Posts Tagged ‘NX’
Daimler is now using NX CAD in all business units after successfully completing the largest CAD software migration ever. Today we look at the factors behind their success.
The transition started back in 2010 with an announcement that shook the CAD and automotive worlds, but really it began before that, when Daimler moved from CATIA V4 to V5.
“Such an upgrade is like a change of systems,” says Professor Alfred Katzenbach, former director of IT Management in Mercedes-Benz R&D department. “We decided to ask ourselves the strategic question, ‘Is this the best option for the company?’ at the next possible opportunity.”
That opportunity came and with it brought a deeper look into Daimler’s current CAD solution and processes. You can read more about the details behind the decision in my previous post, “Why Daimler Made the Switch to NX.” Key factors were improved efficiency, sustainability, and reduced cost. Teamcenter integration also played an important role.
Moving an entire automotive OEM to a new CAD system is no small task, but Daimler managed to do it and improve their processes at the same time. How, you ask?
To start, Daimler had to train more than 6,200 users on how to use NX. They created role-based training modules, 33 in total, in both German and English to accomplish this. Users also could take special courses in Japanese, Portuguese, Spanish, and Turkish.
It was here Daimler saw the opportunity to improve their business processes by getting everyone on the same page. They used real parts in production for more than 250 unique business cases to determine best practices. From these, they created tutorial videos for a reference library. Now their engineers are able to refer back to this material, and everyone uses the same process.
Of course, the users are only half of the equation. What about all of those legacy parts? NX offers content migration tools to the help with the task, but the credit truly goes to the employees at Daimler. More than 300 employees from the R&D (research and development) department in Bangalore, India helped to migrate CAD data from CATIA V5 to NX. That included the migration of 235,000 CAD parts.
Daimler’s CIO Dr. Michael Gorriz shares his appreciation for everyone who worked on the project. “I would like to thank all the people who have worked so hard to complete our biggest IT migration in recent decades to the planned schedule,” he says. “The introduction of the new design and product data software not only means that we are well prepared for the future; we have also taken the opportunity to bring our engineering processes in line with the highest standards in the automotive industry.”
The last part of the equation, of course, is the OEM’s suppliers, all of whom either already used NX or have since made the switch in response to Daimler’s overhaul. Siemens and Daimler will continue to work closely together as we move forward.
Congratulations to Daimler and all who made this transition not only possible, but a true success!
To read the complete Daimler press release this post is based on, go here.
CAE automation empowers your entire team to not only do more, but to do better as well. You can eliminate the simulation bottleneck that delays product development when you implement the relatively easy and cost-effective solution of CAE automation.
Let’s back up for a minute though and first look at the problem. What causes a simulation bottleneck in the first place? Chances are if you’re reading this, you don’t currently have a CAE automation process in place. You also have too few CAE analysts to meet your company’s simulation demands.
Your designers are left to wait as the analysts try desperately to catch up with the simulation backlog. By the time they do, the design process has moved forward without any feedback or results from the CAE team. Or worse, the product development process is delayed and now you won’t deliver your product on time.
Your customers are now unhappy, you’ve lost money, and your reputation is damaged. But think about your employees too. The analysts are stressed from the pressure of being perpetually behind while your designers are left without the information they need to improve their designs.
CAE automation eliminates these problems. CAE analysts can automate their processes to enable the design team to perform basic checks of their designs. The benefit here is twofold: The analysts are then free to focus on complex simulations that drive innovation, and the design team makes more informed design decisions that lead to better products.
CAE automation thus relieves the pressure of a bottleneck, so not only is there an increase in the amount and quality of work being done, but your team faces less stress and is therefore happier. Studies show happiness increases productivity.
Achieving the benefits of CAE automation is easy with NX CAE. The software provides CAE analysts with the tools they need to guide the design team. Your expert simulation analysts can capture and share their processes with less experienced engineers and designers using CAE automation processes developed with NX Open.
The step by step instructions are easy to follow, and your analysts can even include images to show the designers what to look for at each step. They can even attach documentation.
NX also provides some great out-of-the-box CAE wizards. NX Nastran powers them, but your engineers can launch them direct from the NX modeling application. Stress and Durability wizards guide your team to simulate the ability of solid components to withstand a variety of loads and avoid undesirable vibration modes.
Learn more about what CAE automation can do for you!
Product Manufacturing Information (PMI) streamlines the product development process, allowing engineers to focus on designs that will differentiate your products from others on the market. When implemented correctly, PMI saves time and money, and even makes your engineers happier—and thus, more productive.
What is Product Manufacturing Information?
Product Manufacturing Information (PMI) embeds information about how to manufacture, run analysis on, or inspect a product directly into the 3D CAD model, so design engineers can communicate this information to other departments without having to create a 2D drawing.
PMI includes geometric dimensions and tolerances (GD&T), surface finish, material specs, 3D annotations, and more.
Two words you might hear associated with PMI are drawingless and paperless. It’s easy to get them confused, but they are two different things:
Drawingless – No 2D drawings. Your company uses only 3D annotated models.
Paperless – No paper. Your company uses only electronic formats.
It’s also important to note that just because your company uses PMI does not mean you have to eliminate 2D drawings all together, nor do you have to go paperless.
Why use Product Manufacturing Information?
At a time when engineers are overworked, PMI reduces the workload to make the most of a shrinking workforce. Less 2D drawings are needed, so your engineers aren’t tasked with the tedious process of creating them. Instead, they simply embed the Product Manufacturing Information directly in the 3D model. It’s easier, so it saves time and makes the engineers happy. Studies show happiness correlates with productivity, so expect to see a boost there as well.
PMI also reduces mistakes due to communication errors, because the same information the engineers embed gets used downstream by manufacturing and other departments. Fewer mistakes mean savings in cost and time because your company won’t have to redo anything, and you’ll also see improvements in product quality.
How do you get started with Product Manufacturing Information?
Change is hard for people, even when the benefits are obvious. This can make implementing PMI in your business a challenge. Tech-Clarity has a great guide to help you get started. You’ll have to help your team understand the value PMI adds not just to your business but to their individual workflow and processes.
Check out the full Tech-Clarity guide for more tips! How-To Guide for Implementing PMI: Making Product Manufacturing Information a Strategic Advantage
Next generation manufacturing is all about production systems that can think for themselves, much like self-driving cars…
Do you remember how you felt when you first heard about Google’s self-driving car? If you’re like us your reactions probably evolved from: “Interesting,” to “Well, if anyone can do it, it’s Google,” to “This is really going to happen, and probably in my lifetime.”
The company, which is best known for a product that has nothing to do with cars, transformed the collective thinking about cars and their potential. Google is attempting to have a similar effect on other industries, such as networking and medicine. Google allows its employees to work on “audacious” projects, which seems to be a core element of the company’s philosophy.
Siemens is equally as innovative in terms of its audacious vision of next generation manufacturing. We are making significant investments in our own production capabilities – digital manufacturing software, automation equipment, and communication protocols – to learn how to manufacture smarter and lead our customers into the future of manufacturing.
Ultimately, in the not-too-distant future we see this: a manufacturer receives a digital model of a new product, and based on the information in the model, the production environment configures itself to produce that product.
Some refer to this scenario as “self-organizing manufacturing processes for highly customizable products.” It represents a global revival of the manufacturing industry, and is being driven by government funds, market forces, and technology megatrends. In Germany this endeavor is called Industrie 4.0. There is also the Smart Manufacturing Leadership Coalition in the US.
Why do industries need this? And why would governments sponsor it? From industry’s point of view, next generation manufacturing offers a way to meet customer demands for new, high-quality customized offerings at ever-shorter time intervals. It also has the potential to reduce resource utilization, which will help manufacturers cope with growing cost pressures.
From a governmental perspective, one driving factor is the fact that people in emerging markets still need many things. A recent article in Time, GE Makes a Big Bet on Manufacturing, called this a megatrend and described it this way: “. . . emerging-market economies are entering a period very much like the post-World War II period in the US. Countries need houses, bridges, roads, airports, and all types of consumer goods in unprecedented quantities.”
Countries such as the US and Germany would like to have those products manufactured within their borders, so that their workers and their economies benefit from some portion of the $20 trillion per year that McKinsey Global Institute estimates will be spent in this way by 2025. Factories capable of autonomous production will be able to quickly adapt products to the wide range of consumer preferences found in the emerging-market nations.
The Internet of Things
In this article, we will refer to the move toward self-organizing manufacturing processes more simply as “autonomous production.” A key technology megatrend driving autonomous production is the Internet of Things (IoT).
In popular parlance, the IoT refers to “things” – such as Nest thermostats, glucose-monitoring devices, cars with collision-detecting sensors, even pets with embedded microchips – that relay information about themselves via the Internet to someone, or more accurately, to some computer, that uses the information in an intelligent way.
The IoT can dramatically change the status quo. People with diabetes, for example, are much less likely to go into a coma if they can receive a notification on their smartphone that their glucose is low. Losing a pet no longer means printing and distributing flyers and hoping for the best. You can immediately see the animal’s location on your smartphone.
The IoT is not limited to consumer products. The aviation industry, for instance, is undergoing an IoT-based revolution by harnessing information generated by engine sensors. Engine manufactures now have access to enormous amounts of data about engines in flight, which they are using to find ways to reduce fuel consumption and become aware of anomalies while a flight is in progress rather than after the fact. This is changing entire business models, with Rolls Royce and GE now contracting for hours of operation, not sales of engines.
Implications of IoT for autonomous production
The IoT has been made possible by massive technological breakthroughs in computing power, miniaturization of wireless sensors, high-capacity networks, and big data analytics. Another important element is the fact that each of these technologies has come down so far in price, especially with the availability of cloud computing, that its use can spread widely.
All of these technologies – computing power, miniaturized wireless sensors, high-capacity networks, and big data analytics – are already in use to some extent in manufacturing. So it makes sense that we are beginning to see an industrial application of IoT. In fact, there’s now a term for it – the Industrial Internet of Things (IIoT).
The deployment of the IIoT in manufacturing plants is making it possible to extract much more information about the production process than has previously been possible, even though manufacturers have already been collecting a great deal of production data. The more extensive dataset made possible by the IIoT will ultimately move us toward autonomous production by making it possible to quickly update a production model to adapt to changing conditions, such as a custom order.
Compare this with the current practice in which a production system is designed and optimized to execute the exact same process over and over again. In an autonomous production scenario, manufacturing systems will have the flexibility to adjust and optimize for each run of the task.
Let’s use robotic operations as an example. Today robots are programmed to perform specific tasks. As long as certain, pre-defined surrounding conditions are met the robot will always execute the task identically. In a future with autonomous production, the robots will receive a task and will have to determine how to perform it in the most optimized way. Theoretically, for each run of the task, the work may be done differently.
This will also hold true for a complete system. For instance, in the framing station of an automotive assembly plant, where major parts come together to form the complete body, each major component will be measured and the system will adjust itself to make the mating between the components optimal. Or each door panel opening will be measured and the best matching panel will be selected to the specific body (vs. the current just-in-sequence method).
Where are we now?
Even with the growing proliferation of the IIoT, we are not at the point where autonomous production is widespread or routine, although there are some plants implementing some elements of it today. What is already widespread and routine, however, is the solid foundation for autonomous production, which can be seen in practice in the many plants that have adopted digital manufacturing.
Digital manufacturing solutions provide manufacturers with vast capabilities for designing and evaluating their processes virtually. In a digital manufacturing environment such as our company’s manufacturing planning and management solutions, the physical world is replicated in a model-driven database. Digital tools and methods are used to design the physical manufacturing system, including its logical controls.
The result is a comprehensive virtual model of the manufacturing process that crosses multiple engineering disciplines such as tooling, process, logistics, quality and product. Digital simulation tools make it possible to validate and optimize the processes, tools and the control algorithms, and the interactions between them, all in a virtual environment prior to commissioning the system on the shop floor.
Beyond digital manufacturing is the digital factory, which is comprised of additional technological layers. A digital factory requires an infrastructure to connect the devices, the ability to identify where connectivity legitimately adds value and is not merely intrusive, and software platforms that will unlock the torrent of data.
Siemens is delivering big parts of these concepts today with its Digital Factory portfolio. The solutions included here – seamlessly integrated hardware, software and services – are already enhancing the flexibility and efficiency of many company’s manufacturing processes.
To put the Digital Factory in its proper place on the road to autonomous production, let’s look first at how it can improve manufacturing flexibility, since that is a critical element of autonomous production. Traditional production uses a sequential process flow between production modules (a moving line), where each module has a dedicated task to perform in a given sequence.
A flexible process flow between production modules, made possible by Digital Factory solutions, allows different modules to be configured for each production instance. Such a production system is more resilient to changes, and allows greater variation in manufacturing scenarios (production mix, production volume) and product range.
Let’s also look at how Siemens Digital Factory solutions improve efficiency. Digital Factory solutions optimize production asset utilization by constantly monitoring, controlling and analyzing the production ecosystem and making online decisions. They can do this for physical assets, such as machines, inventory or energy consumption, as well as for more amorphic assets, such as time to completion.
The next steps are already being taken
Additional technologies are still needed before autonomous production is a reality, but they are arriving on a regular basis. For example, Siemens PLM Software and our partner, Bentley Systems, recently introduced a new point cloud technology that makes it possible to capture the exact position of the production and logistical assets on a shop floor, providing in almost real-time the actual conditions of the shop floor.
In this past, this task took weeks, with many engineers needed to scan and measure the production facility. By eliminating that step, the new technology will permit faster modifications, a critical requirement for autonomous production.
Similarly event-driven simulation, (also known as discrete event simulation) which is already available, will play a bigger role as a fundamental tool for enabling autonomous production. This is because behind the flexibility and autonomy of this type of production scenario, there are very rigid rules that the system must adhere to. The self-driving vehicle is a good example – without strict rules, these cars would wreak havoc. The challenge is to move from nominal planning to variable planning, where the result is driven by the varying surrounding conditions in the ecosystem.
We already know that manufacturing is a key driver for economic growth, attracting investments, spurring innovation, and creating high-value jobs. All of the breakthrough and developments that are happening now – the building blocks for autonomous production – are already adding economic value. Imagine what will happen when autonomous production is routine.
Not only is it likely to be the revival that many predict for the manufacturing industry. We also see autonomous production as a way to address many global challenges such as a growing and aging population, climate change and resource scarcity.
It’s an exciting time indeed and Siemens is proud to be a leading force driving technological advancements to make autonomous production a reality. Keep watching Siemens as we deliver the results of our own “audacious” projects in the years to come.
To learn more, download our “PLM for Manufacturing” white paper about how to design and validate processes in a digital manufacturing environment.
Simulation is critical to product development, and more companies are starting to sit up and take notice. You can lower costs, save time, and improve the life and durability of your products all by using simulation. So when you invest in a software solution, you want to be sure it not only is the strongest option available for simulation in the market today, but that it continues to be in the future.
That’s why experts and executives from some of the largest simulation organizations out there including Siemens PLM joined forces at the Analysis, Simulation, and Systems Engineering Summit (ASSESS) to discuss the top issues in CAE and what the future holds for simulation software.
Just what issues did the summit determine are most impactful to CAE? How will they shape simulation software going forward? And how does NX CAE address them? Let’s take a look.
- Design centric workflow
Imagine how much time could be saved if design engineers could easily comprehend simulation results. They could tweak their design based on the results without having to consult with a simulation analyst, thus freeing the analyst to focus on more complex simulations. Increasing ease of use so that simulation is not just limited to “experts” is a win-win for everyone involved. Expect to see more design centric workflows in simulation software going forward. NX CAE already addresses this issue with its customizable user interface, which allows everyone from designers to high-end analysts to use simulation. Integrated design validation tools also go a long way in making simulation more designer-friendly.
- Ease of use and/or usability
It’s not just design engineers who need a user friendly simulation environment: A simplified work environment promotes productivity in analysts too. Anytime you make someone’s job easier, they will be happier and get more work done! NX CAE’s new multiphysics environment addresses this concern. Complex simulations such as blade clearance analysis are simplified in this new environment, as analysts can now perform both thermal and structural analyses at the same time on one model. It’s easy to build a coupled analysis, because the analyst can do so on one mesh using the same tools he or she is already familiar with.
- Analysis & simulation before CAD
When you introduce simulation earlier in the product development process, it helps drive changes to the design. Engineers are able to validate the design early on to make a product that will perform better and last longer than if geometry drove the design with simulation performed as an afterthought. It also allows for faster design-analysis iterations through an integrated process. NX CAE facilitates simulation-driven design because it is based on the same platform as NX CAD. Software that allows you to implement simulation sooner—as NX CAE does—will be on the forefront of simulation industry minds.
- The impact of web, cloud & mobile devices
What hasn’t been impacted in the last ten, fifteen or twenty years by the Web and mobile devices? Of course they are going to change the way we think about—and ultimately use—simulation software as time goes on. The cloud has huge implications for simulation as well. Think faster runtimes when you use the power of cloud computing to divide a problem up into smaller pieces, for example. This is a hot trend for simulation that will be garnering a lot of attention in the next few years. Currently, NX Nastran is available on the cloud with Rescale.
- Capturing and reuse of knowledge
Knowledge reuse saves engineers time from creating the same models or data sets over and over again. It can dramatically reduce design development time, so you get your products to market faster. And all of these savings in time translate to savings in cost as well! Time is money, after all. That’s why this concern made the list of top issues in the simulation industry today, and also why you can expect industry leaders to continue to develop ways for their software to capture and reuse information. NX already goes a long way in helping clients with respect to this area. NX Reuse Library is a central platform where users can drag and drop reusable objects into an NX session, among other features.
Other top CAE issues the summit plenary identified include:
- Systems approach to combining heterogeneous models
- Speed & model fidelity
- Unattractive technical issues
- Changes to licensing models
For more information, be sure to check out the ENGINEERING.com article this post is based on!
“I want it, and I want it now!”
You may have heard these words come out of your child’s mouth at one point or another, or perhaps your inner child has stamped its foot while snarling this phrase. Whatever the case may be, the fact remains that we live in a culture of now.
The ever increasing speed at which technology improves and changes has conditioned society to want new and improved products sooner. While we may not always express our wants in such a straightforward and demanding way, the expectation is there.
“The development lifecycle at Nissan is being shortened all the time,” Nissan senior engineer Ian Keen says. “Customer demands are ever increasing.”
This phenomenon is not unique to Nissan. Anyone familiar with the automotive industry should recognize it as a growing trend. But what can you do to ensure your company will rise to meet these challenges?
Nissan saw the answer was to improve their PLM solution, says vice president of Nissan vehicle design and development in Europe, David Moss.
“We set a target of 99 grams of CO2, something that had never been achieved in a vehicle of this size before,” says David. “We were able to use the advanced simulation tools [in NX] to optimize our design.”
Of course, you must first have a design in order to be able to optimize it. That’s where NX CAD comes into play. Nissan designers were able to reduce data creation time by 20% because they were able to use existing designs from their studio in Padddington.
Nissan also used Teamcenter to work in real time with team members from around the globe. Another effect of increasing technology is the shrinking world phenomenon: People and corporations are becoming more and more global as ease of collaboration and communication increases.
“Many of our teams are geographically dispersed, be it in different locations across the U.K. or even globally in Japan,” says David. “Everybody could work on the same data, and we could really speed up our development and get the answers we needed much quicker.”
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This week’s wrap up from Siemens PLM Software contains a big announcement about the latest release of NX software—NX 10—a mission to Mars that started with NX CAD, and a guest post from TEN TECH LLC’s Director of Engineering, William Villers.
What happened to Mars’ atmosphere? NASA MAVEN probe designed with NX CAD seeks answers – NASA sent MAVEN probe to find out why Mars’ atmosphere has all but disappeared over the last few billion years. Lockheed Martin Space Systems Division developed the craft using NX CAD from Siemens PLM Software.
NX Quick Tips: Foreshortening Symbols added in NX 10 – We announced NX 10 earlier this month, and with the announcement came an exciting new NX Quick Tips video showcasing a newly added capability to the NX CAD software.
Preview Latest Software Release at 2014 RUG Meetings – The Siemens PLM Software team will demonstrate a preview of the next release of software at the Regional Users Group (RUG) Meetings that you won’t want to miss. Events are still being held across the country. Look for a location near you!
Better Battery Thermal Management with Co-Simulation – Temperature is a critical parameter for Lithium-Ion batteries, and co-simulation with NX Thermal and LMS Amesim leads to further insights into the effects of this parameter.
Most of US military active fleet carries electronics certified by TEN TECH LLC using NX CAE – Guest blogger William Villers of TEN TECH LLC discusses how NX CAE has met the challenges faced in engineering electronics for US military fleets.
Better Together: Integrated 1D/3D Simulation with NX CAE & LMS Imagine.Lab Amesim – LMS Imagine.Lab Amesim 1D simulation coupled with NX CAE 3D simulation drives systems driven product development, giving engineers the power to design better products faster.
Today we’re proud to announce the latest release of Siemens’ NX™ software, NX 10.
We’ve made some exciting changes with this latest version of Siemens PLM Software’s NX software. From feature enhancements to brand new capabilities in CAD, CAM and CAE, NX 10 has something for designers, manufacturers and analysts alike.
My favorite option is the new touch-enabled interface that puts product information literally at your fingertips. You can use NX 10 on tablets with Microsoft’s Windows. Using your hands to manipulate shapes is a more natural, intuitive way of designing.
Combine this with improvements made to NX Realize Shape, and you’ll be creating highly stylized shapes with complex surfaces in no time. NX Realize Shape uses an approach to creating 3D geometry that was pioneered by the entertainment industry for films.
Another great enhancement we’ve made should spark the attention of simulation analysts: NX CAE features a new multiphysics environment. You can easily build coupled solutions on the same mesh using the same options you’re familiar with thanks to its consistent look and feel. And this environment streamlines complex simulations, because it connects multiple solvers.
You can experience the new functionality of NX 10 starting in December.
Read about all of the new changes and features we’ve added based on feedback from users like you in the NX 10 documentation.
Today, thanks to the Internet, even the phrase “in the blink of an eye” has nearly become obsolete, as we are able to type something into a search engine and get results in three centiseconds. That’s three times faster than you can blink!
This speed has driven the demand for immediacy: We live in a culture of now. “People must be engaged in a culture of continuous improvement,” states Ed Roubal, design engineering and tooling director for Graham Packaging Company. So where does this leave product development?
As society grows accustomed to the rapid speed of new and improved technology, there is a need for the product development process to catch up. The solution is CAD and CAE integration.
Both CIMData and Scientific Computing World have noted how pivotal to product development CAD/CAE integration is. But due to a longstanding history both within organizations and the PLM industry as a whole, a culture of separateness has endured.
Three factors contribute to the cultural issue of CAD and CAE integration: People, Process and Technology. We will delve into these further in future posts to come, but for now let’s look at where the problem originates and why integration matters.
Divergence between CAD and CAE stems from the way the technologies evolved. Design and analysis were completely separate processes performed by different departments. Only recently has the market has begun to shift toward incorporating analysis into the early design stages. Many companies, however, have yet to make the transition due to the cultural issues listed above, which we will explore later.
Simulation driven design solves problems early on in the product lifecycle. Paul Schrier refers to this process as CAE-centric design, but the meaning is the same: Engineers validate and change designs based on simulations conducted in early concepting. This speeds up the design process and improves the overall design because it guarantees your design will work as you intend.
Siemens PLM Software offers a comprehensive system that is fully integrated between NX CAD and NX CAE. This CAD/CAE integration enables engineers to collaborate and share information. This supports innovation, because there are more ideas readily available to implement and try. Someone may just come up with a solution you didn’t see.
Furthermore, NX Synchronous Technology makes implementing new tweaks to designs fast and simple, which ultimately improves workflow. It allows you to edit native or imported CAD geometry in the model without understanding how the geometry was created originally (the model history).
NX also maintains associativity between the CAD model and the CAE model. When a user edits CAD geometry, there’s no need for you to manually recreate the analysis model because the mesh and boundary conditions are associated to the base design and the definitions can be updated automatically to reflect the new, updated design. This all contributes to a faster, more efficient environment so you can focus on what’s important: designing a better product.