So-called knowledge-based engineering has been on the dream list of engineers for years, and for good reason: Among other things, it captures existing company and engineering knowledge to help engineers automate certain design processes and, thus, concentrate on engineering rather than repetitive tasks. Does it work? Ask Paul Simms, senior project engineer for simulation at Kentucky-based Accuride Corp. (www.accuridecorp.com).
Time Savings: Engineers at Accuride used
knowledge-based engineering and finite element analysis to cut design time
for heavy-duty trucks.
The company designs and manufactures steel and forged-aluminum medium- and heavy-duty truck wheels. In one recent project, engineers cut design time from 30 hours per model to 15 minutes per model, with the help of Design To Order software and processes from Design Automation Associates (www.daasolutions.com). They also cut the one-to-six week prototype design time down to two hours.
Simms says Accuride concentrated initially on automating its finite element analysis process. The company's FEA models often have a million degrees of freedom. Accuride uses modelers from ANSYS for its FEA work. "With the automated process we now have, our engineers only spend five to ten minutes interacting with the model, and then the computer takes over," Simms says. Additionally, he says, they have more flexibility for changing boundaries and load conditions on the models.
That flexibility helped engineers cut the weight of the wheels in their most recent project as well as the design time. "There were four wheels involved, and we were able to cut weight between 4 and 14%," Simms says. The lighter the wheels, the more payload they can carry.
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For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.