This week Siemens announced that it was hitching a new car to its acquisition train: CD-adapco. With a purchase price $970 million, CD-adapco is a global engineering simulation company with software that covers a wide range of engineering disciplines including fluid dynamics, solid mechanics, heat transfer, particle dynamics, reactant flow, electrochemistry, and acoustics. It is probably best known for its combustion engine simulation capabilities.
Established in 1980 and still controlled by its founders, the company has about 900 employees and approximately $200 million in annual revenue and an annual growth rate of 15 percent for the past five years, according to its website. Its main competitor in engine simulation software is Ansys.
A day before its official release, I spoke with a couple of Autodesk Fusion 360 staffers, Daniel Graham, Fusion 360 Senior Product Manager and Bill Danon about what to expect in the newest update.
The biggest news was the inclusion of simulation capabilities in Fusion 360 – at no additional cost – at least not for now or the foreseeable future. That in itself is pretty significant. Of course, there were some other improvements and enhancements, but let’s start with simulation
Simulation in Fusion 360 lets you perform linear stress analysis that assumes linear elastic behavior and infinitesimally small displacements and strains, as well as modal analysis for study the dynamic properties of structures undergoing vibration. With Fusion 360 simulation you can define materials, add constraints, and add loads to solve for weaknesses in assemblies, within the design environment.
When in the Fusion 360 design environment, a workspace labeled “SIM” under the workspace switcher is where you choose from two types of simulation studies: Static Stress and Modal Frequencies.
Dassault Systemes announced this week that it has acquired simulation technology provider SIMPACK in an all cash deal. The transaction was completed on July 10, 2014. Not surprisingly, financial details of the deal were not revealed.
With the acquisition of Munich-based SIMPACK, Dassault continues to expand its multiphysics simulation technology portfolio to include multi-body mechatronic systems.
SIMPACK has more than 130 customers in the energy, transportation (primarily automotive and rail), and biomedical industries, including Alstom, Bombardier, BMW, Daimler, Honda, Jaguar Land Rover, MAN, and Vestas.
Earlier this week, MSC Software Corp. announced that a jury in the United States District Court for the Eastern District of Michigan found that Altair Engineering willfully and maliciously took MSC Software trade secrets (from Adams simulation software) to use in its MotionSolve product. In other words, the ruling spells out that Altair Engineering knowingly took MSC Software trade secrets with malicious intent.
Keep in mind, though, that this award was no slam-dunk, as the suit was first filed in July 2007 as MSC Software Corp. versus Altair Engineering Inc. The six-week trial ended with two days of jury deliberation.
The jury awarded MSC Software $26.1 million for misappropriation of trade secrets and breach of confidentiality agreements by Altair and two former MSC employees who are currently executives at Altair.
Jurors found that Altair had misappropriated some source code as well as concepts or processes that are used to write the code from MSC, and that the employees had also violated one or more non-solicitation, confidentiality, or severance agreements with MSC.
According to the lawsuit, after Altair hired some former MSC Software employees, Altair began developing a software product called MotionSolve that competed directly with MSC’s Adams/Solver.
MSC had previously alleged that at least eight employees had left MSC between 2005 and 2007 and took jobs at Altair. Five of those employee claims were dismissed prior to trial.
Designing lighter products, whether they’re as large as jet liners or as small as mobile phones, has always been smart business. Less material means less cost and lower energy consumption, both in production and operation. Lower production costs mean higher profit margins for manufacturers. Lower operational costs lead to broader customer acceptance and higher market share.
These days, the “smart business” in lighter products has been upgraded to “essential ingredient.” Lower weight and material efficiency are mandatory for companies that expect to succeed in markets coping with volatile energy prices and increasing environmental regulations. Higher energy prices cause sharp swings in production costs. Manufacturing a product and its component materials means more predictable costs and higher profit margins.
End products are also subject to more scrutiny during their operational lives. Vehicles have to squeeze more miles out of every gallon to satisfy mandates such as U.S. corporate average fuel economy (CAFE) standards
For decades, making a product lighter meant optimizing designs to cut out mass that wasn’t needed to achieve engineering goals. Now, extensive use of fiber-reinforced composites has introduced a new weight-saving measure into product design. Especially in vehicle design but also in appliances and industrial machinery, composites offer comparable strength to metal at a fraction of the weight.
However, introducing composites into product design requires extensive testing. Composites’ plasticity means they do not perform as predictably as metals under real-world conditions. Many manufacturers qualify composites through extensive physical testing on prototypes. This is expensive, time consuming, and can be replaced by simulation – provided that simulation evolves to accommodate composites. Otherwise, they will not yield accurate material allowables, and inaccurate allowables can lead to poor product performance or outright failure.
Simulation technology has traditionally focused on metals. Composites, however, have different properties from metals. For example, a metal-stamped part will behave the same way regardless of how it is manufactured. By contrast, the manufacturing process can change a fiber-reinforced plastic part’s behavior significantly because the process can affect the orientation of the fibers in the material’s epoxy-resin matrix.
Those additional variables complicate engineers’ tasks. They can optimize a design for maximum lightness but end up with a different set of problems because the composite won’t perform the way they expected. Engineers must be able to simulate the strength of composites in different configurations and through various manufacturing processes down to the microstructure level. However, simulation technology hasn’t accommodated them so far.
Most simulation solutions depict composites as “black aluminum.” They represent a composite part’s geometry, but not the full range of its properties. Composite suppliers provide their customers with property data, but that data seldom takes into account the manufacturing process’ influence on the material. Entered into a simulation, these data points will not produce accurate results.
Without accurate material modeling and simulation, designers have to approximate how the composite will perform under real-world conditions. That often leads to over-designing to guard against failure. Over-designing undermines the purpose of designing with plastic or composite in the first place – using less material and reducing weight. It also adds unnecessary cost.
Many simulation technology vendors have incorporated some level of non-uniform material behavior into their solutions. However, these solutions only simulate composite behavior on the surface. A truly realistic model requires an intelligent handle on:
individual properties of the fiber and the matrix;
the composition of the overall materials; and
manufacturing processes’ influence.
Conventional simulation tools do an excellent job of modeling a party’s geometry, loading, deformation physics, etc. Incorporating detailed material behavior for composites drives further precision into the simulation lifecycle.
Giving engineers that precision opens a new range of possibilities for making products lighter without sacrificing performance. For example, an automotive OEM wants to re-design a metal engine mount in composite to save weight. Design engineers develop the basic geometry for the new mount in a 3D CAD environment. The mount weighs 1.2 kilograms. Simulation reveals that the engine mount performs its function under normal loads and in normal operating conditions.
Through virtual simulations to analyze the composite’s behavior in that shape and function, the design team does a series of iterations, analyzes the mount’s performance, and reduces its mass by 40 percent without compromising performance. The lower mass shaves 15 percent from the mount’s cost.
This is what design teams can achieve when they have the tools to model and simulate composites with the same precision they have for simulating metals. It’s the approach that manufacturers need to incorporate in bringing composites into their designs while keeping prototyping costs. The result will be lower material use and energy consumption in production and operation, and more accurate material and part performance. These essential qualities will enable manufacturers to meet the new economic realities of rising energy costs and the societal obligations of sustainability through lighter, better products.
This article was contributed by Dr. Roger Assaker, PhD, founder and CEO of e-Xstream Engineering, and also chief material strategist at MSC Software. Please see http://www.mscsoftware.com/product/digimat for more information.)
National Instruments (NI) is an interesting company that develops NI LabVIEW software as its flagship product. The company is fortunate to sell its products to a diverse customer base of more than 30,000 different companies worldwide, with no one customer representing more than 3 percent of revenue and no one industry representing more than 15 percent of revenue. Customer base diversity is an especially good thing in the technical software market.
I have followed NI for a number of years and really got interested in the company a few years ago with LabVIEW 8.5 being used alongside SolidWorks. LabVIEW has followed a natural progression in the evolution of the NI product line for designing and prototyping complex systems, including robots, that are becoming increasingly pervasive in the world around us, and not just manufacturing environments anymore.
National Instruments supports the increasing need for simultaneous simulation of mechanical and electrical systems, also known as mechatronics. As I have been saying for several years, there was a time when mechanical systems and products were strictly mechanical, however, the majority of today’s products continue to become more capable, and more complex, involving the integration of mechanical, electrical, and software subsystems.
A more comprehensive way to view mechatronics is the systematic integration of mechanical, electrical, electronics, and embedded firmware (software) components. When all of the various components are combined the result is an electromechanical system. Maybe a better term is functional ecosystem. In this context, mechatronics is characterized by software and electronics controlling electromechanical systems. This description is widely seen in automotive engines and other automotive systems, as well as production machinery and medical equipment.
A continuing trend is that as mechatronics systems get more complex and as functionality demands increase, in many instances software and firmware are replacing or at least supplementing hardware. A benefit of this transition from hardware to the burgeoning emphasis on software is called “postponement,” that is, the ability to include or change major functionality features during the final stages of production via embedded software. (more…)
There are several types of CAE-related manufacturing applications for optimizing the use of materials, tools, shape and time, and machine layout by simulating and analyzing specific manufacturing processes. However, probably the most common method for getting CAE into a manufacturing environment, finite element analysis (FEA) for parts and tooling.
FEA is a numerical technique for calculating the strength and behavior of structures. It can be used to calculate deflection, stress, vibration, buckling, and other behaviors. Typical applications for FEA would include minimizing weight and/or maximizing the strength of a part or assembly.
In FEA, structures are divided into small, simple units, called elements. While the behavior of individual elements can be described with a relatively simple set of equations, a large set of simultaneous equations are required to describe the behavior of a complex structure. When the equations are solved, the computer and FEA tool displays the physical behavior of the structure based on the individual elements.
FEA tools can be used for innovating or optimizing mechanical designs. Optimization is a process for improving a design that results in the best physical properties for minimum cost. However, optimization using FEA tools can prove difficult, because each design variation takes time to evaluate, making iterative optimization time consuming. On the other hand, FEA tools can really shine when seeking new and unique ways of designing things – the most crucial aspect of innovation.
Before committing to any CAE tool, however, be sure it is compatible with your existing CAD and CAM tools, the types of parts and assemblies you design, and your general workflow.
Keep in mind that there is no one tool that serves everyone’s needs. Some will be interested fluid flow, others in structural mechanical properties, and still others in thermal issues. Get input from as many groups within your organization as are likely to benefit from CAE tools. When evaluating CAE tools, make sure you evaluate them with your models; not just models supplied by a vendor. That way, you’ll be able to objectively evaluate different CAE tools that best suit your needs in your environment, and not be overly swayed by what a vendor wants you to see. Obviously, it’s in your best interest for objectivity to use the same parts or assemblies with different CAE tool vendors.
Finally, a word of caution. Don’t expect CAE tools to solve all your problems with all of your parts. Like CAD and CAM tools, they should be used in conjunction with experience and common sense to arrive at optimized and innovative designs. Calculating return on investment when using CAE tools can be as complicated as performing analyses on complex assemblies. However, you can probably count on estimating ROI from time saved during the design process, lower material costs, reduced numbers of physical prototypes and ECOs, and possibly greatly reducing the number of product liability lawsuits. CAE tools cannot perform miracles by themselves because they still require a significant human element, but employed wisely, will likely improve your workflow and provide tangible benefits.
By now you’ve almost certainly got MCAD and CAM tools as a vital component of your business. With them you’ve hopefully seen how they have positively impacted the way you work, as well as the way you interact with your customers and vendors. Looking for a way to further increase your productivity, while continuing to optimize your processes?
If you haven’t already, it’s time you considered integrating tools into your workflow for simulation and analysis of virtually any aspect of the product development lifecycle. Although known in some circles as computer-aided engineering (CAE) tools, that acronym has largely been replaced by simulation and analysis, although they all mean roughly the same thing.
It wasn’t all that long ago that CAE was relegated to the latter stages of the design and manufacturing (product development) process — too many times as an afterthought. This is changing, though, on two fronts. First, realizing the potential payback in terms of reduced production time and getting it right the first time, many design and manufacturing organizations have moved CAE tools further forward in the development process. Some are even using them in the earliest stages of design, the conceptual phase. Second, software vendors are getting better at integrating CAE with their CAD and CAM tools.
A major roadblock to CAE’s wider acceptance has been the perception that only high-priced analysis specialists (math PhDs?) could understand and work with CAE tools. While specialists are required for some of the high-end tools for performing complex analyses, there are many CAE tools now on the market that require just some basic training and practice to become proficient in a relatively time.
Admittedly, all CAE tools require a technical mindset, but you don’t necessarily have to have a doctorate in math anymore to run many types of analysis and simulation. It really just requires familiarity with the interface of a CAE tool for creating and loading digital models, and then reviewing and interpreting the results. A really nice thing is that many CAE tools now work from within the familiar UI of your CAD or CAM tool. Finally, computer prices that continue to drop have helped popularize CAE tools, because some of them require a lot computing horsepower when working with large assemblies or very precise engineering constraints.
If this all sounds easy, it is to a point, but there are some caveats. That’s what we’ll discuss next time, as well as the most commonly used CAE tool — FEA.
Like all aspects of the product development process, to justify its existence, simulation and test productivity are becoming an evermore pressing issue. Vendors say that in many cases, customers are demanding significant tangible proof of ROI in months, not years.
A major obstacle to wider acceptance of virtual prototyping and manufacturing simulation is a persisting lack of interoperability between CAD, CAM, and digital prototyping in the bigger PLM scenario. In this context, working toward data interoperability is not regarded as a value-added activity. Overall, however, one of the primary goals of digital test and simulation is to make the overall engineering activity sequence more of a value center and less of a cost center. Another goal is the ability to simulate the entire product lifecycle – from concept through production through sustainment to retirement.
Integrating the analytical, virtual, and physical is disruptive and is an obstacle to acceptance because the integration forces people to work differently than they had done previously. This integration only works through evolutionary implementation, and not necessarily everything all at once.
Many of the digital prototyping tools are still too difficult to use, and vendors need to pay more attention to ease of learning/use. Ease of use is important because vendors, even Tier 1 automotive suppliers, with their low margins cannot afford to hire and employ Ph.D.s to run their digital prototyping software.
On the other hand and in their defense, though, these same vendors are not interested in simplifying (“dumbing-down”) their software so much that they can solve only relatively simple problems. This is a big issue, and one that is even bigger than CAD, where ease of learning/use have made great strides for most vendors the past couple of years. Conversely, many vendors feel that the legacy workforce is not well-suited or qualified for the digital prototyping tools available today.
One way to address the ease of use issue is to provide a scaleable user interface on test/analysis applications to suit different user needs and skill levels at different times.This is tough to address because it requires flexibility and adaptability.
Finally, there is the trust factor that can be an obstacle. In the simulation/test arena, there is an adage that roughly goes, “Everyone trusts test results except test engineers, and everyone trusts analysis results except analysts.” Just about everyone agrees, however, that even with the best digital methods, physical testing will never go away.
The decision of whether to use physical versus digital prototyping is a delicate balance of tradeoffs. In fact, many companies employ virtual testing and simulation as a decision-making tool for conducting physical testing.
So how will digital prototyping ultimately succeed? It’s not hardware or software that makes or breaks digital prototyping, it’s people. While great people can overcome marginal or bad hardware and software, marginal people can cause the best hardware and software to fail. In this context, digital prototyping is no different than any other technical endeavor with regard to the absolute importance of the “people factor” for success.
Market speak aside and regardless of whether it’s called, digital or virtual prototyping for manufacturing processes basically comes down to simulating something in the physical world, whether it’s simulating the machining of a part, placement of machines on a plant floor, or optimizing workflow.
To set the record straight, digital prototyping of anything, including manufacturing processes, is not necessarily CAD or CAM, per se. In fact, it primarily involves digital simulation and test to verify and validate designs and processes, and is an intensely math-based method of viewing them. Some vendors define digital simulation and test as simply good, old-fashioned computer-aided engineering (CAE), although most don’t anymore.
Prototypes of any type, whether physical or digital, provide a basis for making predictions about behavior for making better design, manufacturing, and business decisions. Ideally, intelligent digital prototyping is not only computer based, but a synergy of simulation (virtual) and testing (physical) information based on experience.
Much like CAD/CAM, the main areas that digital prototyping for manufacturing processes aim to influence in a positive manner include:
Accelerating time to market
Increasing safety of the designed product
Improving product quality, reliability, and performance.
Figures bandied about by various industry pundits and analyst organizations predict that integrated digital prototyping is resulting in cumulative savings for product design and manufacturing processes of billions of dollars, and that’s only the beginning.
One of the greatest benefits of employing math-based methods in digital prototyping is that you can actually see cause and effect and track things that can’t be physically measured. Math captures reality. Digital prototyping is changing the traditional product development cycle from designbuildtestfix to designanalyzetestbuild. This newer paradigm reduces cycle times and is much less physical facility intensive. However, for its value to be fully realized, analysis through digital prototyping should be regarded as important as design of products and processes.
That all sounds good, right? Well, like just about anything that aims to change the status quo, there are obstacles to acceptance of virtual prototyping and manufacturing simulation. Overcoming these barriers will be the topic of the next MCADCafe Blog.