This guest post on Innovation Intelligence was written by Laurent Bernardin, Executive Vice President at Maplesoft. Maple and MapleSim for Mathematical Analysis and Systems Simulation are available through the Altair Partner Alliance.
Many engineering organizations are facing major challenges involving time-to-market and cost control in today’s ever more competitive and global market. At the same time, system complexity is rising to accommodate growing customer expectations and increased regulatory constraints. As a result, key, and sometimes fundamental, design issues are often discovered late in the product development process.
This results in budget overruns and project delays, especially if issues are not discovered until the hardware prototyping or even system integration phase. Many of these issues arise because designing a complex system involves multiple distinct disciplines, each focused on a single aspect of the system. Even when each subsystem complies with its specification, problems arise after these subsystems are integrated during prototyping or even during final assembly.
In order to solve these issues, a growing number of organizations are turning towards a model-driven innovation process, making a multi-domain, system-level model the core of their design activities. This allows the detection and correction of issues early in the design process to increase product quality. Ultimately, such an approach shortens time to market and contributes to controlling costs by lowering iteration times and reducing the number of hardware prototypes.
For example, in one project at Maplesoft, a system-level model allowed analysis of the effects of dynamics on the stability of a large mechanism, detecting a critical operating condition using simulation that would otherwise have required a costly in-the-field fix. Another customer used a system model to optimize the gear shift quality of automatic vehicle transmissions by examining the effects of clutches on output torque to detect trouble spots.
When using a model-driven innovation approach, it is critical to understand what questions you want to answer in order to build a system-level model that will yield those answers. With such a model, a virtual prototype of the design may be viewed and used to simulate the dynamic behavior of the multi-domain system, with varying parameters and configurations. It is also possible to set up interactive analysis tools, driven by the model, which allow the tuning of parameters to instantly see the impact on system behavior. With some tools, you can even get access to the underlying system equations to solve inverse kinematic and inverse dynamic problems, which are useful for advanced control scenarios.
Finally, a system-level model facilitates verification and control design. A virtual prototype derived from the system-level model is used via a real-time simulation. Fewer surprises will be discovered late in the process.
Whenever system complexities threaten the ability to control costs, produce high quality designs, and/or get them to market quickly, it is worth investigating what value a system-level approach can bring to your organization. We have seen these techniques make a real difference for many customers, who now consider model-driven innovation to be a core part of the product development process, and indeed, a competitive advantage.