Designing dual ontological products for human factors: a machine learning and harmonistic knowledge-based computational support tool

Abstract

The physical construct of a dual ontological product is essential for those products which physically interact directly with humans, whereas the product's emotional construct interconnects with human cognition. Multitude of human factor aspects must be considered when designing dual ontological products. To increase the product's impact and reach, designers should also understand the requirements of potential users. Designers find it difficult to achieve the right compromise between these constructions. This research therefore contributes a novel harmonistic knowledge-based computational support tool which makes designers aware of design stage conflicts and consequences of commitments made on human factors in the use phase of the artefact. This paper describes in detail the machine learning and harmonistic knowledge-based system which exploits information collected directly from potential users to proactively assist, guide, and motivate product designers. The paper takes the motorcycle artefact as a case of dual ontological product. The prototype support tool has been evaluated with 28 motorcycle design engineers. The results obtained from this evaluation have shown that the approach and design computational-based tool meet their goals, are beneficial, and are required in design engineering practice.