by John Swaren
| September 25, 2014
Like titanium and other exotic metal-materials, “composites” (by definition, combinations of materials) offer significant weight-savings and reduced part counts, but at a price of high production cost. Sound contrarian to our parametric cost estimating view? Not really. Complexity of manufacture is quite higher. Likewise process index and structural tooling values grow. Plus, design lead times drive developmental cycles.
That said, understand that composites represent more than a material type. They can involve a highly labor-intensive approach to preparing, braiding/ winding, molding, bonding and modular assemblage. Yes, some aspects of braiding and molding lend themselves to automation—which then drives tooling investment. Composite development offers design flexibility, weight savings as well as advantages in long-term deterioration. But not all pre-curing processes are the same, to include recent advances in structural co-processing before subsystem cure.
At this juncture, rather than get ON a soap box, I’d ask that you join me in help getting us INTO a soap box. 40+ (!) years ago, my engineering Dad challenged my brother & I to make a soapbox derby car from fiberglass. (My uncle worked at little known Owens-Corning at the time, and material costs were cheap!) To make a long story short, Dad required that we not get “gluey” too early and instead had his two very young sons learn the benefits of (pre-CAD/CAM) design drawing. So draw, sketch, and describe we did. Talk about information entropy! But the more we had to draw and detail a component’s design with the physical and functional features Dad needed to make it exactly right, the longer (more time) it took him with each piece… and the more our “cost” (waiting anxiously to ride) went up.
25 years later, MIT’s Hoult & Muter would have been proud: we realized that amount of information communicated was the driver in our composite manufacturing process. Multi-dimensions are one thing to grasp at a young age. Communicating corresponding tolerances is a bigger challenge. The latter are typically known as “feature parameters” in engineering circles. Suffice then that to estimate composites processing, the more effective predictor of cost is entropy between design and build.
And how would we propose to count all relevant exchanges of information, including these latter parameters? The same way perhaps that parametricians characterize early stage software concepts visualizing inputs, outputs, data stores, elements (toleranced dimensions), operators (processes), etc. Just like using Function Points in software cost estimation! Over the next few months, we’ll examine this new approach following more composite cost research and predictive modeling using an information entropy statistic. Stay Tuned!