Is Predictive Engineering Supported By Artificial Intelligence The Future ?
Juergen Heimbach
Innovator 3DfindIt.com & Strategic PARTsmanagement PARTsolutions - The Manufacturers Enterprise 3D Search Engine by CADENAS
Overview
In recent decades, the requirements for engineering and creative design development have changed significantly: Instead of reinventing the wheel over and over again, today it is important to use knowledge already gained from existing designs and to transfer known solutions from one application to another in a meaningful way. This is particularly important with regard to the increasing number of individual designs, in which existing designs are precisely adapted to the individual needs of the customer.
?A mandatory prerequisite for this is complete knowledge of the quantity of parts used in the company. This is not only a matter of knowing the existence of a part, but also the designation with which the part can be found in the parts' database, as well as its appearance and correct use or configuration. However, the enormous volume of available purchased parts or company-specific proprietary parts makes it impossible for engineers to maintain a complete overview. Young junior engineers in particular lack years of professional experience and thus the possibility to combine acquired knowledge in an innovative way.
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The goal of the KOGNIA project is therefore to automatically process the knowledge of experienced designers from past designs and make it available for future designs. The expertise acquired over many years, originally bound to individuals, is thereby bundled in a system, processed and made available to all employees of the company. Machine learning is used to identify patterns and regularities, such as commonly used or combined parts, in existing and newly created designs. Based on this knowledge, engineers can automatically be presented with meaningful suggestions for the next required purchased and proprietary parts while they are still designing, thus making generic as well as company-specific engineering knowledge more easily accessible. This system will benefit not only young engineers, whose learning curve is shortened, but also experienced designers, who will be able to acquire and transfer knowledge from similarly experienced colleagues.