Human activity recognition (HAR) is a vital component of human–robot collaboration. Recognizing the operational elements involved in an operator’s task is essential for realizing this vision, and HAR plays a key role in achieving this. However, recognizing human activity in an industrial setting differs from recognizing daily living activities. An operator’s activity must be divided into fine elements to ensure efficient task completion. Despite this, there is relatively little related research in the literature. This study aims to develop machine learning models to classify the sequential movement elements of a task. To illustrate this, three logistic operations in an integrated circuit (IC) design house were studied, with participants wearing 13 inertial measurement units manufactured by XSENS to mimic the tasks. The kinematics data were collected to develop the machine learning models. The time series data preprocessing involved applying two normalization methods and three different window lengths. Eleven features were extracted from the processed data to train the classification models. Model validation was carried out using the subject-independent method, with data from three participants excluded from the training dataset. The results indicate that the developed model can efficiently classify operational elements when the operator performs the activity accurately. However, incorrect classifications occurred when the operator missed an operation or awkwardly performed the task. RGB video clips helped identify these misclassifications, which can be used by supervisors for training purposes or by industrial engineers for work improvement.

Personas are hypothetical representations of real-world people used as storytelling tools to help designers identify the goals, constraints, and scenarios of particular user groups. A well-constructed persona can provide enough detail to trigger recognition and empathy while leaving room for varying interpretations of users. While a traditional persona is a static representation of a potential user group, a chatbot representation of a persona is dynamic, in that it allows designers to “converse with” the representation. Such representations are further augmented by the use of large language models (LLMs), displaying more human-like characteristics such as emotions, priorities, and values. In this paper, we introduce the term “Synthetic User” to describe such representations of personas that are informed by traditional data and augmented by synthetic data. We study the effect of one example of such a Synthetic User – embodied as a chatbot – on the designers’ process, outcome, and their perception of the persona using a between-subjects study comparing it to a traditional persona summary. While designers showed comparable diversity in the ideas that emerged from both conditions, we find in the Synthetic User condition a greater variation in how designers perceive the persona’s attributes. We also find that the Synthetic User allows novel interactions such as seeking feedback and testing assumptions. We make suggestions for balancing consistency and variation in Synthetic User performance and propose guidelines for future development.

The generation of floor plan layouts has been extensively studied in recent years, driven by the need for efficient and functional architectural designs. Despite significant advancements, existing methods often face limitations when dealing with specific input adjacency graphs or room shapes and boundary layouts. When adjacency graphs contain separating triangles, the floor plan must include rectilinear rooms (non-rectangular rooms with concave corners). From a design perspective, minimizing corners or bends in rooms is crucial for functionality and aesthetics. In this article, we present a Python-based application called G-Drawer for automatically generating floor plans with a minimum number of bends. G-Drawer takes any plane triangulated graph as an input and outputs a floor plan layout with minimum bends. It prioritizes generating a rectangular floor plan (RFP); if an RFP is not feasible, it then generates an orthogonal floor plan or an irregular floor plan. G-Drawer modifies orthogonal drawing techniques based on flow networks and applies them on the dual graph of a given PTG to generate the required floor plans. The results of this article demonstrate the efficacy of G-Drawer in creating efficient floor plans. However, in future, we need to work on generating multiple dimensioned floor plans having non-rectangular rooms as well as non-rectangular boundary. These enhancements will address both mathematical and architectural challenges, advancing the automated generation of floor plans toward more practical and versatile applications.

The operational reliability of large mechanical equipment is typically influenced by the functional effectiveness of key components. Consequently, prompt repair before their failure is necessary to ensure the dependability of mechanical equipment. The prognostic and health management (PHM) technology could track the system’s health state and timely detect faults. Therefore, the remaining useful life (RUL) prediction as one of the key components of PHM is rather important. Accurate RUL prediction results could be the data support for condition-based equipment maintenance plans. Also, it could increase the dependability and safety of mechanical equipment while reducing the loss of human and financial resources and meet the requirements of sustainable manufacturing in the Industry 4.0 era. However, with the widespread use of deep learning in the field of intelligent manufacturing, there is a lack of review on RUL prediction based on deep learning. In this paper, different deep learning-based RUL prediction methods for mechanical components are summarized and classified, along with their pros and cons. Then, the case study on the C-MAPSS dataset is mainly conducted and different methods are compared. And finally, the difficulties and future directions of the RUL prediction in practical scenarios are discussed.

This study is predicated on the limited scholarly exploration of the connection between logos and the architectural spaces associated with these brands. The primary objective of this paper is to investigate the relationship between a brand’s corporate identity and its architectural structures through a holistic approach, leveraging artificial intelligence (AI) as a design tool. To achieve this, this study conducts an interdisciplinary literature review, synthesizing existing works in both architecture and branding. The research methodology follows a qualitative, exploratory framework, focusing on the formal and aesthetic evaluations of AI-driven visual outputs. In this context, the central aim of this study is to explore the use of contemporary technologies as a design instrument within the architectural domain. Another key objective is to examine the application of AI as a methodological tool for architectural design within the context of corporate identity. To this end, architectural forms were visually generated using text-to-image and image-to-image, with the resulting products assessed in terms of architectural presentation techniques, visual quality, and aesthetic strategies. For the study’s empirical component, brands ranked at the top of the 2023 Best Global Brands report were selected as the sample, and AI-driven architectural productions were created based on their logos. The findings suggest that AI, with its diverse styles and capabilities, can serve as a design parameter within architectural practice. This study contributes to the discourse on the evolving intersection of AI, branding, and architectural design, proposing new perspectives on the integration of these domains in the design process.

This study compares the design practices and performance of ChatGPT 4.0, a large language model (LLM), against graduate engineering students in a 48-h prototyping hackathon, based on a dataset comprising more than 100 prototypes. The LLM participated by instructing two participants who executed its instructions and provided objective feedback, generated ideas autonomously and made all design decisions without human intervention. The LLM exhibited similar prototyping practices to human participants and finished second among six teams, successfully designing and providing building instructions for functional prototypes. The LLM’s concept generation capabilities were particularly strong. However, the LLM prematurely abandoned promising concepts when facing minor difficulties, added unnecessary complexity to designs, and experienced design fixation. Communication between the LLM and participants was challenging due to vague or unclear descriptions, and the LLM had difficulty maintaining continuity and relevance in answers. Based on these findings, six recommendations for implementing an LLM like ChatGPT in the design process are proposed, including leveraging it for ideation, ensuring human oversight for key decisions, implementing iterative feedback loops, prompting it to consider alternatives, and assigning specific and manageable tasks at a subsystem level.

Engineering design requires humans to make complex, multi-objective decisions involving trade-offs where it is challenging to identify the best solution. AI-embedded computational support tools are increasingly used to aid in such scenarios, enhancing the design decision-making process. However, over- or under-reliance on imperfect “blackbox” models may prevent optimal outcomes. To investigate AI-assisted decision-making in engineering design, two complementary experiments (N = 90) were conducted. Participants chose between pairs of aircraft jet engine brackets and were tasked with selecting the better design based on two (Experiment 1) or three (Experiment 2) competing objectives. Participants received simulated AI suggestions, which correctly suggested a better design, incorrectly suggested a worse design, or arbitrarily suggested an approximately equivalent design. At times, these suggestions were accompanied by an example-based explanation. Results demonstrate that participants follow suggestions less than expected when the model can objectively determine the better-performing alternative, often underutilizing the model’s advice to their detriment. When the “better” choice is uncertain, the tendency to follow an arbitrary suggestion differs, with overutilization occurring only in the bi-objective case. There is no evidence that providing an explanation of the model’s suggestion impacts decision-making. The results provide valuable insights into how engineering designers’ multi-objective decisions may be affected – positively, negatively, or not at all – by computational tools meant to assist them.