Regardless of which systems engineering representation you use, our engineering toolbox can help your team improve its efficiency throughout the product lifecycle. The concepts of improving tool awareness, usability, accuracy, and consistency – all through use of our web-browser-based system – can have big efficiency impacts on any stage.
Read on to see how some of our customers are using this system to accelerate the traditional system engineering stages shown here.
In the early stages of a design, exploratory requirements are captured and the overall system is considered, ultimately establishing feasibility. Traditionally, for complex systems, feasibility assessment can be time consuming, and if solutions don’t converge or if business considerations change, teams can waste a lot of resources in this stage.
GE Wind Energy uses our system for their Wind Inquiry-to-Order App, simplifying what would otherwise be a tedious exercise of site assessment, layout, system configuration, and costing.
The server architecture integrates the entire process across multiple databases, spreadsheets, MATLAB, and in-house scripts. It allows field engineers to capture site characteristics, assess feasibility, configure a system, and establish budgetary estimates – all through their web-browser of choice. This enterprise deployment is now used by over 130 people around the world, and the solution was still easy to build, completed in about 10% of the time of a conventional enterprise-wide integration.
On a smaller scale, this same architecture can be used to appify utilities that assess early system design, traditionally only used by a few team members. In the video shown here, disparate Excel workbooks and a Mathcad worksheet all calculate on the server, to safely empower a wider audience.
This way, system designers or application engineers can perform the same feasibility assessment – quickly and accurately, and without burdening the few engineers that only performed such assessments previously.
Once the feasibility and system solution has been established, preliminary design work can start, and subsystems can be designed. Traditionally, the wide range of potential solutions or configurations can be difficult to assess under the time constraints of a project, so optimal solutions are often overlooked.
ZF | TRW uses our system for their Pitman Arm Smart Product Generator, reducing their preliminary design cycle from weeks to minutes.
In the video to the right, the backend integrates complex spreadsheets and scripts for both fatigue and manufacturability requirements, in concert with a robust parametric CATIA model. The browser front-end uses dynamic geometric constraints and these manufacturing limitations to keep the user in check at all times. Such real-time design rules allow application engineers to explore a custom configuration on the fly, with the confidence that it can be built and will meet the strength and fatigue requirements of the application. Automation of 3D models and drawings further reduces the burden on downstream engineers and designers, ultimately performing preliminary design activities faster and much more consistently.
Similarly, in the video to the left, application engineers can perform a high level preliminary design assessment on a wheel rim. Again, material and manufacturing constraints are used on the server along with a robust CAD model, to allow less experienced engineers to configure with confidence. A preliminary impact analysis is also performed as part of the assessment to ensure initial feasibility before any detailed design and simulation work is performed.
After higher level requirements have been met, detailed design work often takes place, accompanied by further analysis and simulation. Many complex analyses and simulations take place during this detailed design phase, and can therefore be very time consuming.
Procter & Gamble (P&G) used our system to create their VPS Job Manager, which semi-automates the wide range of analyses required, enabling the team to maintain the speed at which they bring new products to the retail market.
The server integrates with backend tools such as MATLAB, ANSYS, in-house scripts, and Excel, and leverages templates to automate detailed analysis work without straining the analysts in the organization. This allows the analysts to focus on more “expert-only” tasks, empowers the extended team, and simplifies the team’s license management by balancing and queuing jobs on our compute servers.
If a family of designs is mature enough, like the ball bearing shown to the left, the systems engineering process can be accelerated even further through automation of design rule checking, 3D model generation, and load condition simulation. When such standardized work processes are involved like in this case, these rules and analyses can be built into the app, and as many conditional simulations can be run as needed in order to validate the custom configuration being generated. This greatly accelerates the design process, helps teams enter the implementation phase faster and with more confidence, and can even generate all the design documentation needed for the custom solution (models, drawings, analysis results, reports, etc).
VERIFICATION & VALIDATION
At any stage within the system engineering process, validation and verification steps can be employed to assess the accuracy of the engineering models, analyses, assumptions, etc. Of course, this pertains to finished product validation – perhaps leveraging the Internet of Things (IoT) and analyzing field data. However, this also relates to validating stages within the design space itself – such as validation of simulations against test data.
Caterpillar uses our methodologies within their in-house scripting environment, to statistically validate their simulated loading conditions against historical test data. This method drives more efficient subsystem and component design, arriving at more accurate fatigue calculations when such simulated events are compiled into an entire life profile of a vehicle.
This statistical validation method simplifies an otherwise tedious and error-prone process of validating time-based simulations against a family of reference signals, historically requiring manual alignment of inflection points, normalization, and ultimately subjectivity. As seen in the image above, use of a browser-based system simplifies tool accessibility for the engineering community, removes subjectivity through use of statistical methods, simplifies interpretation of results, and can make reports and detailed analyses immediately available for download and further evaluation.