A foundation for machine learning in design

Abstract

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

A Survey of Design Rationale Systems: Approaches, Representation, Capture and Retrieval

“…The research work in the area of machine learning has contributed many methods that have been applied to the acquisition of knowledge in design. Machine Learning in Design (MLinD) [95,96] examines the similarities between machine learning and design rationale capture. The main elements in machine learning are the input of knowledge, the output of knowledge, the transformation of knowledge, the learning triggers and the learning goals which are related to the record, the access, the construction, the capture and trigger of design rationale.…”

A Pilot Study on the Prevalence of Artificial Intelligence in Canadian Engineering Design Curricula

“…Engineering design researchers utilize AI-based algorithms methods, especially machine learning, for rapid design data learning and processing [17]- [19] and have achieved successful results in their research contributions. Such contributions include evaluating design concepts [20], decision making for design support systems [21], design for additive manufacturing [22], predicting strain fields in microstructure designs [23], predicting performance of design based on its shape and vice-versa [24], material selection for sustainable product design [25] etc.…”

A function–behavior–structure ontology of processes

“…For example, design optimization processes can be characterized on the basis of differences in their speed, differences in the amount of space they require, or other behaviors. Another example has been provided by Sim and Duffy (1998), who propose a multidimensional classification of machine learning processes in design that can be mapped on structure and function of a process. Specifically, learning processes are grouped according to input knowledge and learning trigger (both i ), knowledge transformers ( t ), output knowledge ( o ) and learning goal ( F ).…”