Innovations In Everyday Engineering Materials PDF Download

This dissertation presents an analysis of innovation in engineering materials and energy sources. More than fifty engineering materials and fourteen energy sources were selected for an evaluation of the relationship between the yearly production activity and yearly patent counts, which may be considered as a measure of innovation, for each. Through the employment of correlation theory, best-fit and origin shift analyses, it has been determined here that engineering materials and energy sources display similar life cycle and innovative activity behaviors. Correlation theory revealed a relationship between the yearly production and yearly patent counts indicating the extent that production and innovation affect each other. Best-fit analysis determined that four-stage life cycles exist for both engineering materials (metals and non-metals) and energy sources. Correlation and best-fit indicators of an estimated Stage III are confirmed by the presence of an origin shift of the patent data when compared to the production data which indicates that patents, or innovation, are driving, or being driven by, production. This driving force could represent the constructive or destructive side of the innovative process, with such sides being delineated by a possible universal constant above which there is destructive innovative behavior and below which exists constructive innovation. The driving force may also illustrate the manner in which an engineering material or energy source transitions into an innovatively less active state, enter Stage IV and possibly become a commodity. A possible Stage V, indicating "Final Death", is introduced in which production is on a steep decline with no signs of recovery. Additionally, innovatively active energy sources are often found to utilize or be supported by innovatively active engineering materials. A model is presented that can be used for the evaluation of innovation and production that can be applied to both engineering materials and energy sources that may be used to predict the innovative behavior of these resources in order that they can be more effectively allocated and utilized.