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A polyacrylonitrile (PAN)-based carbon fiber (CF) manufacturing cost estimation model driven by mass is presented in this study. One of the biggest limiting factors in the large-scale use of carbon fiber (CF) in manufacturing is its high cost. The costs involved in manufacturing the carbon fiber have been formalized into a cost model in order to facilitate the understanding of these factors. This can play a key role in manufacturing CF in a cost-effective method. This cost model accounts for the fixed and variable costs involved in all the stages of manufacturing, in addition to accounting for price elasticity.

A polyacrilonitrile (PAN) based carbon fiber manufacturing cost estimation model driven by weight is presented in this study. One of the biggest limiting factors in the large scale use of carbon fiber (CF) in manufacturing is its high cost. The costs involved in manufacturing the carbon fiber have been formalized into a cost model in order to facilitate the understanding of these factors. This can play a key role in manufacturing CF in a cost effective method. This cost model accounts for the fixed and variable costs involved in all stages of manufacturing, in addition to accounting for price elasticity.

This paper explores the amount of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional composition rules into vocabulary, grammatical, and topological classes and applying them to function structures available in an external design repository. The pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using graph complexity connectivity method. The accuracy is inversely with amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduce the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified through this research approach.