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Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG. 

Generative Adversarial Networks (GANs) have shown stupendous power in generating realistic images to an extend that human eyes are not capable of recognizing them as synthesized. State-of-the-art GAN models are capable of generating realistic and high-quality images, which promise unprecedented opportunities for generating design concepts. Yet, the preliminary experiments reported in this paper shed light on a fundamental limitation of GANs for generative design: lack of novelty and diversity in generated samples. This article conducts a generative design study on a large-scale sneaker dataset based on StyleGAN, a state-of-the-art GAN architecture, to advance the understanding of the performance of these generative models in generating novel and diverse samples (i.e., sneaker images). The findings reveal that although StyleGAN can generate samples with quality and realism, the generated and style-mixed samples highly resemble the training dataset (i.e., existing sneakers). This article aims to provide future research directions and insights for the engineering design community to further realize the untapped potentials of GANs for generative design. 

Generative Adversarial Networks (GANs) have shown remarkable success in various generative design tasks, from topology optimization to material design, and shape parametrization. However, most generative design approaches based on GANs lack evaluation mechanisms to ensure the generation of diverse samples. In addition, no GAN-based generative design model incorporates user sentiments in the loss function to generate samples with high desirability from the aggregate perspectives of users. Motivated by these knowledge gaps, this paper builds and validates a novel GAN-based generative design model with an offline design evaluation function to generate samples that are not only realistic, but also diverse and desirable. A multimodal Data-driven Design Evaluation (DDE) model is developed to guide the generative process by automatically predicting user sentiments for the generated samples based on large-scale user reviews of previous designs. This paper incorporates DDE into the StyleGAN structure, a state-of-the-art GAN model, to enable data-driven generative processes that are innovative and user-centered. The results of experiments conducted on a large dataset of footwear products demonstrate the effectiveness of the proposed DDE-GAN in generating high-quality, diverse, and desirable concepts. 

In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber manufacturing. Using a network science and data mining-based Keyword Co-occurrence Network (KCN) methodology, this work analyzes the trends in data science topics in the manufacturing literature over the past two decades to inform the researchers, educators, industry leaders of knowledge trends in intelligent manufacturing. It studies the evolution of research topics and methods in data science, Internet of Things (IoT), cloud computing, and cyber manufacturing. The KCN methodology is applied to systematically analyze the keywords collected from 84,041 papers published in top-tier manufacturing journals between 2000 and 2020. It is not practically feasible to review this large body of literature through tradition manual approaches like systematic review and scoping review to discover insights. The results of network modeling and data analysis reveal important knowledge components and structure of the intelligent and cyber manufacturing literature, implicit the research interests switch and provide the insights for industry development. This paper maps the high frequency keywords in the recent literature to nine pillars of Industry 4.0 to help manufacturing community identify research and education directions for emerging technologies in intelligent manufacturing. 

Manufacturing has adopted technologies such as automation, robotics, industrial Internet of Things (IoT), and big data analytics to improve productivity, efficiency, and capabilities in the production environment. Modern manufacturing workers not only need to be adept at the traditional manufacturing technologies but also ought to be trained in the advanced data-rich computer-automated technologies. This study analyzes the data science and analytics (DSA) skills gap in today’s manufacturing workforce to identify the critical technical skills and domain knowledge required for data science and intelligent manufacturing-related jobs that are highly in-demand in today’s manufacturing industry. The gap analysis conducted in this paper on Emsi job posting and profile data provides insights into the trends in manufacturing jobs that leverage data science, automation, cyber, and sensor technologies. These insights will be helpful for educators and industry to train the next generation manufacturing workforce. The main contribution of this paper includes (1) presenting the overall trend in manufacturing job postings in the U.S., (2) summarizing the critical skills and domain knowledge in demand in the manufacturing sector, (3) summarizing skills and domain knowledge reported by manufacturing job seekers, (4) identifying the gaps between demand and supply of skills and domain knowledge, and (5) recognize opportunities for training and upskilling workforce to address the widening skills and knowledge gap.