Generative Transformers for Design Concept Generation

Generating novel and useful concepts is essential during the early design stage to explore a large variety of design opportunities, which usually requires advanced design thinking ability and a wide range of knowledge from designers. Growing works on computer-aided tools have explored the retrieval of knowledge and heuristics from design data. However, they only provide stimuli to inspire designers from limited aspects. This study explores the recent advance of the natural language generation (NLG) technique in the artificial intelligence (AI) field to automate the early-stage design concept generation. Specifically, a novel approach utilizing the generative pre-trained transformer (GPT) is proposed to leverage the knowledge and reasoning from textual data and transform them into new concepts in understandable language. Three concept generation tasks are defined to leverage different knowledge and reasoning: domain knowledge synthesis, problem-driven synthesis, and analogy-driven synthesis. The experiments with both human and data-driven evaluation show good performance in generating novel and useful concepts.Comment: Accepted by J. Comput. Inf. Sci. En

Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance.Comment: Accepted by J. Mech. Des. arXiv admin note: substantial text overlap with arXiv:2204.0971

Pore Structure of Surfactant Modified Montmorillonites

A series of organoclays with different surfactant arrangements were prepared by ion exchange. The resulting organoclays were investigated using a combination of characterization techniques, including XRD, FTIR, TG and N2 adsorption-desorption. In the present study, the pores within the organoclays were discussed on the basis of the microstructural parameters, including BET-N2 surface area, pore volume, pore size, surfactant loading and distribution. The results show that both BET-N2 surface area and pore volume decrease from low to high packing density of the surfactant as the average pore size increases. Two basic organoclay models were proposed for hexadecyltrimethylammonium bromide (HDTMAB) modified montmorillonites: 1) the surfactant mainly occupied the clay interlayer and 2) both the clay interlayer space and external surface were modified by surfactant. This study demonstrates that the pore structure of the resulting organoclays has a significant influence on the sorption efficiency and mechanism of p-nitrophenol onto the organoclays

Reconstructing Sparse Illicit Supply Networks: A Case Study of Multiplex Drug Trafficking Networks

The network structure provides critical information for law enforcement agencies to develop effective strategies to interdict illicit supply networks. However, the complete structure of covert networks is often unavailable, thus it is crucially important to develop approaches to infer a more complete structure of covert networks. In this paper, we work on real-world multiplex drug trafficking networks extracted from an investigation report. A statistical approach built on the EM algorithm (DegEM) as well as other methods based on structural similarity are applied to reconstruct the multiplex drug trafficking network given different fractions of observed nodes and links. It is found that DegEM approach achieves the best predictive performance in terms of several accuracy metrics. Meanwhile, structural similarity-based methods perform poorly in reconstructing the drug trafficking networks due to the sparsity of links between nodes in the network. The inferred multiplex networks can be leveraged to (i) inform the decision-making on monitoring covert networks as well as allocating limited resources for collecting additional information to improve the reconstruction accuracy and (ii) develop more effective interdiction strategies