Chemical Analysis Testing Now Available at Industrial Technology Centre

The Industrial Technology Centre is pleased to announce a new addition to our services – Chemical Analysis.

Material Qualification

It is important for materials to meet regulatory, performance and quality requirements. Chemical analysis of ferrous material helps ensure your products meet these requirements. ITC’s chemical analysis service can help you identify unknown alloys and characterize materials.

Our Resources

ITC uses a Bruker Q4 TASMAN Advanced CCD Based Optical Emission Spectrometer (OES) for analysis.

With a measurement range from sub-ppm to percentage levels, we can characterize your materials from trace analysis in pure metals to high alloyed grades.

Optical emission spectroscopy allows all elements in your sample to be analyzed simultaneously, ensuring fast turn-around on your results. A high-energy arc is applied to the sample, causing it to emit spectra that identify the elements present. The amplitude of the spectra allow the instrument to measure the proportions of each element.

Analysis Capabilities
● Carbon steel
● Stainless steel
● Cast iron

Applications
● Metal production
● Metal processing
● Manufacturing
● Recycling

For further information about this new capability, call 204.480.3333 or send email to tech@itc.mb.ca.

The First Steps Toward Robot Automation

The first few steps in introducing robots – or any other automation technology – are crucial to ensuring the overall success of plant automation. A botched early step can cause management to back away from the financial investment needed to automate plant processes. Companies that don’t have the proper engineering staff inhouse, can bring in a systems integrator to make sure the early steps toward automating the plant are sound and form a foundation for the next steps in the automation.

Choosing the right robot can make all the difference during the first steps in automating the plant. Photo couresy of Epson Robotics.

One obvious aspect of that first step is choosing an automation application that will deliver a clear return on investment (ROI). Yet not all ROI-based automation moves are the same. Some are fairly easy, while others are very difficult. “Frist off, start simple,” Rick Brookshire, senior manager at Epson Robotics, told Design News. “Typically, we’ll go into a factory that has no robots, no automation, and there are lots of opportunities. Some look for the best ROI, but if it’s going to be really hard, that could be a poor choice. It’s best to choose something simple. If you do it well at the beginning, management will say ‘Let’s go.’ If it’s difficult, management will lose confidence.”

Do You Need a Systems Integrator?

One of the big questions in an automation project is whether it requires outside expertise. “You have to ask the question, ‘Can we do it by ourselves or do we need a systems integrator?’ How do you determine if you’ll need one? Do you have a mechanical engineer, an electrical or control engineer, and a software engineer to put it together? If not, you’ll need help,” said Brookshire, who will present the session, The Starting Point for Robot Automation: A Beginner’s Guide, on Wed., Feb. 7, 2018, at the Pacific Design and Automation Show in Anaheim, Calif. “If you don’t have those three engineers, you’re probably best off with a systems integrator, because you want to have success.”

Choosing the right systems integrator is also important to ensure success. “You can use your robot vendor to help identify the right integrator,” said Brookshire. “If a customer has an application in medical assembly, we’ll typically refer them to a systems integrator that has medical experience, not one from the automotive world. It’s best to use someone who has done 50 medical systems, so they’re learning just your application and not the particulars of the medical industry.”

Matching the Robot with the Application

A miss-match between the process that needs to be automated and the technology to complete that automation can bring the automation process to a halt. “You want to work with a robot vendor that can help you choose the right robot for the job,” said Brookshire. “If you’re moving parts from a flat surface to another flat surface and speed is the issue, you can find a robot for that. If you have to move the part from a shelf and shift it to a right angle to put it down, you may need a six-axis robot. This is a conversation where a good vendor will help you find the right robot for the job.”

Automation projects run into trouble when plant managers are unclear in describing the process that needs to be automated. The process has to be completely understood, from each individual movement to the nature of the parts that are getting moved. “It’s important to be able to present the process so the vendor understands exactly what you want to do,” said Brookshire. “Customers say they want to automate this process. They say they want it to be high precision, but they can’t say how many parts need to be moved and in what time. The answers can be vague. Then you find out later that they’re working with plastic parts. That tells you they don’t understand the process.”

Automation applications are intrinsically designed to solve problems, and those problems have to be articulated. Areas of specific difficulty have to be explained in order to find the right solution. “You need to talk about the problem areas. When you explain the problem areas, you’re telling the vendor what needs extra attention,” said Brookshire. “If customers can explain the process with precision, and they know the process thoroughly, they will have an easy time finding am automation solution.”

>> This article by Rob Spiegel was published in Design News, January 29, 2018

Why Manufacturing Should Embrace AR/VR

Fear of new technology eliminating manufacturing jobs continues to escalate, particularly when it comes to artificial intelligence and robotics. What’s missing from the discussion, however, is a look at assistive technologies, such as augmented reality (AR) and virtual reality (VR) that can create a partnership between man and machine.

Both AR and VR are improving performance, safety and design function in the manufacturing space; when paired with human labor they are becoming known as “upskilling technologies.” A recent report by Bank of America Merrill Lynch on AR/VR finds that that more than 50 percent of manufacturing organizations are testing AR given its potential to boost productivity (30 percent faster assembly), reduce costs (25 to 60 percent savings on installation and maintenance) and improve output (40 to 90 percent higher accuracy).

Here are three ways VR and AR are re-shaping the mid-market manufacturing space:

Worker Performance – AR and VR improve workers’ sight line and enable them to complete tasks more quickly. For example, a Harvard Business Review study documented a GE technician who wired a wind turbine’s control box in the traditional manner — reviewing the instruction manual, turning to the turbine, turning back to the instructions, etc. — and performing the same task while guided by line-of-sight instructions overlaid via an AR headset. The device improved the worker’s performance by 34 percent on first use.

Inspection and Maintenance – AR and VR can decrease inspection time and assist in detecting errors. Since 2011, Airbus has implemented AR technology to increase efficiency and improve quality control at its installation and inspection facilities. Using the company’s Supply Augmented Reality Tool (SART), employees can use a tablet to overlay a digital mockup over images of real systems to identify faulty parts that need to be repaired or replaced.

Product Design – Companies can also use virtual reality in the product design stage of manufacturing, allowing developers to quickly make modifications and additions before products go into modeling and manufacturing processes. For instance, in September 2017, Ford announced expanding the use of Microsoft’s HoloLens for designers. This allows designers to quickly model changes to cars, where they would previously have to develop a clay model. Using the HoloLens, designers can also overlay new features onto physical prototypes.

While the implementation of AR and VR technologies still face a few hurdles, including companies having adequate processing speeds, bandwidth and AI-analytics capabilities to fully implement the new technologies, the fact that several companies are experimenting with AR/VR is likely to nudge the industry forward in 2019. And, even though factories will continue to automate routine responsibilities, workers will remain imperative in operating robots and artificial intelligence in many non-routine tasks. With AR and VR, employees will be safer and more productive along the way.

Matthew B. Elliott serves in a dual role as Michigan Market President and as the Business Banking Midwest Region Executive at Bank of America Merrill Lynch. 

>> This article by Matthew B. Elliot, was published in Manufacturing Business Technology, 02/08/2018

Smaller Manufacturers Can’t Afford to Dismiss the IIoT

Almost four of every five manufacturers and distributors still have no plans to implement IIoT technologies. They’re making a mistake.

The Industrial Internet of Things has significant commercial and operational implications for manufacturers. It can help companies improve everything from customer experience to asset utilization to employee productivity to supply chain and logistics management. However, our 2017 Manufacturing Report found that 77% of manufacturers and distributors surveyed still have no plans to implement IIoT technologies. Many of these respondents are from small and midsized companies that likely don’t think the IIoT is necessary in their operations or simply don’t feel they have the time or resources to make significant changes to their processes.

While the IIoT isn’t suitable for every company, many of these manufacturers are making a mistake by dismissing it. The report also identified several top areas of focus and concern for manufacturers — such as workforce challenges and supply chain management — that the IIoT can help them address. Nearly 60% of respondents pointed to a lack of qualified workers as a barrier to business growth, and 64% said that supply chain management is important to their success in the next five years. Further, more than half said the ability to improve customer service and response time is a top business driver impacting their technology investment decisions.

The IIoT offers clear and impactful solutions to many of these manufacturers’ biggest stated challenges. It’s important for the leaders of these companies to seriously consider and assess the potential benefits of introducing IIoT technologies into their operations.

Streamline the Supply Chain

The IIoT can fill in gaps in intelligence that have long posed challenges for manufacturers’ customer relationships.

While advanced technology such as cloud-based ERP systems can help a company more efficiently share designs and collaborate with its customers during the production stage, the IIoT can provide a manufacturer insight into the after-sales life of a product. With the IIoT, a manufacturer can monitor its product in the field to assess performance and health in real time. Instead of having to rely on customer requests or regular site visits to ensure continued operation and optimal performance of a product, the IIoT can allow a manufacturer to proactively address emerging problems and prevent costly shutdowns. This is especially helpful for durable products with long service lives. By monitoring product usage and performance, a manufacturer can ensure it maintains strong customer relationships and wards off competitors.

It’s important for manufacturing leaders to understand that as more and more companies begin to utilize IIoT technologies, they will be able to offer other companies’ customers a heightened level of responsiveness and service. The manufacturers that can’t match this level of service risk losing market share to these more advanced competitors.

In addition to its benefits for customer relationships, the IIoT allows a manufacturer to cut down on manual processes when it comes to working with vendors. Replenishment of everyday items such as nuts, bolts, screws and fasteners is a highly manual task that typically requires a vendor to regularly visit a manufacturer and take stock of inventory to determine how much product it needs to ship. The IIoT can eliminate this manual process by sending a signal to vendors when supply runs low that then prompts automatic replenishment.

By increasing the amount of valuable data flowing through a manufacturing operation, the IIoT can help a manufacturer improve communications with vendors and customers, and streamline supply chain processes.

Address Workforce Challenges

The struggle to find the talented workers needed to run today’s increasingly high-tech manufacturing operations is a consistent pain point for the industry. While the IIoT cannot solve this issue directly, it can generate benefits that ease labor pains while also enhancing a manufacturer’s ability to recruit top workers.

By cutting down on manual tasks, manufacturers can make more efficient use of their existing resources. With IIoT-enabled equipment that reports its own condition and accurate run times, manufacturers can redeploy employees to more value-added work. Since employees no longer need to spend as much time on manual interactions with equipment, they can work to gain the technical skills needed to become higher-level machine operators and programmers.

Additionally, manufacturers that demonstrate a commitment to technological advancement will be better equipped to attract the talented and highly skilled workers they need in order to capitalize on IIoT technologies and pull insights from the massive amounts of data generated. So, there’s a potentially significant recruitment element to adopting the IIoT. Companies that refuse to embrace advanced technology will struggle to recruit the talented workers they need to thrive in today’s fast-paced, hyper-competitive economy.

Making the Move to the IIoT

Though many small and midsized manufacturers may view adopting the IIoT as an onerous and expensive project, implementing these technologies will not typically require a significant financial investment. The sensors used to monitor equipment are inexpensive, and cloud-based technology infrastructure, like ERP systems, allows manufacturers to process and manage data from the IIoT without significant upfront hardware costs.

Each manufacturer needs to assess the current state of its operations and its vendor and customer relationships to see if IIoT technologies could meaningfully enhance performance. For some manufacturers, a robust, cloud-based ERP system will be sufficient to reduce manual effort and boost efficiency. For others, adding IIoT technologies to their operations will help revolutionize supply chain relationships and alleviate workforce challenges.

Small and midsized manufacturers face many challenges today as competitive pressures mount. The IIoT can be a powerful tool to help these manufacturers address their most pressing issues and take their operations to new heights.

Greg Price is an enterprise account executive at Sikich. Evert Bos is a technical fellow for Microsoft Dynamics 365 operations at Sikich.

>> By Greg Price and Evert Bos, New Equipment Digest, January 26, 2018

Volumetric 3D printing builds on need for speed

By using laser-generated, hologram-like 3D images flashed into photosensitive resin, researchers at Lawrence Livermore National Laboratory, along with academic collaborators, have discovered they can build complex 3D parts in a fraction of the time of traditional layer-by-layer printing. With this process, researchers have printed beams, planes, struts at arbitrary angles, lattices and complex and uniquely curved objects in a matter of seconds.

While additive manufacturing (AM), commonly known as 3D printing, is enabling engineers and scientists to build parts in configurations and designs never before possible, the impact of the technology has been limited by layer-based printing methods, which can take up to hours or days to build three-dimensional parts, depending on their complexity.

However, by using laser-generated, hologram-like 3D images flashed into photosensitive resin, researchers at Lawrence Livermore National Laboratory (LLNL), along with collaborators at UC Berkeley (link is external), the University of Rochester (link is external) and the Massachusetts Institute of Technology (link is external) (MIT), have discovered they can build complex 3D parts in a fraction of the time of traditional layer-by-layer printing. The novel approach is called “volumetric” 3D printing, and is described in the journal Science Advances (link is external), published online Dec. 8.

“The fact that you can do fully 3D parts all in one step really does overcome an important problem in additive manufacturing,” said LLNL researcher Maxim Shusteff, the paper’s lead author. “We’re trying to print a 3D shape all at the same time. The real aim of this paper was to ask, ‘Can we make arbitrary 3D shapes all at once, instead of putting the parts together gradually layer by layer?’ It turns out we can.”

The way it works, Shusteff explained, is by overlapping three laser beams that define an object’s geometry from three different directions, creating a 3D image suspended in the vat of resin. The laser light, which is at a higher intensity where the beams intersect, is kept on for about 10 seconds, enough time to cure the part. The excess resin is drained out of the vat, and, seemingly like magic, researchers are left with a fully formed 3D part.

The approach, the scientists concluded, results in parts built many times faster than other polymer-based methods, and most, if not all, commercial AM methods used today. Due to its low cost, flexibility, speed and geometric versatility, the researchers expect the framework to open a major new direction of research in rapid 3D printing.

Volumetric 3D printing creates parts by overlapping three laser beams that define an object’s geometry from three different directions, creating a hologram-like 3D image suspended in the vat of resin. The laser light, which is at a higher intensity where the beams intersect, is kept on for about 10 seconds, enough time to cure the object.

“It’s a demonstration of what the next generation of additive manufacturing may be,” said LLNL engineer Chris Spadaccini, who heads Livermore Lab’s 3D printing effort. “Most 3D printing and additive manufacturing technologies consist of either a one-dimensional or two-dimensional unit operation. This moves fabrication to a fully 3D operation, which has not been done before. The potential impact on throughput could be enormous and if you can do it well, you can still have a lot of complexity.”

With this process, Shusteff and his team printed beams, planes, struts at arbitrary angles, lattices and complex and uniquely curved objects. While conventional 3D printing has difficulty with spanning structures that might sag without support, Shusteff said, volumetric printing has no such constraints; many curved surfaces can be produced without layering artifacts.

“This might be the only way to do AM that doesn’t require layering,” Shusteff said. “If you can get away from layering, you have a chance to get rid of ridges and directional properties. Because all features within the parts are formed at the same time, they don’t have surface issues.

“I’m hoping what this will do is inspire other researchers to find other ways to do this with other materials,” he added. “It would be a paradigm shift.”
Shusteff believes volumetric printing could be made even faster with a higher power light source. Extra-soft materials such as hydrogels could be wholly fabricated, he said, which would otherwise be damaged or destroyed by fluid motion. Volumetric 3D printing also is the only additive manufacturing technique that works better in zero gravity, he said, expanding the possibility of space-based production.

The LLNL logo in 3D printed technology.

The technique does have limitations, researchers said. Because each beam propagates through space without changing, there are restrictions on part resolution and on the kinds of geometries that can be formed. Extremely complex structures would require lots of intersecting laser beams and would limit the process, they explained.

Spadaccini added that additional polymer chemistry and engineering also would be needed to improve the resin properties and fine tune them to make better structures.

“If you leave the light on too long it will start to cure everywhere, so there’s a timing game,” Spadaccini said. “A lot of the science and engineering is figuring out how long you can keep it on and at what intensity, and how that couples with the chemistry.”

The work received Laboratory Directed Research and Development (LDRD) program funding. Additional LLNL researchers who contributed to the project were Todd Weisgraber and Robert Panas, Lawrence Graduate Scholar and University of Rochester Ph.D. student Allison Browar, UC Berkeley graduate students Brett Kelly and Johannes Henriksson, along with Nicholas Fang at MIT.

>> This article by Jeremy Thomas, Lawrence Livermore National Laboratory News, December 8, 2017

Microdrones That Cooperate to Transport Objects Could Be Future of Warehouse Automation

Last month, we wrote about autonomous quadrotors from the University of Pennsylvania that use just a VGA camera and an IMU to navigate together in swarms. Without relying on external localization or GPS, quadrotors like these have much more potential to be real-world useful, since they can operate without expensive and complex infrastructure, even indoors.

(Photo: University of Pennsylvania)

One potential application for drones like these is disaster operations, but honestly, that’s just what everyone says when you ask them how their mobile robot could potentially be useful. What’s much more interesting to us are commercial applications, and with drones, that inevitably means talking about delivery. There are a lot of reasons why we’re skeptical about most commercial delivery drones, but that doesn’t mean that the idea of using drones to move things from place to place isn’t a good one.

Vijay Kumar’s lab at UPenn has been working on using their GPS-independent quadrotors for transporting payloads, and they’re doing it collaboratively—the idea is that objects that are too large or heavy for one quadrotor to move can instead be moved by multiple quadrotors working together, and ultimately, they could be the best way to move items around a warehouse.

The use of multiple MAVs can provide additional benefits compared to the use of a single vehicle when solving a task. Although the complexity of such systems increases with the number of vehicles, the additional vehicles allow the transport of payloads that cannot be transported by a single vehicle because of size and payload constraints, and can provide robustness to the system to compensate for single vehicle failures.

What’s new here is not the transportation of objects with multiple quadrotors, but rather doing it without some kind of external localization system. Each of these quads is using its own VGA camera and an IMU, and that’s it, meaning that what you see here would work just as well outside, or in your living room. You can read lots more about how this works in our previous article.

While each quadrotor can do a decent job estimating its position from camera and inertial data alone, that estimation will gradually drift away from the drone’s true location, getting worse and worse the farther the drone moves. With two (or more) quadrotors rigidly connected while transporting an object, you can combine the location estimates from each robot to optimize both of their positions, resulting in a much more accurate estimate that drifts less.

Better localization means better, more reliable performance, and even with cargo, the video above shows the quadrotors zipping around at speeds of 4.2 m/s and accelerations of 5 m/s2, a “level of agility and autonomy [that] has never before been accomplished at this scale,” according to the researchers.

For more detail, as well as a glimpse at a future of drone warehouses, we spoke with lead author Giuseppe Loianno.

IEEE Spectrum: Can you summarize how this work relates to the work we reported on earlier?

Giuseppe Loianno: This work considers multiple aerial platforms cooperating to  transport a payload. In our previous work, we considered the coordination among robots to perform tasks that are inherently distributed. A more advanced form of collective behavior is required for tasks that simply cannot be accomplished by individuals but can be accomplished by cooperation. Examples of cooperation are seen in cooperative manipulation and prey retrieval in nature. In cooperative manipulation, each robot needs to interact with the payload (and therefore the environment) and also accommodate the rigid constraints introduced between the different robots.

What are some of the unique challenges in controlling quadrotors that are collaboratively transporting an object?

The challenges in cooperative control required a new approach which allows independent control of each vehicle while guaranteeing the system’s stability. The estimation, planning and control algorithms are designed for this new “system.” For example, the localization subsystems are independent for each vehicle, and thus may provide different estimates of the load position and orientation. And this will in turn result in control actions that are not consistent with the rigid body constraint.

Would this approach scale up to more quadrotors collaborating to transport larger, heavier payloads?

The approach we proposed scales very well with large number of robots. From a localization point of view, we also show in the work that the optimization solution does not depend on the number of the vehicles carrying the structure. The control and planning approaches are also independent.

You mention in the paper that transportation tasks in warehouses is one potential application. Can you describe how such a system might operate?

Imagine you are in a warehouse where there are objects that are either too heavy either too big to be transported by a single vehicle. These objects need to be identified, picked up and moved to a final destination. In the future, we aim to have a complete system that will be able to automatically infer to an operator the number and types of vehicles needed to pick each object in a coordinated fashion and transport them to the final destination. Moreover, the algorithm will allow multiple teams to concurrently pick different objects in the warehouse guaranteeing obstacle avoidance and solving the overall transportation task in an optimal and distributed way.

What are you working on next?

We are working on pursuing experiments with multiple quadrotors and studying the tradeoffs between increased control authority and increase in inertia of the system, between improved localization estimates because of cooperation and the increase in complexity resulting from an increase in the number of constraints. We are also interested to estimate the geometry and inertia of the payload during the task. Finally, we are interested in combining this work with our previous work on grasping to perform a richer repertoire of tasks.

“Cooperative Transportation using Small Quadrotors using Monocular Vision and Inertial Sensing,” by Giuseppe Loianno and Vijay Kumar from the University of Pennsylvania, will be presented at ICRA 2018 in Brisbane, Australia. It also appears in IEEE Robotics and Automation Letters.

>> This article Evan Ackerman was published in IEEE Spectrum, January 15, 2018

The Connected Factory

What is a connected factory? It is an array of innovations that impact a consolidated, connected and flexible model of organizing factory operations. These improvements primarily relate to the ability of machines to efficiently communicate with each other, the integrated flow of data to a centralized platform, and cross-device functionality. In the past, a human was required to be on the factory floor. Now, the employees, who are responsible for monitoring and controlling production lines, can do so remotely.

The control that can be employed from remote consoles is often comprehensive, comprising a large arrangement of functions, from output level control to repair and maintenance.

Factories can now adapt to workflows in real time, by machines communicating with other machines, and humans. This system brings together people, processes and products to enable continuous delivery of value to a company’s customers.

By connecting all of the parts of the manufacturing process, a manufacturer can simplify and speed up the process of building and testing applications across every platform. Supervisors can monitor the health, performance, and utilization of all applications, workloads, and infrastructure. Business intelligence will grow by making better, faster decisions by analyzing data for deeper insights as to what is happening on the factory floor.

The connected factory can seamlessly integrate applications, data, and processes across both on-premises and the cloud. No matter where data resides, costly business interruptions can be avoided by protecting both data and applications. During an outage or disaster scenario, a disaster recovery solution will protect a range of applications, enabling an entire datacenter to be recovered in a matter of hours instead of weeks or months.

Using a common platform, equipment connected by sensors can supply helpful data about the continuing condition of equipment. This information can be analyzed to predict potential locations of equipment breakdown and production shutdown. In the event a breakdown does occur, a factory can analyze this data to determine the problem and take corrective actions to prevent future occurrences.

Using the IoT (Internet of Things), a manufacturer can connect devices, assets, and sensors to collect untapped data. This also allows a company to deliver scalable, reliable applications faster to meet the ever-changing demands of its customers. A company can focus quicker on innovation instead of infrastructure management.

Good communications between people and production equipment means that transitions among areas go according to plan and process design. Poor or no communication leads to inefficiencies and quality problems. When production equipment talks to other production equipment, it makes manufacturing flow efficiently. This is IoT in action.

New levels of productivity can be found in the connected factory, because of machine-to-machine integration and the Industrial Internet of Things, making the connected factory intelligent and aware. This incorporates using sensors rather than human decision to fine tune the operation of the machinery, using data from the production machinery to adjust workflows, which eliminates inefficient procedures, capacity attrition, wastefulness and performance bottlenecks. This is accomplished by remotely tracking, monitoring and adjusting machinery based on sensor data from different parts of the factory.

Inventory represents a substantial cost for most manufacturers, and an IoT approach can reduce excess inventory through automated replenishment. The production equipment on the factory floor can communicate when quantities are low and order more raw materials to safeguard against the line shutting down, utilizing just-in-time, cost-effective replenishment.

In order to boost manufacturing, new machines, processes and organization can be tied together thanks to the emergence of the Internet of Things and the wide spread of connectivity in the factories. Along with the IoT, human/robot teamwork, virtual reality and 3D manufacturing comprise the future of manufacturing.

This means that all industrial assets will be connected within the same factory as well as between factories. Sensors at each stage of production will exchange data in real time which will then be analyzed to guarantee the ideal operation.

Previously, robots used in factories were designed to work alone and perform single tasks. In the future, we will see more Cobots (collaborative robots), which are intelligent robots designed to work with production people in order to accomplish more with less effort.

Management can utilize virtual reality to design and simulate a product from its creation to finished product. Not only can virtual reality design and simulate a product it can also design the entire production facility, including equipment positioning, employee movement and a simulation of procedures. VR can then test the entire production from start to finish.

3D printing (manufacturing) uses only the material that is strictly necessary to build a product, eliminating the waste that we see when products are built by eliminating material. The 3D printer is controlled by a computer, depositing successive layers until they reach the desired final shape. This approach will make it possible to design lighter, more complex, and lower cost products while obtaining a substantial reduction in the industry’s ecological footprint.

These milling machines, as well as similar arrays of injection molding presses and 3D printers, are connected to the Proto Labs network. Data collected by sensors track the progress of orders and help optimize scheduling. The monitor seen on the left displays real-time updates. Image courtesy of Proto Labs Inc.

General Motors Co. has connected about a quarter of its 30,000 factory robots to the internet, and GM is realizing less down time. In the last two years, GM has avoided 100 potential failures of vehicle assembling robots by analyzing data that the robots sent to external servers in the cloud. Connectivity is inhibiting assembly line interruptions and robot replacements that can take as long as eight hours. Connecting robots to the internet for preventive maintenance is just the beginning of a surge of new robotics technology; and GM is using robots that can work safely alongside humans in the factory.

Siemens’ PLC manufacturing plant in Amberg, Germany has automated the production of its automation systems. The result is a reported 99.99885 percent perfect production quality rate. This is impressive given the plant produces around 12 million Simatic PLCs each year.

At Tesla’s Gigafactory in Nevada, mobile robots called Automated Guided Vehicles or AGVs are being used for moving items from one point to another. Besides the AGVs, the Gigafactory is also equipped with robotic arms that assist humans in making the battery packs at the plant.

Bosch GmbH has put the connected factory at the center of the company’s strategy. Bosch makes an array of smart consumer products, and is the world’s leading manufacturer of Micro-Electro-Mechanical Systems sensors, and provides a host of components for automotive manufacturers. Bosch has achieved a 25 percent output improvement for its automatic braking system and electronic stability program production with the introduction of smart, connected lines.

Bosch has pushed to remake the company’s 250 factories around the world using connected technology. The most advanced plant is in Homburg, Germany, which makes hydraulic components for cars. Connected technologies have enabled that plant to increase productivity by 10% and reduce inventory by the way of faster turnover by 30%.

The core tools needed to implement a connected factory already exist, such as sensors, controllers, big data, the Internet of Things, and cloud computing. More than a technological revolution, the connected factory is a total reorganization of the approach to production, using existing tools and placing a greater reliance on networks.

For additional information:

  1. http://ignition.altran.com/wp-content/uploads/2017/03/factory-of-the-future-towards-zero-downtime.pdf
  2. https://www2.deloitte.com/content/dam/insights/us/articles/4051_The-smart-factory/DUP_The-smart-factory.pdf
  3. http://www.iec.ch/whitepaper/pdf/iecWP-futurefactory-LR-en.pdf
  4. https://www.intel.com/content/dam/www/public/us/en/documents/white-papers/factory-of-the-future-vision-2030.pdf

>> Read more by Len Calderone, Manufacturing Tomorrow, 1/09/18

Benefits of Additive Manufacturing in Functional Prototyping

Additive manufacturing enables companies to build functional prototypes faster, allowing for more iterations and better designs.

Fig. 1. Modifying the overhanging features of the part reduced the volume and weight of the supports.

Previously, we saw how additive manufacturing (AM) can be used to more quickly and flexibly build parts that can be used as direct replacements for conventionally made components. This month, I want to discuss another “speed play” with AM: namely, functional prototyping.

For 30+ years, companies have used 3D printers to create prototypes and concept models that were primarily used for “fit and form” checks. As such, functionality was not critical, because we knew that plastic 3D-printed parts were not going to be as strong as the final injection-molded parts. Now that we can print metallic components, companies can additively manufacture functional prototypes, not just fit-and-form models. By doing so, they can save days, weeks and even months rather than wait for a machine shop to fabricate a functional prototype via more conventional methods. Additionally, companies can make more prototypes in a given time period, enabling more iterations, more customer feedback and so on, by taking advantage of AM’s quick turn-around time relative to many conventional approaches.

So AM enables speed and flexibility in functional prototyping, just as it did in direct part replacement.

Case in point is the piston crown example I have used in previous columns. In this project, the sponsoring company wanted to investigate the use of AM to directly produce functional prototypes that were taking too long (and costing too much) to make. The challenge, though, was that the piston crowns they were considering were not designed for AM; they were designed to be cast and machined, and the resulting geometry did not lend itself well to AM. The overhanging features required extensive support structures during additive fabrication, and these supports were not only difficult and challenging to remove, but also added significantly to material costs and build time—exactly what the company was trying to avoid.

Corey Dickman, Matt Woods and I got a chance to adapt the design for AM. While some of the features underneath the part are functional, many of them are not; they are artifacts of the manufacturing process and can be modified without affecting part functionality. The first modifications we made were to some of the overhanging features of the piston crown. this enabled us to reduce the volume of the supports by 55 percent (from 103.6 cm3 to 46.3 cm3) and therefore reduce the weight of the supports from 58 percent of the build to 22 percent of the build (see Figure 1).

After additively manufacturing the original and reduced support piston crowns successfully, we got more aggressive with our enhancements to improve piston performance. We created conformal cooling channels under the combustion chamber to improve heat transfer, and we added oil drains and additional oil passageways to improve lubrication during operation. Finally, we added a lattice structure on the underside of the part to improve rigidity while adding only minimal extra weight. Corey also was able to leverage machining allowances to add material near the piston rings in such a way that they were self-supporting. All combined, the enhanced piston crown design required zero additional supports to enable additive manufacture in stainless steel (see Figure 2).

Fig. 2. All combined, the enhanced piston crown design required zero additional supports to enable additive manufacture in stainless steel.

The final piston crown weighed 7.5 percent more than the original piston crown yet required 11 percent less time to manufacture. It had zero support structures to remove, and once it was machined to its final specifications, it was actually 4.1 percent lighter than the original design yet had 37 percent more surface area to improve heat transfer. In short, by designing for AM, we enabled a functional prototype that could be made in two to three days with minimal postprocessing effort, compared to two to three weeks via conventional methods.

So why should machine shops care about this? Those that subsist off making functional prototypes could find themselves displaced from the supply chain if their customers decide to leverage AM technology. It may be in their best interests to understand how to contribute to AM rather than be disrupted by it.

This article originally appeared in Additive Insights, a monthly column in Modern Machine Shop magazine.

>> Read more by Timothy W. Simpson, Additive Manufacturing, 12/29/17

Aras Takes On the Pain of PLM

In a marketplace plagued by customer frustration, one product lifecycle management (PLM) developer takes an unusual approach — starting with open software.

Aras, developer of the Innovator product lifecycle management (PLM) solution, recently released results from its PLM Benchmark Survey for Enterprise Organizations. The survey, which ran from 2015 to 2017 and was conducted by Gatepoint Research, invited participation from select executives from “a wide variety of industries,” both within and outside the manufacturing arena.

The results weren’t pretty: Aras found that more than two-thirds of the 300 respondents are unsatisfied with the PLM software they have in place. Most of the surveyed companies “have more of what we would describe as a PDM [product data management] implementation, around MCAD,” explained Marc Lind, senior vice-president of strategy at Aras. Only one-quarter of those surveyed reported being able to make changes quickly and easily — “they’re really being handcuffed by the PDM system,” he said.

Aras realized that the complexity of the modern design and development environment demands greater customizability and upgradeability, Lind explained. Aras PLM is gaining traction, he said, because “people are recognizing that just using mechanical CAD is not going to get them to the smart, connected [place] their products need to be for tomorrow’s world.” With the rise of smart devices and vehicles, “everything is moving in the direction of increasingly sophisticated mechanical design” — complicated by added electronics, sensors, and software. According to Lind, Aras brings all that design information together in a cross-disciplinary way, providing a unified view and bill of materials (BOM) for an entire aircraft, for example.

Tackling PLM Pain Points

Aras is out to make PLM a less painful proposition, starting by eliminating something that no one enjoys: paying for software.

Changing the cost equation. “Anybody can download and use [Aras Innovator] forever with no cost,” said Lind. Aras has brought a “Red Hat business model” to the PLM marketplace, Lind explained, referencing a company known for providing Linux platforms and other open-source software products.

An optional subscription plan, which includes security updates, user training, and a help hotline, is available for a fee (which varies based on the number of users). Lind pointed out that “if people stop subscribing, they still can use the software,” but most don’t stop; he reported a subscription renewal rate of more than 97%.

Accommodating a custom fit. Customers on subscription also receive software release upgrades, regardless of how much they have customized the system. That’s a benefit that “nobody else includes,” said Lind, because of the way his competitors’ solutions are structured.

According to Lind, “all other PLM systems” — including Windchill from PTC, Teamcenter from Siemens PLM Software, and ENOVIA from Dassault Systèmes — are built from hard-coded data modules with hard-coded business logic. Making a change to the system, such as expanding part numbers from seven digits to eleven, requires breaking the business model. And by doing so, “you have effectively orphaned yourself from future updates,” Lind explained.

Aras takes a different approach, and hard-codes services instead. Services for check-in/check-out, revision and version, etc., “are hard-coded, but they have no comprehension of a data model,” said Lind. Updates to those services don’t impact users’ customizations, so Aras is able to guarantee upgradeability.

Overcoming institutional inertia. “These are long-life systems,” Lind observed; in many cases, they have been in place more than 10 years, so adopting a new solution is challenging. And particularly in larger organizations, there are many different processes that are all or partially automated. “Companies are customizing their systems, even if it is just MCAD management, and they’ve had them in place a long time,” he noted. “[There’s] a lot of organizational status quo … it’s not an easy proposition to say, ‘We need a new one, let’s go for it.’”

To reduce these obstacles to a PLM changeover, Aras offers customers the option to layer the new solution over the old. “With Aras, it’s not an all-or-nothing proposition,” said Lind. He shared the example of car maker GM, which uses Teamcenter with NX — both Siemens PLM Software solutions — but needed enterprise change management, so the company layered Aras PLM over Teamcenter.

It’s less disruptive, Lind explained, to leave a legacy MCAD system in place for a time, rather than “rip and replace.” Digital transformation and the current pace of disruption are such that “you have bigger fish to fry [than] simply ripping out your old CAD management system … you really need to get control of the BOM and variance of your product.”

PLM: Perceived Like Mandate?

The level of dissatisfaction revealed in Aras’s survey is especially concerning since PLM is increasingly seen as a non-optional tool. As the survey report states, “Product lifecycle management (PLM) is a core requirement for modern product development, particularly as organizations face increasing product complexity and shorter product lifecycles.”

Lind pointed to technological advances that are making PLM ever more necessary: For the industrial Internet of Things (IoT), PLM is a critical enabler to get data in and out of the system more easily. “The digital twin is so important right now,” Lind said. “People are realizing that the virtual model from the design is not a digital twin”; rather, a digital twin fully replicates one specific real-world item, not just in its mechanical, electrical, software, and firmware aspects but in its maintenance schedule, wear, environmental conditions, etc. — all of which translates to tremendous masses of data streaming in. “If machine learning and AI [artificial intelligence] can’t get to the data, it’s either going to not work well or not work at all,” he said.

Lind is seeing “real movement across the board,” as organizations of all sizes struggle to embrace these changes. “It’s not just consumer companies — even the largest companies are realizing that they’ve got to do something … [we’re seeing] an uncharacteristically high level of motivation to change and take this seriously.” So while companies in aerospace, automotive, industrial equipment, and medical devices may be at the front of the pack, product developers in every industry would do well to pay close attention to the changes afoot — and to examine their strategy for managing product data.

>> This article by Cyrena Respini-Irwin appeared in Cadalyst, January 27, 2018

Mobile Robotics: Thinking in Terms of Fleet Productivity Is Key to Success

Autonomous mobile robots are causing a paradigm shift in the way we envisage commercial and industrial vehicles. In traditional thinking bigger is often better. This is because bigger vehicles are faster and are thus more productive. This thinking holds true so long as each vehicle requires a human driver. The rise of autonomous mobility is however upending this long-established notion: fleets of small slow robots will replace or complement large fast manned vehicles.

In this article, we will explore the basis and the future implications of this major transition. We will focus on two applications in particular: agriculture and last mile delivery. For both, we will demonstrate how autonomous mobile robots have already begun this transformation.

In this article we draw from two of our recent studies: Agricultural Robots and Drones 2017-2027: Technologies, Markets, Players; and Mobile Robots and Drones in Material Handling and Logistics 2017-2037. These detailed reports [found on www.idtechex.com] offer a comprehensive technology and market assessment, considering how robots and drones are penetrating into agriculture, logistics and material handling.

Last mile delivery vehicles: now and the future

Last mile delivery remains an expensive affair, often representing more than half of the total delivery cost. This is because it is inherently a low productivity process: small parcels must be delivered to custom destinations. This is in stark contrast to long haulage in which large loads are transported along fixed routes.

The current modes of last mile delivery all involve humans: a driver may drive a van along local routes, dropping parcels door by door; a person on a motorbike or cycle may carry one or few items to limited destinations per run; or both. These modes work and may be fast, but remain expensive despite employing many new business models and route-optimization algorithms.

Change is however underway, and we can already see the silhouette of the medium-term future: unmanned autonomous robots carrying small loads to pre-determined destinations. These robots may at first seem like strange creatures: they are smaller and slower than current modes of last mile delivery and can carry fewer items per trip, certainly making them less productive and thus less cost effective.

However, this is old thinking in which productivity is compared on a per unit basis. This is because autonomous mobility lends itself to fleet operation. In this model, one remote operator may monitor and control the work of many delivery robots. In this case, the wage and overhead of the person is spread over many robots. In essence, the fleet will magnify the productivity of the operator. Consequently, productivity must be compared at level of the fleet. Fleets of autonomous last mile delivery robots have the potential to ultimately boost the productivity in last mile delivery, thus reducing costs.  Sources for robots: first row: Starship Technologies, Alibaba, and TwinWheel; second row: DJ, Dispatch, and Teleretail; and third row: SideWak, Marathon Technologies, and Marble.

In our report Mobile Robots and Drones in Material Handling and Logistics 2017-2037, we have built a model looking at the cost competitiveness of last mile delivery robots. We estimate the operator-to-fleet-size ratio at which mobile robots become more competitive than a man on a bike. This threshold is difficult to achieve given the current retail prices for early adopters of this robot technology.

Our roadmap suggests that this threshold become very achievable in the near future. This is because these mobile robots currently travel at low speeds along well-structured and uncrowded routes performing short delivery runs. In the future, as these robots learn more, they will become better adapt at navigating more complex environments at higher speeds with less supervision, thus boosting the productivity of individual units.

Furthermore, we expect the cost to fall dramatically in the near term. These robots do not require complex hardware. In fact, they are not too dissimilar to an electric scooter integrated with a mobile phone. Indeed, we expect the hardware platform to become commoditized in the future, and the primary value to shift towards fleet management and delivery services. This explains why some in this business are already self-positioning as providers of robots-as-a-service (RaaS).

The forecasts in our report Mobile Robots and Drones in Material Handling and Logistics 2017-2037 suggest that the sales of ground-based last mile delivery robots will be modest until 2021-23. In this early phase the industry is essentially in learning mode and the activity will be limited to trials or modest sales for use in sparse and highly-structured environments. The growth phase will then commence. In this phase, the robots will also become increasingly ready to handle more crowded environments and their speed of travel and navigational technology will significantly improve.  The unit sales boom and the hardware product will become commoditized. This way we will witness the rise of fleets of small-sized last mile delivery robots.

For further information please refer to our report Mobile Robots and Drones in Material Handling and Logistics 2017-2037. This report provides a comprehensive assessment of robots and drones in material handling and logistics. It considers many uses cases beyond last mile delivery robots including automated guided vehicles/carts; autonomous industrial material handling vehicles, autonomous mobile carts, autonomous mobile picking robots, and autonomous trucks

Agricultural vehicles: now and the future

The traditional thinking is to develop larger, faster and more powerful agricultural vehicles like mammoth tractors to boost productivity. This has been sound thinking thus far: such vehicles amplify the productivity of the driver, enabling it to cover more ground per hour in a limited time window that the weather might permit.

As discussed in our report Agricultural Robots and Drones 2017-2027: Technologies, Markets, Players, agriculture is the leading adopter of autonomous mobile technology: we estimate that more 320,000 autosteer and auto-guidance tractors will be sold in 2018 (up to level 4 autonomy). The technology is already evolving towards full navigational autonomy (level 5). Indeed, as the numerous prototypes demonstrates the commercialization emphasis has shifted from addressing technical issues towards tackling commercial and behavioural challenges.

The rise of autonomous mobility may also enable another type of agricultural vehicle that might also at first seem like a strange creature: the small, slow and lightweight agricultural robot (agrobot). These robots are evidently less productive than a larger vehicle therefore you might wonder why are they being seriously considered by players large and small around the world?

The answer here, as was the case in last mile delivery robots, is in fleet operation. Autonomous mobility eliminates the need for a human driver per vehicle, enabling the cost of the remote operator to be spread across the fleet. This instantly lowers the operational cost per unmanned robot. As was the case with last mile delivery robots, here we also find that achievable operator-to-fleet-size ratios exist at which fleets of small agrobots become attractive. The seeds of a paradigm shift in the way we envisage agriculture machinery have been sown. In this shift few large, heavy, and fast vehicles are replaced with large fleets of small, slow and light weight agrobots. Images source for agrobots:  first column: FENDT; Queensland University; Naio Technologies, Australia Centre of Field Robotics, and ecoRobotix; second column: Earthsense, Idaho, and Kongskilde; and third column: Rowbot, Ibex Automation, and Vinerobot.

This alone however may not be compelling enough since large tractors will also become autonomous, lending themselves to fleet operation.  Such fleets will reduce the headcount, cut down the wage bill incurred in farming and open the door to limited precision farming.

Fleets of small agrobots will go further in that they will enable site-specific or even plant-specific ultraprecision farming. These slow-moving robots will give each site the customized attention that it needs all the way from planting to harvesting. This would be akin to industrialized gardening. This will boost yields and will also significantly reduce chemical use. Furthermore, these light robots will cause no soil compaction and will drive down energy consumption, emitting no noise and CO2 since they are electrically powered.

Here too we are also at the beginning of the journey. We also see numerous such robots developed at start-ups and research entities. We are also witnessing more engagement from larger firms with some promising to take such robot swarms towards full production. This is why our report forecasts that autonomous small mobile robots will grow to more than $500M annual sales in 2028.

For further information please refer to our report Agricultural Robots and Drones 2017-2027: Technologies, Markets, Players. This report develops a detailed roadmap of how robotic technology will enter into different aspects of agriculture and how it will change the way farming is done. It will consider many applications and technologies including static milking robotics, mobile dairy farm robots, autosteer tractors, autonomous tractors, unmanned spraying drones, autonomous data mapping drones, robotic implements for de-weeding, autonomous de-weeding mobile robots, robotic fresh fruit harvesting, robotic strawberry harvesting, manned and unmanned robotic lettuce/vegetable thinning/harvesting and so on.

>> Contributed by IDTechEx, Robotics Tomorrow, 1/18/18