The growing adoption of Industrial Internet of Things (IIoT) technologies enables automated, real-time collection of manufacturing process data, unlocking new opportunities for data-driven product development. Current data-driven methods are generally applied within specific domains, such as design or manufacturing, with limited exploration of integrating design features and manufacturing process data. Since design decisions significantly affect manufacturing outcomes, such as error rates, energy consumption, and processing times, the lack of such integration restricts the potential for data-driven product design improvements. This paper presents a data-driven approach to mapping and analyzing the relationship between design features and manufacturing process data. A comprehensive system architecture is developed to ensure continuous data collection and integration. The linkage between design features and manufacturing process data serves as the basis for developing a machine learning model that enables automated design improvement suggestions. By integrating manufacturing process data with sustainability metrics, this approach opens new possibilities for sustainable product development.

This study explores how Low-Rank Adaptation (LoRA) fine-tuning, guided by human aesthetic evaluations, can enhance the outputs of generative AI models in tangible product design, using lamp design as a case study. By integrating human feedback into the AI model, we aim to improve both the desirability and aesthetic appeal of the generated designs. Comprehensive experiments were conducted, starting with prompt optimization techniques and focusing on LoRA fine-tuning of the Stable Diffusion model. Additionally, methods to convert AI-generated designs into tangible products through 3D realization using 3D printing technologies were investigated. The results indicate that LoRA fine-tuning effectively aligns AI-generated designs with human aesthetic preferences, leading to significant improvements in desirability and aesthetic appeal scores. These findings highlight the potential of human-AI collaboration in tangible product design and provide valuable insights into integrating human feedback into AI design processes.

This paper presents a novel conversational AI-enabled active ideation interface as a creative idea-generation tool to assist novice designers in mitigating the initial latency and ideation bottlenecks that are commonly observed. It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI) to produce multiple statements of potential ideas for different design problems. Integrating such AI models with ideation creates what we refer to as an Active Ideation scenario, which helps foster continuous dialogue-based interaction, context-sensitive conversation, and prolific idea generation. A pilot study was conducted with thirty novice designers to generate ideas for given problems using traditional methods and the new CAI-based interface. The key parameters of fluency, novelty, and variety were used to compare the outcomes qualitatively by a panel of experts. The findings demonstrated the effectiveness of the proposed tool for generating prolific, diverse and novel ideas. The interface was enhanced by incorporating a prompt-engineered structured dialogue style for each ideation stage to make it uniform and more convenient for the designers. The resulting responses of such a structured CAI interface were found to be more succinct and aligned towards the subsequent design stage, namely conceptualization. The paper thus established the rich potential of using Generative AI (Gen-AI) for the early ill-structured phase of the creative product design process.

The synthesis of product design concepts stands at the crux of early-phase development processes for technical products, traditionally posing an intricate interdisciplinary challenge. The application of deep learning methods, particularly Deep Generative Models (DGMs), holds the promise of automating and streamlining manual iterations and therefore introducing heightened levels of innovation and efficiency. However, DGMs have yet to be widely adopted into the synthesis of product design concepts. This paper aims to explore the reasons behind this limited application and derive the requirements for successful integration of these technologies. We systematically analyze DGM-families (VAE, GAN, Diffusion, Transformer, Radiance Field), assessing their strengths, weaknesses, and general applicability for product design conception. Our objective is to provide insights that simplify the decision-making process for engineers, helping them determine which method might be most effective for their specific challenges. Recognizing the rapid evolution of this field, we hope that our analysis contributes to a fundamental understanding and guides practitioners towards the most promising approaches. This work seeks not only to illuminate current challenges but also to propose potential solutions, thereby offering a clear roadmap for leveraging DGMs in the realm of product design conception.

This paper explores the enhancement of creativity in Large Language Models (LLMs) like vGPT-4 through associative thinking, a cognitive process where creative ideas emerge from linking seemingly unrelated concepts. Associative thinking strategies have been found to effectively help humans boost creativity. However, whether the same strategies can help LLMs become more creative remains under-explored. In this work, we investigate whether prompting LLMs to connect disparate concepts can augment their creative outputs. Focusing on three domains -- Product Design, Storytelling, and Marketing -- we introduce creativity tasks designed to assess vGPT-4's ability to generate original and useful content. By challenging the models to form novel associations, we evaluate the potential of associative thinking to enhance the creative capabilities of LLMs. Our findings show that leveraging associative thinking techniques can significantly improve the originality of vGPT-4's responses.

Online customer data provides valuable information for product design and marketing research, as it can reveal the preferences of customers. However, analyzing these data using artificial intelligence (AI) for data-driven design is a challenging task due to potential concealed patterns. Moreover, in these research areas, most studies are only limited to finding customers' needs. In this study, we propose a game theory machine learning (ML) method that extracts comprehensive design implications for product development. The method first uses a genetic algorithm to select, rank, and combine product features that can maximize customer satisfaction based on online ratings. Then, we use SHAP (SHapley Additive exPlanations), a game theory method that assigns a value to each feature based on its contribution to the prediction, to provide a guideline for assessing the importance of each feature for the total satisfaction. We apply our method to a real-world dataset of laptops from Kaggle, and derive design implications based on the results. Our approach tackles a major challenge in the field of multi-criteria decision making and can help product designers and marketers, to understand customer preferences better with less data and effort. The proposed method outperforms benchmark methods in terms of relevant performance metrics.

Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations.

For a while now, Washington has been wrestling with two big forces shaping technology: social media and artificial intelligence. Who should do it--and how? Currently, Congress is considering a bill that would regulate how social media companies treat minors: the Kids Online Safety Act. Although it has bipartisan support, KOSA is not without controversy. Several critics have called it "government censorship." One group, the Electronic Frontier Foundation, says it is "one of the most dangerous bills in years."

In the world of digital product design, innovation is a key factor in the success of any business. Companies need to keep up with the latest trends and technologies to remain competitive and stay ahead of the curve. Digital product design is an essential part of the process, as it helps to create products that are both user-friendly and effective. In this blog post, we'll take a look at the role that digital product design plays in innovating for the future. Digital product design is a great way to ensure that products are innovative and efficient.

As the world continues to grapple with the impact of both artificial intelligence & the global pandemic, it's essential to consider the future of work. What will the workplace look like, & how will people adapt? Calling all designers, UX researchers, PMs, & architects in NYC! Join us for an exciting evening of discussion & exploration, meet like-minded individuals, & learn from Ate Atema, owner of Atema Architecture, & Suzanne Li, Director of Product Design at Newstand, during an intimate fireside chat! More about speakers: Ate is a visionary architect passionate about creating sustainable spaces. He has designed offices for TED, Endeavor Global, & The Nature Conservancy NY.