A knowledge recommendation approach in design for multi-material 4D printing based on semantic similarity vector space model and case-based reasoning

Abstract

4D printing refers to a breakthrough technology combining additive manufacturing (AM) and smart materials (SMs) under the effect of an energy stimulation. From the numerous experiments proposed in the literature, which highlight the broad spectrum of shape- and/or property-changing capacities, an emerging research interest is to tackle 4D printing from a design perspective. The so-called design for 4D printing requires rethinking well-established models, methods, and tools to develop innovative devices with changing capacities. It is particularly suitable to focus on the computational design synthesis where decisions on AM techniques and SMs selection have an important influence on the part geometry and materials distribution. Previous research work has developed an ontology for capturing multiple perspective knowledge related to SMs, stimuli, AM, and product design. However, the construction of a complex and interdisciplinary knowledge base is not sufficient and requires knowledge reuse/recommendation mechanisms for guiding designers and engineers. Therefore, the article aims to propose a knowledge recommendation approach in computational design for 4D printing with a semantic similarity vector space model. A specific emphasis is made on multi-material 4D printing where recommendations are needed to determine active and passive materials’ distributions for achieving the desired shape change. An implementation of the approach in a computer-aided design plugin is described and illustrative cases from the literature are introduced to demonstrate its applicability.