Traditional Design Process Research Articles

A Matrix-Based Approach to Unified Synthesis of Planar Four-Bar Mechanisms for Motion Generation With Position, Velocity, and Acceleration Constraints

Abstract This paper introduces a novel matrix-based approach for the simultaneous type and dimensional synthesis of planar four-bar linkage mechanisms, accommodating various practical constraints, including position, velocity, acceleration, and joint placements. Traditional design processes segregate type synthesis, the determination of joint and link configurations, from dimensional synthesis, which involves specifying link sizes and pivot locations. This segregation often leads to complexities in addressing the complete design challenge. The novel methodology proposed in this paper departs from the conventional sequential design approach by concurrently evaluating type and dimensional parameters using a data-driven matrix formulation. The crux of the paper’s methodology involves formulating a singular design equation through a transformation matrix, parameterized by the Cartesian parameters of the mechanism’s dyads. This formulation linearly expresses a broad range of constraints, facilitating the identification of viable solutions through singular value decomposition and null space analysis. This integrated approach not only simplifies the synthesis process but also provides direct insights into the mechanism’s parameters, encompassing both type and dimensions, thereby obviating the need for further interpretative steps common to the use of quaternions and kinematic mapping. In essence, the paper presents two main contributions: the development of a unified design equation capable of encompassing a wide array of constraints within the mechanism synthesis process, and the introduction of an algorithm that effectively identifies all potential planar four-bar linkage mechanisms by accurately satisfying up to five constraints. This approach promises to enhance the design and optimization of mechanical systems by offering a more holistic and efficient pathway to mechanism synthesis.

Application and renovation evaluation of Dalian's industrial architectural heritage based on AHP and AIGC.

This paper takes the example of industrial architectural heritage in Dalian to explore design scheme generation methods based on generative artificial intelligence (AIGC). The study compares the design effects of three different tools using the Analytic Hierarchy Process (AHP). It first establishes the key indicator weights for the renovation of industrial architectural heritage, with the criterion layer weights as follows: building renovation 0.230, environmental landscape 0.223, economic benefits 0.190, and socio-cultural value 0.356. Among the goal layer weights, the highest weight is for the improvement of living quality at 0.129, followed by resident satisfaction at 0.096, and educational and display functions at 0.088, while the lowest is for renovation costs at only 0.035. The design schemes are generated using Stable Diffusion, Mid Journey, and Adobe Firefly tools, and evaluated using a weighted scoring method. The results show that Stable Diffusion excels in overall image control, Mid Journey demonstrates strong artistic effects, while Adobe Firefly stands out in generation efficiency and ease of use. In the overall score, Stable Diffusion leads the other two tools with scores of 6.1 and 6.3, respectively. Compared to traditional design processes, these tools significantly shorten the design workflow and cycle, improving design quality and efficiency while also providing rich creative inspiration. Overall, although current generative artificial intelligence tools still have limitations in understanding human emotions and cultural differences, with continuous technological iteration, this method is expected to play a larger role in the design field, offering more innovative solutions for the renovation of industrial architectural heritage.

A 3D Spatial Model to integrate Stormwater Drainage Design into Public Space

Abstract. This paper explores the integration of water sensitive urban design within public space planning using integrated 3D digital technologies. In traditional design processes, drainage and public space design often utilise distinct software and are led by different professionals, which creates significant communication barriers.Although civil and hydraulic engineers have made significant strides in integrating hydrology analysis with multi-objective space design, the collaboration of conventional 2D hydrology with 3D landscape design still presents challenges. To address these challenges, this study proposes a novel approach that integrates drainage analysis and design within a 3D design model. This methodology is specifically designed to aid landscape and urban designers in incorporating stormwater management and drainage systems early in the design process. The approach involves three key steps: (1) identifying critical factors that impact, or are impacted by, drainage design; (2) translating these factors into specific spatial parameters; and (3) developing a workflow to integrate these parameters. This integration not only streamlines the design process, making it more cohesive and efficient but also mitigates potential conflicts between drainage requirements and spatial design, thereby reducing unnecessary revisions. Ultimately, this approach enhances the incorporation of essential drainage considerations into urban and landscape planning, leading to more sustainable and functional public spaces.

Automated construction site layout design system for prefabricated buildings using transformer based conditional GAN

Construction site layout plans (CSLP) are crucial for efficient prefabricated construction project management. Traditional manual design process is costly and time-consuming, while optimization methods heavily depend on expert knowledge. Recent advancements in deep generative models present promising alternatives. However, their application to the generation of prefabricated construction site layouts is hindered by several challenges, including limited datasets, significant overlap between facilities, and the necessity to generate layouts based on fixed facilities with specific attributes such as minimal transportation costs. These challenges constrain the efficacy and applicability of the generated layouts. To address these issues, this study introduces an innovative automated generative design system for prefabricated construction site layouts, leveraging a novel Transformer-based conditional generative adversarial network (GAN). The data preparation module of the system collects and augments layout data for training. The CSLGAN module is designed to generate layouts that conform to spatial constraints and desired attributes, with minimal facility overlap. Furthermore, this study establishes benchmarks in terms of model capacity and specialized performance metrics. Extensive experiments demonstrate the effectiveness of the proposed system in automated construction site layout generation.

A generative-AI-based design methodology for car frontal forms design

With the advancement of artificial intelligence, big data, and cloud computing, numerous generative AI applications have surfaced. In contrast to conventional generative design and computer-aided design tools, these applications significantly enhance design productivity. However, there are currently few design methodologies based on generative AIs in the academic community to improve the efficiency of industrial designers and optimize the design process. This study introduces a creative and practical methodology for designing car frontal forms based on generative AIs. In this methodology, imagery adjectives describing car frontal forms are generated by sending valid prompts to the text-generative AI application GPT-4.0. Then input typical imagery adjectives into the image-generative AI Midjourney successively as prompts to generate many car frontal forms that align with the typical imagery adjectives, forming a reference form database. Subsequently, a base form is selected, and target imageries are defined. Simultaneously, forms from the reference form database, conforming to the target imageries, are chosen as the reference forms. The main form elements of the base and reference forms are then delineated using cubic Bézier curves. Finally, a form curve blending algorithm is applied to obtain a set of alternatives, and the image-generative AI application Vega AI is utilized to convert the alternatives into three-dimensional renderings. FAHP-based expert evaluation and consumer perceptual evaluation are employed to validate the alternatives. Results indicate that the alternatives effectively capture the imageries of the reference forms. The proposed generative-AI-based design methodology enhances the efficiency of industrial designers, thereby minimizing human and material costs in product development. Additionally, this study presents a design case using various generative AIs to inspire designers to re-examine the traditional design process.

Innovative Upcycling: The Creative Potential of Collaborating with Degraded Materials

This research explores the detailed methods of upcycling, contrasting them with traditional linear design processes through autoethnographic documentation. By centering on a material-oriented view of reuse, the study uncovers how upcycling actively interacts with a material’s past, encompassing human imprints, temporality, fragmentation, and signs of aging. While commonly acknowledged as reuse of worn garments, upcycling can entail intricate interactions with historical remnants and weathered materials, including the challenges posed by aged material patina and its transient nature. The research challenges the prevailing upcycling literature’s limited dialogue of the nuanced complexities associated with working with aging materials, which instead often prioritizes consumer-driven goals within a circular remanufacturing context. Through practices like upcycling, which center on physical making and materials, we uncover insights and opportunities for impactful change often overlooked in traditional material processes, design planning, and manufacturing processes.

Application of gamification based virtual robots in urban landscape Design: Interaction and entertainment experience in the design process

The traditional design process lacks fun and participation, so new elements need to be introduced to enhance the attraction and creativity of the design. The goal of the research is to design an urban landscape design method based on gamified elements and virtual robots to increase the interactivity and entertainment experience in the design process, and to explore its impact on the design results. This paper proposes an urban landscape design framework based on gamified elements and virtual robots, which includes multiple stages in the design process, each of which introduces different gamified tasks and interactions of virtual robots. Designers can gain new design ideas and inspiration by completing tasks and interacting with virtual robots. The study evaluated the effects of design methods using gamification elements and virtual robots on the design process and design results. The results show that this approach effectively increases the engagement and enjoyment of the design process, while also promoting innovation and sustainability of the design results. The urban landscape design method based on gamification elements and virtual robots has broad application prospects, which can bring more creativity and fun to urban landscape design.

Impact of Artificial Intelligence on Mechanical Engineering: A Comprehensive Overview

Artificial Intelligence (AI) is coming to mainstream and as emerged as a transformative force in various fields not just limited including mechanical engineering. This paper provides an overview of the profound impact of AI on the practice and evolution of mechanical engineering. The usage of AI technologies in the field of mechanical engineering has potential to revolutionize traditional design, manufacturing, and maintenance processes. With AI-powered design tools engineers now can generate optimized designs faster with greater efficiency, leading to enhanced product performance and reduced development cycles. Further, Predictive/forecasting method of AI in maintenance systems facilitate early detection of equipment failures, thereby minimizing downtime and maintenance costs.

Automatic topology and capacity generation framework for urban drainage systems with deep learning-based land use segmentation and hydrological characterization

This paper proposed a comprehensive and automated framework that integrates the entire urban drainage design process, from refined land use segmentation, to catchment subdivision and characterization, to network topology generation, and finally to hydraulic estimation of pipeline capacity, based on multiple open-source datasets. The method incorporated deep learning-based semantic segmentation, GIS-based hydrological characterization, topology generation and hydraulic calculation algorithms. The performance and applicability of the method were validated through case studies of two cities. The results showed that the deep learning model with prior knowledge could accurately segment urban land use to obtain the detailed shape and boundary description, and achieved higher detection accuracy for water bodies, buildings, and roads. The stormwater drainage topology and conveyance capacity were automatically generated based on subcatchment division and hydrological characterization under a set of topological parameter conditions. In the optimal Critical Success Index (CSI) scenario, only 8% and 21% of the generated pipelines were inconsistent with the expert-designed networks. Compared to the traditional manual design process, the automated approach can save significant time and resources and reduce the reliance on field surveys and empirical/expert work. At the same time, the tool can be employed for system layout and capacity optimization, providing important support for the automation, design, and rehabilitation of stormwater drainage systems.

Prediction of strong coupling in resonant perovskite metasurfaces by deep learning.

Resonant metasurfaces are often used to achieve strong coupling, and numerical simulations are the common method for designing and optimizing structural parameters of metasurfaces, while their calculation process takes a lot of time and occupies more computing resources. In this work, the deep learning strategy is proposed to simulate the strong coupling phenomenon in resonant perovskite metasurfaces. The designed fully connected neural network is constructed based on the deep learning algorithm that is used to predict transmission spectra, multipole decomposition spectral lines, and anti-cross phenomena of a perovskite metasurface. Through comparison of numerical simulation results, it can be seen that the neural network can efficiently and accurately predict the strong coupling phenomenon. Compared with the traditional design process, the proposed deep learning model can guide the design of the resonant metasurface more quickly, which significantly improves the feasibility of the design in complex metasurface structures.

제품디자인에서 생성형 인공지능 활용을 통한 협업프로세스 활용 가능성 탐색

With the rapid advancement of generative AI, most industries are entering a new phase. Generative AI is being utilized across a wide range of fields, including design. Consequently, this study explored the application of AI in the product design field and integrated it into the design process. The goal was to identify the differences between traditional product design processes and those enhanced by AI. Through this research, the potential of AI in product design was confirmed, and future methods of its application were investigated. First, the study considered the domains of human and AI design separately. AI's role was seen as generating and displaying various images, while humans decided on keywords and concrete designs. Next, experimental results indicated that at crucial moments in the design process, the designer’s ability and judgment in selecting designs remained important. Additionally, the effective use of prompts through questions became crucial in applying generative AI. Language, as the culmination of human knowledge and the most fundamental means of expression, plays a critical role. Ultimately, the effectiveness and efficiency of AI usage will depend on the user's level of knowledge in the respective field or their ability to extract knowledge from AI learning. As AI technology advances, paradigms across all industries are shifting. This phenomenon will merge with traditional design methods, resulting in an evolved form of design approach.

생성AI를 이용한 디자인 리서치 방법론 제안

This study explores the impact of AI tools like ChatGPT on design methodologies, focusing on the Double Diamond Model. It aims to understand how AI can transform traditional design processes and offer insights into the future of design. Design experts from industry and academia were selected using intentional and snowball sampling. Focus Group Interviews(FGI) and In-Depth Interviews(IDI) were conducted over two weeks. Results were validated through discussions and Denzin's triangulation method. ChatGPT positively influences design thinking, data-driven decision-making, and user experiences. It enhances efficiency and creativity in the design process, leading to innovative changes in the industry. This research lays the foundation for understanding AI's potential impact on design. The integration of AI and design is expected to reshape design methodologies, exemplified by the AI-Driven Double Diamond Model, with broad applications across the industry.

Enhancing BIM Integration: A Comparative Analysis of Novel Composite Structure Documentation Methods

The proper selection and planning of building materials are crucial tasks in architectural design, as they fundamentally impact the functioning of the structure. In traditional design processes, this information is recorded in text form, typically using word processing software. However, this approach hinders the integration with modern, data-driven design methods and is incompatible with the increasingly popular building information modeling (BIM) processes. To address this, two new methods have been developed: one database-like method in the form of an Excel spreadsheet and the other as a dedicated web application. This article introduces and compares these methods based on pilot projects conducted by university students and an expert. Based on the results of the study conducted among students and expert, the database-like method proves to be the fastest. For students, creating a composite structure took an average of 14–20 min, while for experts, it took an average of 1.2 min. According to the evaluation of participants, the traditional method does not facilitate automatic communication with BIM, while both the database and web solutions promote it. The web-based solution, with its dedicated layout and functionality, offers additional advantages in this regard. The research underscores the importance of structured data in BIM and proposes new methods to streamline composite documentation processes during the design phase.

Machine learning in prediction of residual stress in laser shock peening for maximizing residual compressive stress formation

Laser Shock Peening (LSP) is an advanced technique for enhancing surface properties, drawing significant interest for its ability to induce beneficial residual stresses in materials. Traditional LSP design processes, reliant on manual parameter selection, often result in imprecise control over the stress distribution, necessitating multiple iterations and high costs. This study introduces a machine learning (ML)-based approach, utilizing the Random Forest (RF) algorithm, to automate and optimize the design of LSP parameters for nickel-aluminium bronze surfaces. Our findings demonstrate the RF model’s capability to accurately predict and optimize residual stress distributions, achieving compressive stresses up to 472 MPa with a notable reduction in design iterations. The model forecasts both uniform and non-uniform stress patterns, particularly identifying areas susceptible to Residual Stress Holes (RSH) with improved precision. With an Absolute Percentage Error (APE) of only 6.2 %, our approach significantly outperforms traditional ML algorithms, offering a novel method for efficiently designing complex residual stress fields in LSP applications.

HVAC Design Optimization for Pharmaceutical Facilities with BIM and CFD

Building Information Modeling (BIM) has been widely used in the past decade to enhance the design quality of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, in specialized areas such as pharmaceutical facilities, HVAC design has traditionally relied on Computer-Aided Design (CAD) drawings. This conventional approach does not allow for the simulation of temperature distribution or the verification of system efficiency, which may lead to design failures. To address these challenges in pharmaceutical facilities, this study proposed a BIM-based approach for optimizing HVAC design with Computational Fluid Dynamics (CFD). By employing CFD to simulate the dynamic airflow conditions of pharmaceutical clean rooms, the effectiveness of HVAC systems can be verified. A case study of a clean room HVAC design is presented to demonstrate the workflow. The results of the case study indicated that the pharmaceutical temperature requirements were met within 1 °C during the design optimization simulation, and there was a 95% match in the 72 h temperature mapping test during site validation. The results confirmed that using CFD with BIM not only successfully simulates the design intentions of indoor air quality but also suggests HVAC system optimization for the required clean room design. The findings of this paper contribute to the body of knowledge on overcoming the limitations of the traditional CAD-based HVAC design process and provide valuable insights on optimizing HVAC design with BIM and CFD technologies.

A novel deep learning strategy to optimize Al2O3–SiO2 porous ceramics with phosphate tailings as raw material

The Al2O3–SiO2 porous ceramics (ASPC) prepared from phosphorus tailings usually showed a large fluctuation in material properties due to the complex composition of the raw material, which required extensive experimental exploration to optimize their properties. To solve the above problem, a novel deep learning strategy for automatically adjust hyperparameters had been proposed to predict the property of ASPC based on the small experimental datasets in this work. Initially, phosphate tailings and bauxite were used as raw materials. Active carbon, ranging from 0 to 15 wt%, was added as a pore-forming agent. After mixing the raw materials, they were shaped under a pressure of 5 MPa and sintered in the temperature range of 1000–1200 °C to produce ASPC, the density and compressive strength of the ASPC were measured to train the artificial neural network (ANN). An exhaustive search was conducted to identify the optimal combination of hyperparameters suitable for small datasets, then the influence of each hyperparameter on the training performance of ANN was analyzed. Subsequently, the trained model was used to predict the feature space of ASPC, and the key processes for regulating ASPC performance were explored, thereby the experimental schemes that meet target performance (density and compressive strength) were identified. Three groups of experimental schemes were predicted, whose properties were compared with the experimental ones. The results showed that the mean squared errors (MSE) of 0.03 and 0.85 for density and compressive strength, respectively. Furthermore, the ASPC exhibited excellent comprehensive properties when 70 wt% phosphorus tailings and 30 wt% bauxite were added, whose apparent porosity was 59.11 %, density was 1.16 g/cm3 and cold compressive strength was 6.49 MPa. And the predicted data trend influence of the pore-forming agent content and sintering temperature on the performance of ASPC is the same as the experimental data. This research provides valuable guidance for addressing the limited controllability of traditional experimental design processes and accelerate the preparation of ASPC from solid waste.

Asymptotic tracking control for a class of uncertain nonlinear systems with output constraint

In this paper, we investigate the asymptotic tracking control for a class of strict-feedback nonlinear systems with unknown nonlinear functions and output constraint. The nonlinear transformation function-based system transformation approach is employed to convert the initial constrained system into an unconstrained one. To achieve the objective of asymptotic tracking, the tracking error is scaled by an exponential proportional function. Furthermore, to address the challenge of repetitive derivation of virtual control laws that occurs in the traditional design process of high-order backstepping methods, a first-order filter is applied. The designed control scheme guarantees that the system output will asymptotically track a predefined reference tracking trajectory while ensuring that the output constraint is not violated. Moreover, it is shown that the filter error will converge asymptotically to the origin. The effectiveness of the developed control strategy is verified through simulation results.

A New Approach to Interior Design: Generating Creative Interior Design Videos of Various Design Styles from Indoor Texture-Free 3D Models

Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence.

Development of a Novel Codesign Method for Use in Early-Stage High-Performance Craft Design

The performance requirements of modern vessels have increased significantly over time, introducing unique challenges in design and analysis. Driven by competition, such as in the case of racing craft, new high performance vessels require design spaces that push the envelope of hydrodynamic technology. This optimization knowledge resides in the experience of racing experts and hasn’t yet been translated into a naval architecture taxonomy. This paper seeks to bridge the knowledge gap between experienced race manufacturers and naval architects, and in doing so, delineate a design methodology. Modeling risk as a function of vessel speed, as well as coupling the design of the control system in conjunction with its physical design embodiment, allows the overall system to reach a greater point of optimality than what can be accomplished by traditional iterative design processes alone. The approach will be demonstrated utilizing the design of a University of Michigan student-led undergraduate high-speed, electric boat competition team design. The team’s goal is to develop a vessel that has a top speed of 135 mph. The paper will discuss how the team used marine design methodologies integrated with a novel codesign method to create the design that is currently under construction for professional racing use by the team.

LLM enabled generative collaborative design in a mixed reality environment

In the collaborative design process, diverse stakeholder backgrounds often introduce inefficiencies in collaboration, such as delays in design delivery and decreased creativity, primarily due to misunderstandings and communication barriers caused by this diversity. To respond, this study proposes an AI-augmented Multimodal Collaborative Design (AI-MCD) framework. This framework utilizes Large Language Models (LLM) to establish an iterative prompting mechanism that provides professional design prompts for Generative AI (GAI) to generate precise visual schemes. On this basis, the GAI cooperates with Mixed Reality (MR) technology to form an interactive and immersive environment for enabling full participation in the design process. By integrating these technologies, the study aims to help stakeholders form a unified cognition and optimize the traditional collaborative design process. Through a case study involving the development of heart education products for children, the effectiveness of the framework is emphasized, and the practical application and effectiveness of the proposed method innovation are demonstrated.