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.

By the reason that mathematical analysis is not feasible for practical control of buildings, decentralized control (DC) and fuzzy control (FC) technologies were introduced to optimize the control problem of high-rise building (HRB) structures. For the control problem of HRB structures, magnetorheological fluid dampers (MRFDs) were introduced to optimize the lateral stress problem of each floor, and the influence of different output variables on FC was compared. In the analysis of fuzzy DC experiments, there were significant differences in the impact of different structural controls (SCs) on building acceleration. In the comparison of the interstory displacement (ISD) time history of the lower concrete structure, the maximum ISD value without control was -12 cm in the nineth second, −7 cm in the nineth second of LQR (linear quadratic regularization) control, and -6 cm in the FC. The proposed biomedical evolutionary technology had better SC effects in practical scenarios, with better safety and stability. The research was mainly based on FC controller technology, and in the future, updated IT2FL (interval type2 fuzzy logic) control technology can be adopted. At the same time, machine learning models are used to optimize parameter problems and improve the control effect of concrete structures. Therefore, fluid dampers help reduce vibrations caused by external earthquakes and other dynamic loads. By dampening devices, fluid dampers enhance the overall stability of the building by improving comfort levels. By allowing for lighter structural designs, fluid dampers can reduce the amount of material needed for construction, leading to cost savings. With reduced vibrations and stresses, there may be fewer maintenance issues over time. Fluid dampers can be designed for various types of structures and can be used in conjunction with other damping systems, making them flexible solutions for different engineering challenges. The future study can be effectively combined with base isolation systems to further improve a building’s resilience against seismic forces.

Various factors are considered when designing a floorplan layout, including the plan’s outer boundary, room shape and size, adjacency, privacy, and circulation space, among others. While graph-theoretic approaches have proven effective for floorplan generation, existing algorithms generally focus on defining the boundary of the plan or different room shapes, lacking the investigation of designing circulation space within a floorplan. However, the circulation design in architectural planning is a crucial factor that affects the functionality and efficiency of areas within a building. This paper presents a graph-theoretic approach for integrating circulation within a floorplan. In this study, we use plane graphs to represent floorplans and develop graph algorithms to incorporate various types of circulation within a floorplan as follows:

1. i. The first phase generates a spanning circulation, that is, a corridor leading to each room using a circulation graph.

2. ii. Subsequently, using an approximation algorithm, the circulation space is minimized, that is, generation of minimum circulation space covering all the rooms, thereby enhancing space utilization in the floorplan.

3. iii. Furthermore, customized circulations are generated to cater to user preferences, distinguishing between public and private spaces within the floorplan.

In addition to the theoretical framework, we have implemented our algorithms in Python and developed a user-friendly graphical interface (GUI), enabling seamless integration of our algorithms into architectural design processes.

This article presents a novel conversational artificial intelligence (CAI)-enabled active ideation system as a creative idea generation tool to assist novice product 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 dialog-based interaction, context-sensitive conversation, and prolific idea generation. An empirical study was conducted with 30 novice product designers to generate multiple ideas for given problems using traditional methods and the new CAI-based interface. The ideas generated by both methods were qualitatively evaluated by a panel of experts. The findings demonstrated the relative superiority of the proposed tool for generating prolific, meaningful, novel, and diverse ideas. The interface was enhanced by incorporating a prompt-engineered structured dialog style for each ideation stage to make it uniform and more convenient for the product designers. A pilot study was conducted and the resulting responses of such a structured CAI interface were found to be more succinct and aligned toward the subsequent design stage. The article thus established the rich potential of using generative AI (Gen-AI) for the early ill-structured phase of the creative product design process.

The utilization of creative design methodologies plays a pivotal role in nurturing innovation within the contemporary competitive market landscape. Although Theory of Inventive Problem Solving (TRIZ) has been recognized as a potent methodology for engendering innovative concepts, its intricate nature and time-consuming learning and application processes pose significant challenges. Furthermore, TRIZ has faced criticism for its limitations in processing design problems and facilitating designers in knowledge acquisition. Conversely, Environment-Based Design (EBD), a question-driven design methodology, provides robust methods and approaches for formulating design problems and identifying design conflicts. Large Language Models (LLMs) have also demonstrated the ability to streamline the design process and enhance design productivity. This study aims to propose an iteration of TRIZ integrated by EBD and supported by an LLM. This LLM-based conceptual design model assists designers through the conceptual design process. It begins by using question-asking and answering methods from EBD to gather relevant information. It then follows the EBD methodology to formulate the information into an interaction-dependence network, leading to the identification of functions and conflicts required by TRIZ. Lastly, TRIZ is used to generate inventive solutions. An evaluation is carried out to measure the effectiveness of the integrated approach. The results indicate that this approach successfully generates questions, processes designers’ responses, produces functional analysis elements, and generates ideas to resolve contradictions.

The rise of generative artificial intelligences (AIs) has quickly made them auxiliary tools in numerous fields, especially in the creative one. Many scientific works already discuss the comparison of the creative capacity of AIs with human beings. In the field of Engineering Design, however, numerous design methodologies have been developed that enhance the creativity of the designer in their idea generation phase. Therefore, this work aims to expand previous works by leading a Generative Pre-trained Transformer 4 (GPT-4) based generative AI to use a design methodology to generate creative concepts. The results suggest that these types of tools can be useful for designers in that they can inspire novel ideas, but they still lack the necessary capacity to discern technically feasible ideas.

In this paper, an improved U-net welding engineering drawing segmentation model is proposed for the automatic segmentation and extraction of sheet metal engineering drawings in the process of mechanical manufacturing, to improve the cutting efficiency of sheet metal parts. To construct a high-precision segmentation model for sheet metal engineering drawings, this paper proposes a U-net jump structure with an attention mechanism based on the Convolutional Attention Module (CBAM) attention mechanism. At the same time, this paper also designs an encoder jump structure with vertical double pooling convolution, which fuses the features after maximum pooling+convolution of the high-dimensional encoder with the features after average pooling+convolution of the low-dimensional encoder. The method in this paper not only improves the global semantic feature extraction ability of the model but also reduces the dimensionality difference between the low-dimensional encoder and the high-dimensional decoder. Using Vgg16 as the backbone network, experiments verify that the IoU, mAP, and Accu indices of this paper’s method in the welding engineering drawing dataset segmentation task are 84.72%, 86.84%, and 99.42%, respectively, which are 22.10, 19.09 and 0.05 percentage points higher compared to the traditional U-net model, and it has a relatively excellent value in engineering applications.

The engineering-to-order (ETO) sector, driven by the demands of new energy transition markets, is witnessing rapid innovation, especially in the design of complex systems of turbomachinery components. ETO involves tailoring products to meet specific customer requirements, often posing coordination challenges in integrating engineering and production. Meeting customer demands for short lead times without imposing high price premiums is a key industry challenge. This article explores the application of artificial neural networks as an enabler for design automation to deliver a first tentative optimal design solution in a short period of time with respect to more computationally demanding optimization methods. The research, conducted in collaboration with an energy company operating in the Oil & Gas and energy transition markets, focuses on the design process of reciprocating compressors as a means of study to develop and validate the developed methodology. Three case studies corresponding to as many representative jobs related to reciprocating compressor cylinders have been analyzed. The results indicate that the proposed method performs well within its training boundaries, delivering optimal solutions and providing reasonably accurate predictions for target configurations beyond these boundaries. However, in cases requiring a creative redesign using artificial neural networks may lead to errors that exceed acceptable tolerance levels. In any case, this methodology can significantly assist design engineers in the efficient design of complex systems of components, resulting in reduced operating and lead times.

Morphological matrices (MMs) have traditionally been used to generate concepts by combining different means. However, exploring the vast design space resulting from the combinatorial explosion of large MMs is challenging. Additionally, all alternative means are not necessarily compatible with each other. At the same time, for a system to achieve long-term success, it is necessary for it to be flexible such that it can easily be changed. Attaining high system flexibility necessitates an elevated compatibility with alternative means of achieving system functions, which further complicates the design space exploration process. To that end, we present an approach that we refer to as multi-objective technology assortment combinatorics. It uses a shortest-path algorithm to rapidly converge to a set of promising design candidates. While this approach can take flexibility into account, it can also consider other quantifiable objectives such as the cost and performance of the system. The efficiency of this approach is demonstrated with a case study from the automotive industry.

Artificial intelligence (AI) facilitates designers in generating creative ideas. How designers work with AI to effectively stimulate and enhance their creativity is an urgent topic in the context of design education and creation. This study conducted a controlled experiment in a design education context to explore the effects of generative AI tools and visual stimuli on the creativity of designers in the early design stages. The results show that the use of text-to-image (T2I) AI tools and the stimulation of abstract visuals enhance the design creativity in both divergent and convergent thinking processes. However, it is important to be aware of the design fixation during this process. This study sheds light on the role and value of different AI tools for designers in the design process, offers a more effective solution of using AI for designers so as to improve creativity and design quality, and provides a theoretical basis for the application of AI-assisted design process.

As artificial intelligence grows, human–robot collaboration becomes more common for efficient task completion. Effective communication between humans and AI-assisted robots is crucial for maximizing collaboration potential. This study explores human–robot interactions, focusing on the differing mental models used by humans and collaborative robots. Humans communicate using knowledge, skills, and emotions, while robotic systems rely on algorithms and technology. This communication disparity can hinder productivity. Integrating emotional intelligence with cognitive intelligence is key for successful collaboration. To address this, a communication model tailored for human–robot teams is proposed, incorporating robots’ observation of human emotions to optimize workload allocation. The model’s efficacy is demonstrated through a case study in an SAP system. By enhancing understanding and proposing practical solutions, this study contributes to optimizing teamwork between humans and AI-assisted robots.

This study investigates the applicability of generative artificial intelligence (AI) in early-stage architectural design by evaluating the daylight performance of AI-generated sustainable housing plans across five distinct climate zones. A three-phase methodology was implemented: (1) Plan generation using text-to-image diffusion models (ChatGPT, Copilot, and LookX); (2) digital reconstruction in AutoCAD; and (3) daylight simulation via Velux Daylight Visualizer. Climate-adaptive prompts were formulated to guide the AI tools in producing context-specific floor plans with passive strategies. Out of 31 initial plans, eight valid outputs (five from ChatGPT and three from Copilot) were reconstructed in AutoCAD and simulated. Quantitative simulations were conducted on equinox and solstice dates, and average illuminance values were analyzed for key interior spaces (living room, kitchen, and bedroom). ChatGPT-generated plans demonstrated higher spatial clarity and more balanced daylight performance, whereas Copilot outputs varied significantly, and LookX was excluded due to insufficient architectural legibility. Results revealed that none of the models consistently integrated solar orientation or seasonal lighting considerations, indicating a gap between generative representation and environmental logic. The research contributes a replicable workflow that bridges generative AI and performance-based evaluation, offering critical insight into the current limitations and future potential of AI-assisted architectural design. The findings underscore the need for next-generation AI systems capable of semantic, spatial, and climatic reasoning to support environmentally responsive design practices.

Design-by-analogy (DbA) is a powerful method for product innovation design, leveraging multidomain design knowledge to generate new ideas. Previous studies have relied heavily on designers’ experiences to retrieve analogical knowledge from other domains, lacking a structured method to organize and understand multidomain analogical knowledge. This presents a significant challenge in recommending high-quality analogical sources, which needs to be addressed. To tackle these issues, a knowledge graph-assisted DbA approach via structured analogical knowledge retrieval is proposed. First, an improved function-effect-structure ontology model is constructed to extract functions and effects as potential analogical sources, and six semantic matching rules are established to output entity triplets, and the DbA knowledge graph (DbAKG) is developed. Second, based on the knowledge of semantic relationships in DbAKG, the domain distance and similarity between the design target and the analogical sources are introduced to establish an analogical value model, ensuring the novelty and feasibility of analogical sources. After that, with function as the design target, analogical sources transfer strategy is formed to support innovative solution solving, and TRIZ theory is used to solve design conflicts. Finally, a pipeline inspection robot case study is further employed to verify the proposed approach. Additionally, a knowledge graph-assisted analogical design system has been developed to assist in managing multidomain knowledge and the analogical process, facilitate the adoption of innovative design strategies, and assist companies in providing more competitive products to seize the market.