet its customer requirements. The company decided to install several Makino T-Series 5-axis horizontal titanium machining centres. Research engineers from ATEP determined the machines could help the company perform certain production processes three times faster than previous methods. It eventually led to a 60 percent reduction in cycle times and 30 percent reduction of tool costs.
The company also has realized benefits related to quality improvements. ATEP is a fully integrated supplier of titanium and other specialty metals products. ATEP is receiving additional business from customers who are asking the company to correct quality issues from other suppliers, according to a company executive.
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.
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.
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.
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.
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!