Enabling Composite Optimization through Soft Computing of Manufacturing Restrictions and Costs via a Narrow Artificial Intelligence

Abstract

In industry, manufacturing has a huge imprint on structural design; which particularly holds for composites. This is caused by complex interaction of geometry, process parameters and material quantities e.g., fiber orientation. This interaction yields a wide variety of feasible designs, which severely differ in costs and structural performance, measured in mass, stiffness and strength. In order to cope most effectively with this complexity, this paper discusses a weak artificial intelligence, emulating human expertise on composite manufacturing. This approach is extended such that the used knowledge-based system is capable of providing a reason for having determined a certain level of manufacturing effort. Moreover, this extension also provides advice pointing into the direction of optimal improvement. These novelties may be used during designing, optimization and post-processing. These three cases are herein discussed by applying it onto an automotive structure.

Adequate Method Selection for Quantifying Verbal Knowledge in Context of Composite Manufacturing

“…This third branch is of light in this paper as well. For more literature in this context, please consult [10].…”

Expanding the Breadth of Ability in Artificial Intelligence Systems with Decision Trees

“…Modern artificial intelligence is considered narrow (Schatz, 2018). An algorithm is entirely incapable outside the severely limited scope of what it has been trained to do, and it can only be trained to do one thing.…”