Return to 1985. The manufacturing world was far simpler: Consumer expectations were different. Timelines were longer. Automotive OEMs delivered half the number of models they do today, each with far fewer variants. A buyer could not, for example, be guaranteed that a vehicle was available with a gas or diesel engine, with manual or automatic transmission, as a sedan or SUV or convertible. The two-wheel, four-wheel, and all-wheel drive derivatives available today weren't an option.
Now fast forward to 2005. Car buyers expect superior quality, an enormous variety of choices even within the same basic car model, hybrid fuel cells in addition to gas and diesel options, SUVs that turn into trucks at the push of a button -- and all at inflation-adjusted price points well below those charged 20 years ago.
If we take one car model, allow a convertible and a sedan option, either gasoline or diesel, choice of automatic and manual transmission, and front-wheel or four-wheel drive, we've created 16 design variants. Each derivative places unique physical loads on the vehicle and each must be analyzed for safety, durability, manufacturability, cost, and dozens of other decision criteria. The same reality shift that pertains in the automotive industry holds for aerospace, consumer products, industrial machinery, off-road vehicles, and every other industry and is magnified by Moore's law in the electronics industry.
Unfortunately, many companies are not responding as strongly as they could to these challenges. They still use work processes created to respond to the reality of 20 or perhaps 10 years ago, with smart employees educated in the 1980s (or earlier) and used to working in the environment appropriate to that time. But these processes, in many cases, are insufficient to meet today's challenges. Leading companies in 2005 are exploring ways to use technology to redefine what they do, how they do it, and how to optimize the outcome.
What's a smart manufacturer to do?
CAE to the Rescue
In the last several years, the use of CAE has grown from failure analysis (why did this break?), to part verification (will this piece withstand the stresses of continued use?), to part optimization (how can this part be manufactured less expensively and still meet performance objectives?) -- and from part-level analysis to system-level analysis. As a result, it's now possible to perform ever more complex levels of verification for the part itself, the system in which it resides, and the tools that will be used in the manufacturing process. The problem is coordinating all of this analytical work, providing results in a manner usable by the most functions, and fitting these analyses into the engineering/design/manufacturing/after-sales support workflow.
It sounds simple: create a team of designers, engineers, and manufacturing specialists for the project. Let them collaborate. Hold design reviews. Front-load the design process with analysis to weed out the inappropriate designs as early as possible. But this is far from easy, as many have found. Cultural and technological challenges, legacy codes, application interoperation, data integration, user training, redefined job functions, and other issues continue to stress companies seeking to implement CAE to its fullest capability. daratechDPS2005 Digital Product Simulation & PLM will explore these issues and offer best practices to address some of these concerns. Below, we've taken a closer look at some of these issues as well as emerging trends in order to get a better understanding of the challenges faced by CAE and PLM implementers -- and their suppliers.
A Rose by Any Name?
Virtual product development. Simulation-driven product design process. Knowledge-enabled product development. Optimized product development.
Each of these supplier taglines refers to incorporating analytical tools into the mainstream design process. Each also reflects the migration currently underway that is taking CAE from a silo within the engineering organization to a broader audience: through the growth of PLM, CAE is becoming an enterprise app. Cutting-edge manufacturers are using simulation, together with visualization capabilities, to provide a communication and collaboration platform for design discussion between engineering, design, and supplier teams.
Forward-thinking companies start their analyses at initial concept design -- and keep going through to product service. Applied early, often, and systematically, CAE leads to less design rework and allows designers to explore more innovative alternatives in the early stages, when examining each alternative is least expensive.
Often, "success" is defined as producing the best possible final product as efficiently as possible to maximize total value to the end-consumer -- however the consumer defines that value. Reaching this goal begins in the earliest phases of design, as value and affordability specifics are locked in. But decisions made early in the design process also have ramifications on the product lifecycle as well -- a fact which many design teams do not take into consideration. If a product is designed, for example, to meet the consumer desire for lowest initial cost, maintenance variables may not be taken into account. If a product is designed to minimize total cost of ownership, what tradeoffs are made on initial cost? Fostering close collaboration between designers and analysts allows companies to proceed into manufacturing with the confidence that the design will meet its requirements for strength, vibration, crashworthiness -- or whatever factors apply.
Ultimately, all design is compromise -- finding the best route to meeting conflicting objectives -- and the data offered by intelligent use of analytical tools offers critical information for making the best decision.
One interesting recent development in CAE is the realization that what is conceived of in design is often only partially realized in the manufacturing process -- not because manufacturing is flawed, but because the production process is, by its very nature, inconsistent from object to object. For example, something as seemingly simple as folding sheet metal into shape for computer housings varies from case to case. The quality of the sheet metal may not be consistent, atmospheric conditions in the shop may vary from day to day, or the skill of the operators setting up the production machines is inconsistent. Analysts are increasingly modeling manufacturing variability in components and in materials -- leading to even clearer predictions of a product's lifetime performance.
After-sales support and warranty service is another area in which a great deal of innovative process change is taking place. Manufacturers now use virtual prototyping to simulate what field workers are likely to do during routine maintenance. It used to be that factors such as access and optimal repair work processes were simulated; now the results of those work processes are also simulated. What happens to the product if 60% of the time a critical nut is over-tightened during maintenance? How can the performance of the product be optimized after a period of normal wear and tear? This type of simulation requires more information than has traditionally been available but the data collection practices from warranty repairs, returned product analyses, and other PLM-centric dataflows makes this possible. Such "forward" engineering yields significant benefit -- both to refine a design before production and to optimize performance after sales.
Do You Know What I Know?
The objective of many PLM implementations is to capture the decisions -- and the decision-making process -- which led to the final design. PDM systems form the backbone, enabling groups to share information and collaborate without physical co-location. Similar structures, for the capture of CAE "knowledge," work processes, and techniques are just now emerging. Some of the issues are similar to those of the early PDM systems: each company seeking an implementation has unique workflows, the data needs (and transferability issues) are complex, there are many possible combinations of tools and data, etc. But effective use of such data management tools will soon become a determining factor in the successful use of CAE. Too, these tools will be required to manage issues surrounding an aging workforce, a lack of new engineering talent entering many discrete manufacturing verticals, and the reality of outsourcing work to lower-cost, high-value engineering centers.
Complete functional performance assessment often involves both digital simulation as well as physical simulation using a prototype and a test rig. In an ideal PLM implementation, a bill of materials would be extended to cover the needs of the designer, analyst, test engineer, and, ultimately, manufacturing engineer in a series of "lifecycle BOMs." Each bill of material (BOM) would carry the information needed for its specific function, but tie back to all others, enforcing synchronicity and traceability.
Even companies not involved in immediate outsourcing are dealing with the question of how to effectively manage their resources. The question of who in a design team is best suited to do what types of analysis is still in flux at many companies. Some have moved specific analytical tasks to the designer level, reasoning that more information as early as possible in a process is best; others have determined that their designers lack the specialized knowledge to competently perform sophisticated analyses. Solution providers are doing their best to support both sets of workflows, embedding excellent analytical tools in most CAD solutions while continuing to add sophistication to their high-end offerings. Ultimately, it comes down to how the manufacturer assesses the level of complexity of the analysis and the skill level of the participants.
Formula for Success: An Example from Formula 1 Racing
Even companies using CAE to great effect still have to make tradeoffs between physical testing and digital simulation. A perfect example comes from the world of Formula 1 racing, a global sport whose popularity is rumored to be on par with soccer -- and where teams reportedly spend $300 million per year in their quest for the top prize. Formula 1 cars are amazingly complex and expensive, with bodies created to maximize aerodynamic performance. The cars have wings, deflectors, spoilers, and other external devices to optimize the airflow around the car; tiny changes in these surfaces can have a significant impact on how well the car performs on race day.
One Formula 1 team, Sauber Petronas, estimates that the aerodynamics accounts for roughly three-quarters of the performance of a racecar and places a great deal of emphasis on simulating the performance for its cars. Before each race season, Sauber Petronas uses CAE to design and refine aerodynamic components of the racecar and evaluate dozens of variants -- but then test a fraction of those resulting vehicles at 60% scale in a brand new, state-of-the-art wind tunnel. Once the season begins, the teams race just about every weekend and spend the week in between tuning the car(s) for the next race.
With so little time between race outings, the teams must determine what needs to be tweaked, change the virtual design, run a simulation -- and iterate until the desired result is achieved. While these computations have been getting faster with improvements in computing technology, it is still impractical in many cases to recalculate the airflow around a Formula 1 car model for every small design change. During the race season, Sauber Petronas often modifies the 60% scale model of the racecar to ascertain the immediate effects of the design change -- and then follows this with an analytical CFD run to gain a better understanding of the physical observations from the wind tunnel.
This strategy of combining physical and analytical testing to the benefit of both has been successful for Sauber Petronas, leading the team to a very consistent performance in the 2004 and 2005 racing season.
Formula 1 efforts don't result in consumer products, at least not directly. But the lessons learned -- of simulating what can be simulated, using learnings gained from simulation to optimize physical testing, and then refining understanding through simulation -- are directly applicable to everything manufactured today. Several speakers at daratechDPS2005 are expected to share how they gain the maximum benefit from carefully balancing physical prototype testing and virtual simulation.
When used throughout the product conception and design process, digital prototyping contributes to better product quality that consequently serves to build brand loyalty. Very significantly, it also helps avoid program-killing warranty problems and recalls and reduces their related costs. However, many companies still see CAE as an expensive, nice-to-have-but-not-mainstream luxury, and use it in silos that are apart from the design-engineering mainstream.
Not only does this limit the return on a company's CAE investment, but it also prevents the company from realizing many of the key benefits enjoyed by competitors who have cracked the code on CAE usage and implementation. Worse, it makes it much more difficult to justify that next investment in CAE capability, since what's already in-house is delivering such modest results. Yet there are case studies aplenty that showcase the success of CAE, with more to be highlighted at daratechDPS2005. The common denominator: understand what makes your product unique, look at the total product creation process to discover where and how CAE can best be used, start modestly, and build on success. Sounds simple, doesn't it?
