Siemens Smart Infrastructure has launched Enlighted Safe, a new workplace digital contact tracing app, which helps employees return safely to the workplace. Enlighted Safe provides greater transparency into the contact history of employees who are known to have tested positive for COVID-19. This is designed to support organisations in reducing the exposure of infection, keep healthy employees safe and productive, and eliminate the inefficient, expensive and error-prone manual contact tracing process for employers.
Built upon Enlighted’s real-time location services capability, employees are assigned Bluetooth Low Energy (BLE) identification badges while in the workplace. The app continuously records location, movement and proximity of the employees relative to each other during the period they are in the building. The solution prioritises data privacy, without the need to store personal information. When an employee is known to have tested positive, authorised administrators can query the Safe app, identify other IDs the badge has come in contact with, and disclose the list of anonymised IDs as part of their contact tracing process.
“As COVID-19 restrictions are lifted in some locations, ensuring a safe return to the workplace and re-building employee trust is a global challenge. Smart office technology can play an important role,” said Matthias Rebellius, COO of Siemens Smart Infrastructure. “With new and varied regulations coming into effect, our intelligent IoT solutions can support the safety and well-being of occupants and visitors. We help provide peace of mind and enhanced safety for employers, their workforce and visitors.”
With intelligent IoT analytics, the solution provides greater insight for authorised personnel to visualise the contact events by location visited, duration of contact and proximity data of affected employees inside the workplace. The data is used to inform potentially exposed employees, as well as drive targeted sanitisation efforts. The application’s dashboard also provides transparency on contacts in the building, assisting management in developing safer workplace strategies for physical distancing policies, enhanced sanitisation and monitoring, occupancy limits and contact tracing. Additionally, Enlighted Safe delivers data-driven insights for organisations to proactively manage risks and design safer spaces.
Commenting on the app release, Stefan Schwab, CEO of Enlighted, said: “The important role of IoT technology in buildings has been magnified by the COVID-19 pandemic. The Enlighted sensory system can now provide digital contact tracing. It also lays the foundation for future-proofed buildings ready to help us understand with real-time data the changing nature of our at-work experiences and meet challenges beyond COVID-19.”
Siemens has been working with organisations around the world to support bringing employees back to their workplace with smart building solutions. This includes Comfy, an intuitive workplace app that keeps occupants informed and enables room and desk bookings; and the Siveillance Thermal Shield body temperature detection integrated with access control and a suite of services, such as enhancing indoor air quality, designed to mitigate the risks of further virus spread.
The future of manufacturing is brimming with opportunity—it is full of new technologies designed to reduce waste and maximise process efficiency and flexibility through software and hardware capabilities. Article by Rahav Madvil, Simulation Product Manager for Siemens Digital Industries Software, and Noam Ribon, Senior Business Consultant at Siemens Digital Industries Software.
Industrial manufacturing as a sector has been an early adopter of robotics and other forms of technological improvements for decades. Robotics have been one of the best options to increase production efficiency for large and often highly repetitive manufacturing processes. But the era of producing large quantities of just a few products with low mix is coming to an end, giving way to increased product personalisation requiring a more flexible production process with less waste than ever before.
Fortunately, the future of manufacturing is brimming with opportunity. It is full of new technologies designed to reduce waste and maximise process efficiency and flexibility through software and hardware capabilities. Almost all of this promise is built upon a foundation of digital transformation – and the digital twin. Everything from raw material tracking to process optimisations to hardware selection stem from insights gained from the digital twin and a closed-loop optimisation of entire facilities.
The most difficult aspect of any change to operation are the inevitable changes to process—they are expensive twice over, because nothing is being produced and resources are still being consumed. An autonomous transport initiative squarely addresses this, relying on a few, key technologies to make it happen.
The Power of Virtual Commissioning
Creating a comprehensive digital twin of your production process can greatly reduce downtime for new machines, new processes and new products. Let’s say you need to install a new CNC station. What if the processes for this new machine could be validated before it ever arrived on the production floor by using the digital twin of the production line? Less time could be spent integrating the new component into the overall production lines through line integration as a part of virtual commissioning. Available today, virtual commissioning is the critical underpinning to an efficient production environment enabling a closed-loop iterative optimisation of the entire facility.
Virtual commissioning is vital, not only for testing software controls, but for adding insight to the efficiency of the controls strategy. It is also essential for embarking on the advanced robotics journey, laying the groundwork for implementing greater process automation and flexibility needed to efficiently implement tomorrow’s manufacturing technologies today.
Simulate Everything Upfront
One of the best options to minimise risk when updating an existing process or making a new one is to simulate the new operations. It nearly eliminates upfront investment in machinery before knowing whether the new process will operate as expected on the shop floor. For new digitalisation efforts, this is where a digital twin should be established for the process. Without a comprehensive study of the actions within a plant new equipment could be under-utilised leading to lost investment.
Just as important is the implementation of IoT devices, that serve to close the loop between the digital twin and the physical processes once the new processes have been initiated. Although these devices are often embedded in new production equipment, but it is important to consider how to best maximise the voluminous data they generate to gain crucial insight into the production process.
Next Generation Programming
Another route to maximising production time even when supporting a high product mix is to expedite the reprogramming of the robotics in use on the factory floor. Without integrated robotic control, updating a robotic arm for a new task can be incredibly time-consuming. It needs to be taken offline, reprogrammed, validated and restarted, for each robot that will handle the new processes.
Siemens Digital Industries Software bring flexibility to robotic arms by enabling automation for flexible products.
All that changes by integrating the programmable logic controllers for these robots into the comprehensive digital twin. Much of this process can be streamlined. Does a bolt spacing on a phone need to be shifted slightly to accommodate the latest 5G wireless antenna? If the entire fleet of robots working on that production line could understand the change, that would save many hours across multiple engineering and production teams. Engineers simply need to let the robots know of the change and any differences in manufacturing tolerances can be accounted for with closed loop sensing through visual or force feedback. With force feedback within the robotic arm, any force exerted over a defined threshold can initiate a pause to the robotic arm’s actions and readjust positioning to address the perceived problem. Instead of shutting them down for reprogramming, all the robots working on the project can adjust independently to subtle changes.
Although this might sound like some futuristic scenario, task-based programming has already been tested in the real world. In a partnership between AtriMinds and B/S/H/, Siemens Digital Industries Software helped bring flexibility to robotic arms by enabling automation for flexible products. Previously, one of the largest hurdles to automating assembly was how to work with flexible components. Traditional robotics rigidly follow predefined movements, so if something were to inadvertently shift, the whole assembly could be destroyed. But by implementing force sensing on the robotic arms, there is an almost intuitive understanding of the parts and how the robot is interacting with the workpiece at its station. If a hole is slightly out of place on a panel, the input from force sensors can help the robot redirect its movement and thread a screw through without complex, preprogrammed instructions for misalignment scenarios.
Optimising Production with Autonomous Robotics
Simulation, virtual commissioning and advanced robotics programming lay the foundation for a fully flexible production floor, but automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) weave it all together and bring it to life. Historically, conveyor belts acted as the material flow paths on a shop floor. They efficiently move product from point A to point B but require semi-static positioning. Even mobile conveyor systems, common in logistics work, take time to move and to ensure a safe path for product.
Heatmap from simulating AGV and AMR activity on a manufacturing floor.
In contrast, AGVs and AMRs can change their path during transit. This saves time that would have been spent readjusting existing features, this is critical for a flexible production environment. Imagine a production floor, making two distinct version of a product. For version one, the bolts need to be added before the secondary assembly is added, while in version two bolts cannot be added until after the sub-assembly has been mounted. In a static conveyor facility, this could be completed given enough conveyor length and a sorting mechanism. Beyond a couple variations to the production sequence the factory would fill up with conveyor loops that only transport a few products at a time, defeating one of the main goals of the technology But with a fleet of AGVs or AMRs moving materials and work pieces throughout the facility, products can be rerouted and the sequence reordered to another machine. Or, in the case of highly customised consumer products, components could be routed to the best machine for the task. It can account for how much time is required to switch over to the new process, how many units can it produce compared to other machines, and even the impact of a re-route on other processes on the shop floor.
Reaping the Benefits of Tomorrow’s Robotics Today
Achieving all this requires a highly integrated production process. To guarantee a product is still made correctly during an automated process change, it needs to be simulated beforehand using a digital twin. To certify the product can be made in the new location, the production machine needs to be validated for the task using virtual commissioning. And to ensure the slightly different parts don’t produce errors in the process, the machines themselves need to be flexible to adapt to in real time to changing conditions with AGVs and AMRs.
Properly managing all these variables can have an incredibly positive effect on process performance, in fact it can produce up to a 40 percent improvement in labour productivity, according to a 2020 McKinsey study. Understanding the shop floor is an invaluable proposition and will continue to net savings and improvements through the life of the facility, even making it last longer by reducing maintenance overhead and costs with the improved condition monitoring of extensive IoT and the comprehensive digital twin.
Siemens has officially launched its Advance Manufacturing Transformation Center (AMTC) to provide guidance, support and training to companies in Southeast Asia on their journey of adoption, transition and transformation towards advance manufacturing.
AMTC is the first-of-its-kind, three-in-one competence center that combines the Digital Enterprise Experience Center (DEX), the Additive Manufacturing Experience Center (AMEC) and Rental Labs – creating a one-stop advance manufacturing ecosystem that addresses operational transition.
The DEX showcases Digital Enterprise solutions that enable companies to create digital twins of their envisioned advance manufacturing plants, so that they can simulate, optimize and evaluate manufacturing operations before constructing the actual manufacturing environment. It also provides manufacturing design consulting.
The AMEC is where companies can experience hands-on exposure to an advance end-to-end additive manufacturing production line supported by AMTC’s ecosystem of technology partners. It fills the gap between additive manufacturing R&D and commercialization by letting companies carry out prototyping, supported by on-site additive manufacturing experts.
The Rental Labs (Additive Manufacturing) provide affordable access to the latest industrial design software and high-end additive manufacturing printers as well as post-processing equipment – allowing companies to do low-volume 3D printing for proof of concept, and testing of such production line before deciding if they want to invest in additive manufacturing infrastructure.
Minister Chan Chun Sing congratulated the launch of the Siemens AMTC with a video message.
“Today, most companies understand the urgent need for digital transformation, and the disruption brought on by the COVID-19 pandemic has emphasized that. But many companies are deterred by factors such as complex and unintegrated technologies, high cost of transition, disruption to business continuity and lack of technical experts,” said Raimund Klein, Executive Vice President of Digital Industries, Siemens ASEAN. “Siemens is supporting companies in their transition into Industry 4.0 with the AMTC, a consulting, training, R&D and small-scale production facility, all rolled into one.”
Siemens, through the AMTC, is partnering SkillsFuture Singapore to roll out a six-month additive manufacturing training under the SGUnited Mid-Career Pathways Programme. The programme equips mid-career jobseekers with skills in additive manufacturing and digitalization to move into roles such as Programmable Logic Controller engineers and automation engineers, so as to better support the current wave of industrial companies undergoing digital transformation. The AMTC will host projects for trainees to work on and organise Project Demonstration Days for trainees to pitch their projects to potential hiring employers.
“The launch of its Advance Manufacturing Transformation Center reflects Siemens’ continued confidence in Singapore as a leading location to spur regional development and adoption of Advanced Manufacturing. We believe it remains relevant and will catalyse the digital transformation of businesses in the new operating environment,” said Lim Kok Kiang, Executive Vice President, International Operations, EDB. “We are also heartened that Siemens is supporting our mid-career professionals with training opportunities during this challenging period, and equipping them with skills for the future.”
The AMTC ecosystem currently consists of technology providers, education and research institutes, as well as government agencies. They are:
FARO Technologies Inc. has hired two industry veterans to join its senior leadership team and manage the global hardware and software R&D teams. Avi Ray-Chaudhuri, who serves as Vice President of Hardware R&D, and Wesley Tilley, who serves as Vice President of Software R&D, joined the company on August 31, 2020.
“As FARO continues to increase its focus on cloud-based software applications that enable long-term differentiation of our 3D solutions, we are adding critical talent to FARO’s executive team to lead both our software and hardware R&D organizations that will accelerate our product development efforts,” said Michael Burger, President and CEO of FARO. “I am thrilled to have two industry leaders like Wes and Avi join our organization and lead these teams.”
Ray-Chaudhuri has over 20 years of leadership success in diverse industries including semiconductor, advanced lithography, and laser development. Most recently, he served as VP, Engineering, Commercial Lasers for Lumentum, where he significantly reduced the product development cycle time and implemented best-in-class program management, engineering and operations practices. Ray-Chaudhuri earned a Doctor of Philosophy, Electrical Engineering from the University of Wisconsin and a B.S. in Electrical Engineering from Princeton University.
Meanwhile, Tilley brings more than 30 years of experience in the telecommunications industry, primarily in the areas of product management and R&D leadership. He most recently served as VP, Communications Software as a Service at Oracle, where he led a strategic shift in global business unit strategy to Cloud native, SaaS offerings in the telecommunications space. Tilley has an MBA in General Management from Duke University and a B.Sc Computer Science from North Carolina State University.
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.
Here’s a look at how a tool and die maker reduced time to machine die form plates from 11.3 to 4 hours.
Burr OAK Tool Inc. produces dies used to manufacture two types of fins for window air conditioners: evaporator fins on the side of the air conditioner inside the window, which transfer heat from the inside air to the cold refrigerant flowing through the evaporator coil; and compressor fins located on the side of the air conditioner outside the window, which move heat from the now hot refrigerant to the outside air. Burr OAK Tool dies progressively stretch and reform the fins through a series of metal forming operations that extrude and reduce the thickness of the fins. The very complex geometry of the dies must be controlled within +5/-0 ten thousandths of an inch in order to meet fin tolerances.
Simulating machining operation with ESPRIT.
Until recently, the company finished and semi-finished form plates on a grinding machine because its machining centres could not hold the required tolerances. It took 9.2 hours to produce form plates with a waffle form and 11.3 hours for sine wave form plates. Burr OAK Tool recently purchased a Mazak VTC-800 4-axis vertical machining centre with the goal of reducing machining time for these dies. The new machine is much more difficult to program than any of the machines used previously by the company. Adding to the challenge is the fact that parts are designed in 2D because they have so many holes and other features that it would take prohibitively long to design them as solid models.
David Schwartz, CNC Programming Manager for Burr OAK Tool.
Back in the mid-1990s, Burr OAK Tool used a CAM software package that did not accurately simulate machining operations. The company mounted many of the parts it machined on workholding devices called tombstones, and it was not unusual for a spindle driven by a new program to crash into a tombstone, which often required expensive repairs.
“We switched to ESPRIT CAM software from DP Technology because it accurately simulates the machine, spindles, tools and workpiece in real-time operation,” said David Schwartz, CNC Programming Manager for Burr OAK Tool.
After the purchase of a new 4-axis machining centre, Burr OAK Tool programmers attended ESPRIT training for the Mazak VTC-800 and the company purchased a Solid Mill Free-Form 3-Axis add-on for one of its ESPRIT licenses.
Completed fin die.
With ESPRIT, Burr OAK Tool programmers detect crashes and gouges during the programming process before downloading the program to the machine. ESPRIT’s simulation capabilities have eliminated crashes while substantially improving the productivity of the company’s programming team. Over the time it has used ESPRIT, the company has reduced its programming team from 13 to six people through innovation while substantially increasing its programming volume and capabilities.
The first step in programming the form plate is importing the 2D models that contain the part definition. Only a few clicks are needed to extrude the 2D models to create the 3D surface geometry. The next step is to define features such as holes and bosses which map into machining operations. Burr OAK Tool programmers currently perform this step manually although in the future they plan to investigate the automatic feature recognition capability of ESPRIT. Burr OAK Tool programmers use ESPRIT’s mill between curves feature to define the surface to be milled.
Fins produced on Burr OAK dies.
Most machining operations are performed with the spindle tilted at 30 deg with respect to the workpiece because ball nose end mills perform better when cutting on their sides than on their points. The milling operation is typically run at a 250 inches per minute feed rate and produces an 8 ra finish, which matches or even exceeds the finish produced by grinding. This new procedure works so well, they were able to eliminate a separate roughing operation on the vertical machine centres and go directly to a tilted head semi-finishing operation on the VTC-800 that leaves only 0.002 in for the finish. A small ball nose end mill removes the last 0.002 in.
ESPRIT simulation automatically identifies any moves where the spindle or tool passes too close to the part or machine. The programmers closely compare the simulation results to make sure it matches the design spec. As a final step, programmers use the ESPRIT post-processor for the Mazak VTC-800 to produce code that runs perfectly every time. Thanks to its accurate simulation and code, Burr OAK Tool programmers feel confident enough to run lights-out even with high precision, single run, custom parts.
“We have reduced machining time to 3 hours on the waffle dies and 4 hours on the sine wave dies, substantially reducing the cost of producing these critical tools,” Schwartz concluded. “Programming the form milling operations on the dies takes only about 2 hours, which is remarkably low considering the complexity of the part. We are confident that once we fully incorporate the capabilities of ESPRIT into our programming methodology, we will be able to reduce fin die programming time to only 1 hour.”
Metal additive manufacturing has seen many trends over recent years including pushing the (build) envelope and deposition rate to higher levels, broadening the materials portfolio, and expanding into new markets. According to IDTechEx, one trend that cannot be overlooked is the number of product launches for low-cost “desktop” variants; but the question is, will they be successful?
It is well-known for polymer 3D printing that the hobbyist market, which, although popular and great for engagement, is not where the value lies. The majority of the market value is and will be based in industrial applications. Metal additive manufacturing currently services high-value industries, most of the printers sold are powder bed fusion and can cost over $1 million with expensive powder feedstocks. The industry is forecast to have a fall resulting from COVID-19 before rising to significant levels, according to an IDTechEx report, Metal Additive Manufacturing 2020-2030.
The high price-tag for current metal printers has kept it in the realms of high-value industries such as aerospace and defense, and medical. Powder bed fusion processes are gaining traction in other sectors, such as energy, but require time to find the economically viable use-cases. There are a large number of alternative printer processes emerging, including directed energy deposition (DED), metal binder jetting, material jetting, and more. The report highlights all the main players and benchmarks the different processes against each other, allowing the gaps in the market to be observed.
According to IDTechEx, one recent trend is the release of “desktop” or cheap/affordable metal printers. Here we are not talking about systems costing around $0.5 million and targeting small-to-large part production but rather those at ca. $100,000 or below. These small printers are designed to make this technology more accessible and ideal for research, prototyping, and small replacement parts.
There are numerous players entering this field with different processes. The bound metal approaches of Markforged and Desktop Metal grab most of the headlines, although there are others extruding pellets (rather than filaments) and Rapidia with a “water bound” approach. Then beyond that, there are players like One Click Metal (a TRUMPF spin-off) making low-cost powder bed fusion machines and the likes of Meltio and InssTek making directed energy deposition units utilizing wire and powder feedstocks, respectively. Some companies have their whole business model around these low-cost printers whereas others have them as more as a secondary side project, the issue comes with the economics.
To make these printers a success, large annual sales volumes are needed which means a far greater adoption than has previously been observed. The follow-on sales from materials will not be as significant and the “simple” designs will result in less servicing, installation, and training fees. The counterpoints are that there will be software services and a replacement market which could be beneficial with a large installed base.
There are also printer limitations that are quick to be overlooked, IDTechEx notes. For bound metal processes, this includes necessary debinding steps (which brings solvent considerations, cost implications, and part thickness limitations) and consolidation in a sintering furnace (bringing impact on size, time, and cost). The same is true for other processes and although they are not deal-breakers (and there are constant innovations progressing this), it does mean they are not the small, cheap, “plug-and-play” printers initially perceived.
Then there is the important question of what the adoption will be like. This is unchartered territory and although the products are at an attractive price point, and there are good early signs, the market potential has many barriers to be truly realized. The competitive landscape is heating up, the complicated legal history between Markforged and Desktop Metal is well documented and given both have significant funding and valuations there is clear confidence in the potential. It should be noted that both players have other offerings that could prove more lucrative in the longer-term.
Beyond the bound metal printers, Markforged are major players in 3D printing of continuous fiber composites. Desktop Metal also entered this field in late 2019 with their Fiber printer, and there are many more new and established players developing this technology. This includes a wide range of fiber integration, material choices, and design opportunities.
Building on the success of its world-first field trial in June this year, a WarpSPEE3D 3D metal printer has again deployed and been put through its paces by the Australian Army during a two-week field exercise in the extreme heat and humidity of the Northern Territory.
WarpSPEE3D is the world’s first large-format metal 3D printer to use patented cold spray technology that enables significantly faster and more cost-effective metal part production than traditional manufacturing. Developed by SPEE3D, Australian award-winning manufacturer of metal additive manufacturing technology, the printer is capable of printing large metal parts up to 40kg at a record-breaking speed of 100grams per minute.
The printer arrived in Darwin in early June and forms the backbone of the Army’s developing 3D printing capability.
Having received a number of upgrades and modifications in the two months since its first deployment, the WarpSPEE3D print cell deployed, as part of 1 CSSB’s larger Brigade Support Group, to various field locations in temperatures up to 38 deg C and 80 percent humidity, whilst printing and machining genuine military metal parts.
SPEE3D printers make metal parts the fastest way possible, leveraging metal cold spray technology to produce industrial quality metal parts in just minutes, rather than days or weeks. This process harnesses the power of kinetic energy, rather than relying on high-power lasers and expensive gasses, allowing 3D metal printing in the field, at affordable costs.
The Australian Army announced a $1.5 million investment in a pilot of SPEE3D technology in February 2020 with a 12-month trial designed to test the feasibility of deploying 3D metal printers both on-base and in the field. SPEE3D partnered with the Advanced Manufacturing Alliance (AMA) and Charles Darwin University (CDU) to deliver the program with soldiers from the Australian Army’s 1st Brigade training in 3D printing at CDU since February.
The program aims to significantly increase unique parts available to the Army compared to what the regular supply chain can provide.
SPEE3D CEO Byron Kennedy said, “This second field deployment proves our technology is a genuine solution for expeditionary metal 3D printing. This two-week trial demonstrates the WarpSPEE3D is a robust workhorse that is capable of printing real parts and solving real problems in the field. It also proves that soldiers can take control of the whole workflow of creating the spare parts they need, from design to printing and post-processing, right here where they need them.”
Digitalisation at the enterprise level has proven to be critical to bringing production back online safely, quickly and with greater resilience in preparation for crises of the future. By Nand Kochhar, vice president of Automotive and Transportation Industry Strategy for Siemens Digital Industries Software.
The COVID-19 pandemic has put exceptional strain on manufacturing facilities in the automotive industry. While all parts of the automotive enterprise have been impacted, manufacturing facilities have proven especially vulnerable because of the crucial link that human operators form in the vehicle production chain (Figure 1). Taking action to protect the health of these employees is challenging.
Figure 1: Human operators perform critical tasks in the vehicle production chain, making automotive production facilities especially vulnerable to the COVID-19 pandemic.
Today’s production lines were designed and optimised for a pre-pandemic world. Operators often worked in close physical proximity and shared tools, parts bins and other resources to complete their tasks. The measures necessary to prevent the spread of COVID-19, of course, invalidated many aspects of these production designs and optimisations.
The Challenges of Redesigning Production Facilities
Automotive companies had to quickly modify and adapt their production facilities to ensure the safety of their employees. While these changes are necessary, they can dramatically impact efficiency and output in a production facility. For example, production stations have to be redistributed across a production line to ensure that human operators remain at least six feet apart at all times during the performance of their duties. In addition, each operator must have their own tools and parts bins to prevent the spread of the disease via mutual contact with a surface or object. While seemingly small, these changes can greatly influence how human operators perform their duties, often slowing them down. Just the increased spacing between production stations can slow production down.
The changeover of employees between shifts also presents safety challenges. Manufacturers will need to ensure that workers are healthy when they arrive to work, and allow extra time between shifts to thoroughly clean stations and tools. These extended shift changes result in more production downtime and potentially could require plants to reduce the number of shifts they run in a day, further impacting productivity.
These and other effects of the pandemic have pushed companies to turn toward advanced manufacturing technologies to mitigate the shortcomings of socially distanced production lines and stations. Novel applications of technologies such as virtual reality, advanced robotics and additive manufacturing are enabling safer and more productive manufacturing facilities. Automated guided vehicles (AGVs), for example, can replace shared parts bins, delivering materials to production stations quickly and efficiently while facilitating physical distancing among human operators (Figure 2).
Figure 2: AGVs can help maintain physical distance between human operators by automating material delivery and other logistics tasks.
While these technological innovations have provided some relief, integrating them with existing facilities can create additional challenges. The implementation of new production processes or technologies can be costly. The redesigned production lines also must be tested, verified and validated to avoid issues as production comes back online. This is especially true at the junctures where old and new processes interact. Any problems that occur can lead to schedule overruns, delays in production ramp-up and increased cost.
It is not just original equipment manufacturers (OEMS) conforming to the new constraints of operating in response to a global pandemic. As OEMs determine how to modify their production design and strategy to account for social distancing measures, their suppliers, including Tier 1 and 2, are engaged in the same exercise. As all these companies adapt, digitalisation at the enterprise level has proven to be critical to bringing production back online safely, quickly and with greater resilience in preparation for crises of the future.
Digitalisation Enables a Smarter Way Forward
Digitalisation has helped companies to adapt their production facilities quickly to ensure social distancing and protect employee health. Modern software solutions enable production engineers to virtually plan and design production stations, lines and even entire facilities before physically implementing any changes (Figure 3). The virtual copy of a station, line or facility, known as a digital twin, can then be simulated to verify, validate, troubleshoot and optimise production designs for safety and efficiency before any machinery is commissioned or facilities reorganised. Virtual production planning and design solutions can even simulate human operators, enabling the production design to account for ergonomics and physical distancing requirements.
Figure 3: Digital manufacturing engineering solutions enable production facilities to be re-designed virtually. Recently, Siemens announced a new solution that helps manufacturers to simulate and manage employee exposure risks while enabling productivity throughout their facilities.
As facilities come back online and production ramps up, digital manufacturing operations management solutions have helped companies monitor and optimise the operation of their facilities. These solutions can gather production data from multiple sources and aggregate it into useful, contextualised reports. This data can then drive production scheduling optimisations, quality enhancements and more.
A robust digitalisation strategy, however, should extend beyond production design and management. Integrated solutions from product and production design through product lifecycle management (PLM), manufacturing operations management (MOM) and enterprise resource planning (ERP) create a complete digital thread from product design into the supply chain. Such a comprehensive digital thread can help companies turn complexity, whether from operating during a pandemic or from the requirements of next generation products, into competitive advantage by streamlining operations and improving collaboration throughout their supply chains.
In particular, enabling more frequent and effective collaboration throughout the supply chain will be critical as OEMs and suppliers continue to recover production output and prepare for unforeseen future disruptions. Better communication among partners also will help enable OEMs and their suppliers to coordinate the ramp-up of production capabilities with market demand to avoid both excesses and shortages of product. Collaboration also facilitates the sharing of experiences and key lessons learned while adapting to the pandemic. These experiences can help inform disaster recovery plans, allowing companies to incorporate a realistic estimation of how they will react to emergency situations.
Building in Resilience Through Digitalisation
The COVID-19 pandemic has automotive manufacturing facilities and employees under particular strain. As the pandemic has progressed, automotive OEMs and suppliers have been challenged to reorganise and redesign their manufacturing facilities to keep their employees safe and healthy. Redesigning a production facility, however, is extremely difficult, and this is especially true under the pressure of responding to a major crisis.
Throughout the ongoing process of redesigning and restarting automotive manufacturing facilities, digitalisation has proven key to achieving safe and efficient production environments. Digitalised production design and simulation solutions enable engineers to quickly design and verify new configurations for production lines and stations, while MOM, PLM and ERP solutions enable greater insight into facility performance and supply chain logistics. Digitalisation has also helped automotive companies come together in a time of crisis to improve collaboration and learn from others’ experiences. As the industry continues to overcome the effects of the COVID-19 pandemic, the lessons learned from these new partnerships will help the entire automotive industry become more resilient as they prepare for the challenges of tomorrow.
As the COVID-19 pandemic has ripped through much of the world this year, 3D printing has emerged as an agile and effective technology for producing personal protective equipment, medical equipment prototypes and nose swabs. But General Motors (GM), which has been steadily upping its investments in 3D printing over the past couple years, is betting that the business benefits will continue long after the current crisis subsides. The company added 17 production-grade Stratasys FDM 3D printers to its fleet at the end of 2019 and has been turning to 3D printed tooling for speed, weight reduction and cost efficiency on its production lines.
“With the pace of change in modern industry accelerating and business uncertainty increasing, 3D printing technology is helping us meet these challenges and become more nimble as a company,” said GM’s director of additive manufacturing, Ron Daul. “We’ve been on this journey for more than 30 years, but 3D printing is becoming even more widespread at our company, with more than 700 employees now trained to use the technology. Additive manufacturing is consistently providing us more rapid and efficient product development, tooling and assembly aids, with even more benefits to come.”
An April 2020 study by SME Media found that 25 percent of U.S. manufacturing professionals were planning to change their supply chains in response to the pandemic, and 3D printing was the top choice (with robotics) of 11 manufacturing technologies for post-COVID investment. The technology can be used to 3D print spare parts, produce end-use parts closer to assembly, help manufacturing lines retool faster, and develop new and better prototypes more quickly.
GM is moving faster than some companies to seize a competitive advantage. The company has used 3D printing since 1989 for prototyping. In fact, 75 percent of the parts in the prototype of its 2020 Chevrolet Corvette were 3D-printed, and GM now has 3D printers installed in many production facilities around the world. The company is increasingly moving beyond prototyping to production-related applications like tooling.
A big test of this application came in April when GM entered into contract with the U.S. Department of Health and Human Services to deliver a 30,000-unit order for critical care ventilators, in conjunction with Ventec Life Systems, by the end of August. The company reverse-engineered part data for tooling fixtures from the original ventilator manufacturer, and started 3D printing them the next day. All 3D printed tooling used for critical care ventilators was 3D printed on Stratasys systems. When the company requires more 3D printing capacity, there is an automatic offload path to Stratasys Direct Manufacturing for parts on demand. This helps GM run at a high utilization rate for its existing machines, expanding in-house capacity when it can ensure it has a sustained need for it.
Material innovation and machine repeatability have made a difference. For example, Nylon12 Carbon Fiber is a composite material containing 35 percent chopped carbon fiber by weight, which translates to an exceptionally high strength-to-weight ratio, even in places subjected to heavy vibrations. As a result, heavy parts that would have previously required metal can now be 3D printed in polymers. And production-grade systems like the Stratasys F900 have been designed to not only perform to a high degree of precision but also consistency so that every part is as identical as possible.
“GM is making the smart investments in 3D printing to succeed in this new normal of uncertainty and disruption,” said Stratasys Americas President Rich Garrity. “As a result, GM has manufacturing lines that are more adaptable and less expensive, and products that are developed faster and better. They are a clear model for the future of additive manufacturing in the automotive industry.”