EDEM Simulation
By Corinne Bossy

Do you design heavy equipment such as truck bodies, dozers and excavators?

If so then I’m sure you are very familiar with tools such as Finite Element Analysis (FEA) and Multi-body Dynamics (MBD) to help design your equipment. When using such tools one challenge is to determine the loads acting on equipment.  Hand-calculations, assumptions, or physical testing are common methods but they have limitations especially when it comes to equipment intended to handle bulk materials such as rocks, ores, soils or sand. You could be dealing with large quarry rocks that are generating high force impacts; or perhaps a fine but highly abrasive material such as sand; or a cohesive soil that might stick to equipment. This sort of variability means it is very difficult to predict how materials will behave with a piece of equipment and impact on its performance.



This is where bulk material simulation and EDEM software technology come into play. EDEM simulates the behavior of bulk materials and provides accurate loads for a range of materials types including rocks, gravels, ores and more cohesive materials like clays and soils.



While this technology has traditionally mainly been used by a small pool of experts in the field, latest developments mean it is now easily available to a wider number of engineers.

The development of key partnerships between EDEM and leading CAE companies has led to the release of specialized products known as ‘EDEM for CAE’ which connect to widely used FEA and MBD platforms. With these tools, design engineers can add realistic bulk material loads directly and easily in their structural analysis and their system dynamics analysis. This means they no longer have to rely on hand calculations and assumptions to predict the effect of materials on their equipment, which in turn leads to improved designed accuracy and performance, not to forget reduction in the frequency and cost of physical prototyping.

At present the EDEM for CAE range includes EDEM for ANSYS, EDEM for Adams and EDEM for LMS Virtual.Lab Motion. The tools have a very easy-to-use interface and do not require any expertise in bulk material simulation to operate. All the analysis is performed in the host software in an environment familiar to the user.



To find out more about the EDEM for CAE tools and how they can benefit your design processes check our webinar series. Our EDEM engineers will take you through applications examples and show you the workflow of each tool.

WEBINAR SERIES

In addition, if you’re ready to jump-in and learn how to use each tool, check our free eLearning courses. You’ll even get a free trial once you complete a course.

Note: if you are interested in bulk material simulation but use another FEA or MBD tool, we do offer coupling solutions with other providers. Just get in touch to discuss your needs.

Source: EDEM Simulation Blog

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Siemens PLM
By TParella – Siemens Experimenter

As a wave of new technologies continue to drive customer expectations, manufacturers, especially those in highly complex industries, such as automotive, aerospace and energy, are racing to adapt.

 

Customers are demanding products that are enabled by the new technologies—embedded electronics, Internet of Things connectivity/communication, and advanced materials—and they expect them to be delivered faster than ever before.

 

This trend will continue into the foreseeable future as other fast-maturing technologies, such as data analytics, augmented and virtual reality, and other disruptive innovations are embedded in products. The increased number and variation of new technologies means manufacturers must manage more data and processes hand-offs throughout the product development and management workflow.

 

To control process costs and ensure they can meet future demands, manufacturers themselves are turning to new technologies and work models that help them deliver increasingly complex products faster and in a more streamlined manner.

 

Topping the technology list is digitalization, a system in which digital technologies are integrated into all business processes, creating an end-to-end digital thread of product, production system and process data, from design through production. Available to anyone at any time, the digital thread allows product engineers and operators to work concurrently because they have immediate access to the information they need. An electronics engineer can work on components while a software engineer writes code, for example. One doesn’t have to wait for the other to finish.

 

Digitalization also makes it possible to incorporate new, innovating ways of working as a multi-disciplinary team, such as using generative design and Convergent Modeling.

 

Generative design and Convergent Modeling

Generative design is a breakthrough capability in product design. It leverages algorithms based on previous design knowledge and high-performance computing to autonomously deliver optimum designs based on specific shape and performance parameters. Mimicking nature—only faster—generative design can deliver more options than feasible with traditional design tools and, likely, many options that engineers wouldn’t have considered. This enables engineers to experiment more and yet determine a final design more quickly, compared with traditional approaches.

 

Convergent Modeling comes into play with the inevitable need to combine generative design models, which are faceted, with traditional models that are b-rep. Siemens’ Convergent Modeling solution allows designers to combine facets, surfaces and solids in one model without converting data, previously a time-consuming, error-prone process.

 

The benefits of a digitally empowered multi-disciplinary design team

Digitalization, generative design and Convergent Modeling are integral to effective multi-disciplinary design, which brings together engineers from different disciplines to work together on a product that encompasses multiple types of systems. By enabling mechanical, electrical, electronic and software engineers to work quickly and concurrently on a product design no matter the model source language, these technologies, combined with multi-disciplinary design, can shrink timelines and accelerate speed-to-market and customer responsiveness. 

Further, such a model enables:

• Quality control, by reducing errors caused by redundant versions and missing information
• Visibility and accessibility to the data throughout the product life, which provides the foundation for standardization of processes, as well as incorporation of personalized user interfaces that provide guidance at each step
• A ready model for adding transformative technologies such as additive manufacturing, embedded sensors and data analytics to processes and products
• The ability for engineering teams to exploit the trade-offs between and among the disciplines to quickly identify the optimum design


Ultimately, the approach tames complexity by bridging the gap between design planning and execution by reducing the manual effort and cost of managing and tracking product data. With such a system, manufacturers can focus people on the right tasks, with the right data, to make the right decisions at the right time.

 

Companies that are adapting multi-disciplinary design by bringing together humans, high-performance computing and an end-to-end generative design thread, across the design lifecycle, are setting the pace of innovation. In the race toward the seamless integration of the cyber and physical systems, early adopters will not only deliver complex designs more quickly, they’ll upend industries as they perfect and accelerate speed-to-market for differentiating innovation.

Teamwork works

The benefits of a multi-disciplinary approach was recently demonstrated at the Siemens test center for industrial gas turbines in Lincoln, England, where a global team collaborated to optimize gas turbine blades and their production. The project brought together engineers and materials experts from Lincoln, Berlin, and the Swedish municipality of Finspong.

 

“Within just 18 months, the international project team succeeded in developing the entire process chain from the design of individual components, to the development of materials, all the way to new methods of quality control and the simulation of component service life,” according to reports.

 

In addition, Siemens tested a new additively manufactured blade design with a fully revised and improved cooling geometry. Willi Meixner, CEO of Siemens Power and Gas Division, said by using additive manufacturing, “we can develop prototypes up to 90 percent faster.”

 

The power of people

As the rush of new technologies remakes how products are designed, produced, delivered and maintained, it’s easy to forget that people and their work processes remain a critical factor of manufacturing success. Indeed, in a world of constant technological change, people—or more specifically, teams of people—become more important. They are needed to access and apply the appropriate knowledge when and where it’s needed throughout a product’s lifecycle.

 

In today’s fast-paced marketplace, manufacturers must arm their teams with the right technology—with the digitalization of the product development and management process—and adapt new team-based workflows, such as multi-disciplinary design.

 

Because the future belongs to manufacturers that can manage their process knowledge, so they are able adapt to necessary changes while staying in control of processes and performance.

 

This concludes our introductory series on multi-disciplinary design and generative design. 

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Siemens PLM

By Indrakanti “Chaks” Chakravarthy – Siemens Theorist


The world of manufacturing continues to evolve as it becomes increasingly global and digital. Many manufacturing companies are struggling to make the transition to digital manufacturing. But the companies choosing to move forward with a digital strategy stand a better chance of not just surviving these changes, but thriving in them.


One company hedging its bets on its digital strategy is Nexteer, a Michigan-based Tier 1 steering and driveline supplier with global locations. Nexteer is embracing the changes digital manufacturing is bringing to the market, and it’s implementing processes throughout its digital strategy that will allow its business to bring its “digital map” to life and continue to bring value to customers and shareholders in the future.

 

Nexteer’s process, as well as the digital manufacturing issues it currently faces, were highlighted in a presentation from Dennis Hoeg, vice president of manufacturing, manufacturing engineering and enterprise systems, at this year’s Digital Twin Summit. Some of Hoeg’s observations included how: 

 

• The company’s digital strategy begins with the company’s culture. Hoeg said that Nexteer believes the people throughout the company will determine how successful its digital strategy actually is, so it created a digital map that will make sure the right processes are in place moving forward. This digital map includes virtual manufacturing and virtual design processes, global plants, current technologies place, as well as space to accommodate for future changes.

 

• Traceability is a key part of Nexteer’s business, so the company is ensuring its digital strategy efforts account for this important safety component. Traceability is a core part of the company’s manufacturing intelligence model, as well as the quality and product performance side of the business, to ensure that its products and processes stay safe.

 

• Nexteer works with a lot of data. The company has data coming from sources such as manufacturing execution systems (MES), traceability, lock control, enterprise resource planning (ERP) and PLM. One of Nexteer’s major goals is to connect all of its important operations so it can predict performance before it begins testing. The company wants to accomplish this with fast, complete problem-solving processes that allow for mining databases, understanding important data in them and then being able to analyze, predict and control key parameters. Hoeg said this can be a difficult dream, but one that’s very possible to achieve.

 

We hope you enjoy this video.




VIDEO TRANSCRIPT

TOM MAURER: Ok. Our first speaker this afternoon is Dennis Hoeg from Nexteer. He’s vice president of manufacturing, manufacturing engineering and enterprise systems. Dennis, the stage is yours all.

 

DENNIS HOEG: Alright, thanks. Good afternoon, everyone. I am very thankful to represent Nexteer here at this great event, and it’s been a great event so far today. Now we’re going to talk a little bit about manufacturing, which is deep in my blood. 

 

A little bit about Nexteer. We’re a tier 1 steering and driveline supplier to many, many OEMs. We’re headquartered in Michigan, but globally we have 13,000 employees, and we’re a $4 billion company.


Our product overview: we have steering columns and I-shafts. We have driveline products, electric power steering, hydraulic. And then in the center, you’ll see, and many of you talked about this – this morning ADAS [advanced driver-assistance systems] and automated driving technologies – a big move towards that. You’re going to need steering in these cars. Whether you need a steering wheel or not, you still need steering – an important part of what’s coming in the future.

 

Then, this is our manufacturing footprint, and an important part about this is going to come up later when I talk about integration and manufacturing. Just kind of keep an idea that we are a global company in a lot of different locations.

 

You think about digital manufacturing, and we talked about already this morning, the landscape is changing, and so are we. We’re really working hard at changing how we do manufacturing and really integrating the business.

 

Also mentioned this morning, the term “shift left.” We use that term as well. This is our business process: from product design, through process design, through industrialization, into production. Our whole goal is to shift to the left, reduce the lead time, shrink what it takes to do our business.


It’s really important going forward, because these digital solutions and this architecture, we believe, is a key part of future customer and shareholder value. It really does bring value to the company, and brings value to those we serve.

 

But it starts with people. On the left is our culture. Culture is a big part of our company, and a big part of who we are. The people inside the company are really the pillar, that are implementing this digital strategy across the company.

 

So we created an organization. Their job is to implement these technologies within manufacturing and across the enterprise, a really important part of this strategy beyond just the technology itself.


You’ll never be able to read this map, but this is our strategy map for the digital trace across the company. We started out trying to figure out, how do we get this on one page? So this is our first attempt to trying to get our strategy on one page. As technology changes and time changes, there’ll be a 2.0 and a 3.0. But we’re going to keep this updated, because it starts on the right with the virtual manufacturing and the virtual design, and works our way through the plants globally, and with each of the technologies that we’re applying and the future ones that we’re working on as well.

 

So this is our digital map for the future. It’s also a dream. You know, you’ve got to dream big. This is our dream. But I don’t think it’s that big; I think it’s very doable.

 

I’ll talk about a couple of segments, what we’re doing inside of this digital trace inside that map. One thing is, we’ve been using TeamcenterNX and the drawing and design tools for a long time. We integrated a new PLM system here a few years ago, and now we’re moving on to integrate the teams that are manufacturing part of this to add to that database all of the manufacturing details that go along with the product details. 

 

You know, a lot of us have what you see on the left, are document centers full of paper and that out on the floor. Not the ultimate goal, but an ultimate outcome of doing this, is going to get to a paperless factory. We know that’s going to be the outcome, and we also know that that way, we’ll have the latest information in front of those people who need it.


All of this is around digital standards. We start with a standard machine design; we call it SMD. But what is it, is it’s the base design that we’re going for to meet the requirements of the product. And then you see, there’s folders, so to speak, of categories inside of this machine design that has all the parameters around the machine: machine design itself, operator interface. It could be standard work, operator control plans, quality documents – everything that’s required tied to that job, tied to that database.

 

The other part is our manufacturing structure, and this is a picture of what we’re implementing across the company. We’re calling it our manufacturing intelligence model. If you look to the right, it’s traceability. We’re a safety product supplier; traceability is a key part of what we are and what we do, so that’s a major part of what we’ve implemented already.

 

Then, we’re working to the left. We’re implementing maintenance operations. This quality system; warehouse inventory operations, which is internal to us; we already have an ERP system for external; and then MES, how to tie all this together inside our production operations.


The key part of this is integrating all of this. You see on the left, the business system, all the production and the quality systems. We have models inside of each of those that we’ve implemented. The key about this is integrating them together, getting them in the right database, making sure that database is clean and then being able to integrate that all together.

 

How does this all fit together? Why are we trying to do all of this? I’ll go back to our map. Here’s a case where we have a product A that’s manufactured in five different locations globally. It’s important to understand that these things are very similar: similar machines, similar operations, similar tools, similar requirements.

 

How do we make sure that we’re optimizing all of those and that they’re at the best value and at the right requirement level? We’re sharing lessons learned. We probably all struggle with how do we get lessons learned integrated back into what that main design is and the standards going forward.

 

When I think about data connections, this is where the database part comes in. You have machine and operations analysis. That comes out of the MES, the ERP databases, and the standards are coming from PLM. It’s important all three of those databases are accessible and integrated.

 

Then on the quality and the product performance side, there’s traceability, which is very critical to us. There are on some of our products, for example, we carry at least 1,500 lines of information on a completed product when it goes out the door. You talk about having all this data. Do we look at all of it, no? But it’s important to have that, because we’ve found cases where the data itself wasn’t important, but the relationship between some of the data was important. Just having that data and being able to analyze it is really critical to us.

 

Then there’s warehousing. How do I connect what’s coming in the door? How do I connect what comes through the supply chain to us, and then integrate it in the quality side and into the performance side of our products? Then always it gets down to cost – what’s our cost? What’s our opportunity? That’s a connection between MES and ERP.

 

You can see there’s a lot of need to connect all these databases together and to use that for what we have to do to improve our business and operations.

 

If you think about performance improvement, I think about it as a math equation. You’ve got this function of X, and I’ll pick on a test parameter – I’ll pick friction, for example. At the very end of the line, or very close to you in a line we test for friction. There’s a lot of things that go into that friction level before it even gets to the end of the line.

 

We need to connect, and we are starting to connect, with all of those operations that are important, trying to figure out which ones are important, connect the data from those. This is where I strongly believe we can get to the point of predicting performance before we even get to the point of testing for it. If you can predict for it, now you don’t have to do the test for it anymore. You can do something different. You don’t have large system costs tied up at that final end where you’re testing – you can take care of it in a component level or in a subsystem level.

 

But you think about where all the data comes from, it’s the same thing. It comes from MES, it comes from traceability, it comes from lock control, PLM. There’s even operator interfaces that are tied to some of this.

 

I really believe that we can get to faster and more complete problem-solving by digging into these databases, understanding what’s important and then start to do the analysis, the prediction, and then controlling some of those key parameters that are well before the point where we test today, and then spend that time later doing something else that adds value to the product. I know that may be a difficult dream, but I know it’s very possible. We’re seeing gains already in some areas that prove that; I know that’s very possible.

 

I like to say we’ve been hearing this recently from many other companies, so I stole it. But I think it’s important to think big. I go back to our picture of what we believe is our vision of what digital trace should be.

 

But think big, and then start small. You know you’re not going to conquer the world, but pick what you want to pick that’s going to make a difference for you.

 

And then fail fast. You guys talked about this morning about doing iterate changes that were going on inside software development. Same thing with us: fail fast. Check, fix, check, fix, do the PDCA [plan-do-check-act] cycles as quickly as you can.


Then scale quickly. Sometimes that’s hard. It’s easy to just start small. It’s easy to fail this. But we have to learn and have to really push to scale quickly, because the opportunity is there as long. As we can define and get our systems to where we want them to be, we need to just scale it up as fast as possible. If I think back about our map and that product that’s in five different locations, it’s critical that those five locations get integrated together.

 

Some key takeaways I’ll give you from where we are so far.

 

I know that reduced lead times really are going to impact our customers’ shareholder value. It’s a big deal to us, and it is not that difficult to do. It’s a great opportunity for us.


Complete analysis of our production process, of our designs, can be done now following a global standard. Using these databases, integrated databases, to help ourselves. That’s a key to me going forward of our manufacturing and process design and analysis.

 

Lessons learned and continuous improvement. How do you integrate that back together? We’re going to do it inside of the PLM system, but get it back to a standard. That’s really important. How do you get those lessons learned from all these different locations of all these different points back to a common place?

 

Proactive solutions. Be more predictive.

 

Also, this global collaboration. It’s difficult sometimes to do the collaboration globally. But when you get the data, when you get these digital systems together, I believe it’s a lot easier to do the collaboration that needs to go on.

 

And, again, passing those lessons learned.

 

For us, technology really is allowing us to change how we manufacture. It’s really a difference in the world today. To me, the future is really exciting about being in manufacturing.

 

Not a lot of people stand up and say it’s really exciting to be in manufacturing – but it really is.

 

Thank you for your attention.

 

[Applause]

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