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Introduction. In the middle of the 20th century, technological progress made it possible to enlarge the building elements of buildings as much as possible, which led to the development of new industrial construction systems and increased production rates. However, to date, the issue has not been completely resolved and remains relevant. Modular construction is not sufficiently widely spread today, but many design experts predict its widespread introduction in the near future. The factor that decelerates the development of modular construction is the lack of theoretical foundations in the field of modular design, as well as significant differences from traditional design approach.Materials and methods. The purpose of this work is to develop the theoretical fundamentals of modular design based on the study of regulatory documents, Russian and foreign sources of research works, as well as the implemented projects. The task is to determine the fundamental features of modular elements of maximum readiness: principles of modular design, parameters of a modular element, advantages and disadvantages of a modular system, features of the design processes.Results. The theoretical basis of modular design includes basic concepts, principles and features; the conceptual design process is described using the case of development of an information model; parameters of modular elements of maximum readiness are presented, and the influence of modular items on the life cycle of a construction facility is analyzed.Conclusions. The presented system of principles of design based on modular elements of maximum readiness and parameters of modular elements allow us to systematize the established theoretical aspects of modularity, which make it possible to make modular buildings. The author formulates a new approach to design, which affects the entire life cycle and serves as the basis for new areas of engineering activities and scientific research.

Piezoelectric microelectromechanical system (MEMS) converts mechanical motion to electrical energy for low-power application. The device can be used continuously to harvest energy from the environment and therefore the electrical energy generated is clean and renewable. Due to this, piezoelectric MEMS is increasingly favorable and stands as potential candidate for self-powered Internet of Thing (IoT) based electronic products and wireless electronic systems. Conventionally, piezoelectric devices used for energy harvesting are based on cantilever structure which is generally fabricated through series of dedicated manufacturing processes. Surface acoustic wave (SAW) based piezoelectric device on the other hand can be fabricated via monolithic photolithography technique which is much simpler and cost effective compared to the fabrication of the cantilever in micro/nano scale. However, the design procedure of such SAW device for self-powered electronic is generally much complex and complicated to be followed than the earlier design. Hence, this paper presents the exploratory work in designing the SAW based MEMS device for self-powered electronic using customized back propagated deep learning neural network. The limitations of the work and related resolutions are highlighted and discussed in the paper. Overall, the result shows promising outlook in using the artificial intelligence technique for design automation of the SAW device.

Analog circuit design is comparatively more complex than its digital counterpart due to its nonlinearity and low level of abstraction. This study proposes a novel low-level hybrid of the sine-cosine algorithm (SCA) and modified greywolf optimization (mGWO) algorithm for machine learning-based design automation of CMOS analog circuits using an all-CMOS voltage reference circuit in 40-nm standard process. The optimization algorithm’s efficiency is further tested using classical functions, showing that it outperforms other competing algorithms. The objective of the optimization is to minimize the variation and power usage, while satisfying all the design limitations. Through the interchange of scripts for information exchange between two environments, the SCA-mGWO algorithm is implemented and simultaneously simulated. The results show the robustness of analog circuit design generated using the SCAmGWO algorithm, over various corners, resulting in a percentage variation of 0.85%. Monte Carlo analysis is also performed on the presented analog circuit for output voltage and percentage variation resulting in significantly low mean and standard deviation.

Introduction. Building Information Modeling (BIM) and modular construction are modern technologies that ensure the development of the construction industry. Despite certain weaknesses, both areas have strong prospects today. However, the consequences of integrating these technologies into design have not been sufficiently investigated. Therefore, it is advisable to study the total potential effectiveness of information modeling and modular construction technologies, if applied in combination. Hence, the purpose of this work is to apply information modeling tools to a particular construction facility to identify the effectiveness of design based on prefabricated modular elements. It is necessary to solve the following tasks to achieve this purpose: development of performance evaluation criteria, design of an information model based on prefabricated modular elements, comparison of two alternative facilities, drawing conclusions based on the results of the work.Materials and methods. The study is based on a comparative analysis of technical and economic indicators of two alternative facilities, or buildings of data processing centres, whose design documents were developed using modular and traditional approaches. The efficiency analysis is based on the values of design evaluation criteria to be compared. At the same time, the process of designing an information model of a facility, made of prefabricated elements, is considered.Results. As a result, the efficiency assessment of the design based on prefabricated modular elements is presented: a similar facility, whose design documentation was developed in the traditional way, is employed to conduct a comparative analysis. The author compares basic evaluation characteristics to the design developed using two different design concepts. At the same time, the author presents an information model, developed using modular design with account taken of the most recent theoretical and practical data available in this area.Conclusions. The proposed method of evaluating the design efficiency, based on prefabricated modular elements, demonstrates the obvious advantage of the modular method in terms of the facilities under consideration. At the same time, the information model of a facility is demonstrated, aspects of modular design are presented; weaknesses, further development and efficiency improvement trends are identified.

Advances in artificial intelligence (AI) offer new opportunities for human-AI cooperation in engineering design. Human trust in AI is a crucial factor in ensuring an effective human-AI cooperation, and several approaches to enhance human trust in AI have been explored in prior studies. However, it remains an open question in engineering design whether human designers have more trust in an AI and achieve better joint performance when they are deceived into thinking they are working with another human designer. This research assesses the impact of design facilitator identity ("human" versus AI) on human designers through a human subjects study, where participants work with the same AI design facilitator and they can adopt their AI facilitator's design anytime during the study. Half of the participants are told that they work with an AI, and the other half of the participants are told that they work with another human participant but in fact they work with the AI design facilitator. The results demonstrate that, for this study, human designers adopt their facilitator's design less often on average when they are deceived about the identity of the AI design facilitator as another human designer. However, design facilitator identity does not have a significant impact on human designers' average performance, perceived workload, and perceived competency and helpfulness of their design facilitator in the study. These results caution against deceiving human designers about the identity of an AI design facilitator in engineering design. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) offer new opportunities for human–AI cooperation in engineering design. Human trust in AI is a crucial factor in ensuring an effective human–AI cooperation, and several approaches to enhance human trust in AI have been explored in prior studies. However, it remains an open question in engineering design whether human designers have more trust in an AI and achieve better joint performance when they are deceived into thinking they are working with another human designer. This research assesses the impact of design facilitator identity (“human” versus AI) on human designers through a human subjects study, where participants work with the same AI design facilitator and they can adopt their AI facilitator’s design anytime during the study. Half of the participants are told that they work with an AI, and the other half of the participants are told that they work with another human participant but in fact they work with the AI design facilitator. The results demonstrate that, for this study, human designers adopt their facilitator’s design less often on average when they are deceived about the identity of the AI design facilitator as another human designer. However, design facilitator identity does not have a significant impact on human designers’ average performance, perceived workload, and perceived competency and helpfulness of their design facilitator in the study. These results caution against deceiving human designers about the identity of an AI design facilitator in engineering design. [ABSTRACT FROM AUTHOR]

Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (≈ 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design. [ABSTRACT FROM AUTHOR]

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely generative pre-trained transformer 3 (GPT-3), is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The time required to model a single component and then develop the component's family consumes a great deal of time and is a strenuous task. This issue can be resolved by parametrizing the component and adjusting it in response to changes in the design inputs. To be feasible, this approach requires precise experimental data. This strategy of incorporating experimental data and utilising design equations in the back-end enables engineers to develop a valid model for industrial use. This is the reason for the development of the compound die parametric modeller. The modeller enables direct control over the shape and parameters of the die. This enables the user to create an accurate first design before fine-tuning it using more computationally intensive methods. The modeller is written in Python and runs within the AutoCAD environment. The approach of building a variety of compound dies is partly based on state-of-the-art parametrization techniques and makes extensive use of fundamental design variables. The modeller's design resulted in a reduction in the time required for manual modelling via an automated approach. [ABSTRACT FROM AUTHOR]

Implementation of Machine Learning (ML) to improve product and production development processes poses a significant opportunity for manufacturing industries. ML has the capability to calibrate models with considerable adaptability and high accuracy. This capability is specifically promising for applications where classical production automation is too expensive, e.g., for mass customization cases where the production environment is uncertain and unstructured. To cope with the diversity in production systems and working environments, Reinforcement Learning (RL) in combination with lightweight game engines can be used from initial stages of a product and production development process. However, there are multiple challenges such as collecting observations in a virtual environment which can interact similar to a physical environment. This project focuses on setting up RL methodologies to perform path-finding and collision detection in varying environments. One case study is human assembly evaluation method in the automobile industry which is currently manual intensive to investigate digitally. For this case, a mannequin is trained to perform pick and place operations in varying environments and thus automating assembly validation process in early design phases. The next application is path-finding of mobile robots including an articulated arm to perform pick and place operations. This application is expensive to setup with classical methods and thus RL enables an automated approach for this task as well. [ABSTRACT FROM AUTHOR]

Micro-additive manufacturing techniques have the potential to meet the demand for miniaturised functional components for minimally invasive surgical instruments. These techniques create monolithic, compliant mechanisms with micro-sized free-form structures that can be tailored to patient-specific surgical procedures. The automated design synthesis of the mechanisms using building blocks results in structures that are shape-programmable. This is achieved through an algorithmic-based computational workflow, which automatically converts user-specified 2D and 3D curves into discrete curve segments. The actuated motion of the mechanisms can be designed to move in a specific way, both forwardly and inversely. The mechanisms are manufactured using micro-laser powder bed fusion and hardenable stainless steel 17-4 PH. By carefully selecting the process parameters, it is possible to 3D-print micro-sized features such as a compliant beam thickness of 80 μm and an actuation hole of 100 μm. Both 2D planar curved mechanisms and 3D spatial curved mechanisms have been implemented and experimentally validated. [ABSTRACT FROM AUTHOR]

Design engineers are required to deliver design solutions by a fixed deadline. Increasing demand for shortening lead time limits the exploratory and creative freedom of the designer. This paper proposes an automated design system that can substitute part of the repetitive and standardized tasks in the design process, increasing efficiency and shifting the design engineer's efforts to creative work. Computer Aided Welding Fixture Design is an engineering domain increasingly being pressed for shortening lead time. Welding Fixture Designs are multi-part structures that secure an assembly product during a welding process. The compact and complex assembly product to be fixed and welded presents a configuration and geometric challenge for the fixture regarding sequence, collisions, tolerances, welding path, human interaction, etc. This paper presents the welding fixture design problem in its general form. To describe the freedom of automation, the paper introduces the term decision space as the subset of a design space feasible by automation. Depending on the desired outcome, the decision space can be exploited through various strategies and automation approaches. This paper presents one of these approaches by implementing an evolutionary algorithm to the parametric fixture design problem of the use case. The results show reduced engineering efforts and the potential for expanding the exploratory spectrum of design proposals during conceptualization. [ABSTRACT FROM AUTHOR]

The implementation of algorithmic modelling in CAD technologies is an opportunity to reduce manual design work in repetitive design tasks. This increases the importance of design automation and digital workflows. This technology can process the results from computational fluid dynamics (CFD) and finite element analysis (FEA) automatically and can optimize designed geometries sequentially. Nevertheless, this design process often sets high computational requirements. The aim of this paper is to present a design automation workflow that reduces the computational time of a design process. The computational resource requirements of the system are reduced by using knowledge-based engineering techniques to obtain information from previous successful designs, decomposing the design into sub-parts according to their functions and optimizing each sub-part individually. Furthermore, through algorithmic modelling, the different input geometries required for the physical description of each simulation are made separately. This allows different design simplifications to be made for each simulation domain. Once the output of the simulations is obtained, the design is evaluated in CAD to optimize the geometry. After each sub-part has been optimized, the sub-parts are composed to obtain the final design. The case study of a reactor for methanol synthesis supports the results of this paper. [ABSTRACT FROM AUTHOR]

GitHub and GitLab contain many project repositories. Each repository contains many design artifacts and specific project management features. Developers can automate the processes of design and project management with the approach proposed in this paper. We described the knowledge base model and diagnostic analytics method for the solving of design automation and project management tasks. This paper also presents examples of use cases for applying the proposed approach. [ABSTRACT FROM AUTHOR]

The subject of research in the article is decision-making support processes in the tasks of optimizing technological processes (TP) at the stages of their design or reengineering. The goal of the work is to improve the efficiency of technologies of automated design of TP due to the development of mathematical models of the tasks of selecting subsets of effective design solutions with intervally specified characteristics of options. The following tasks have been solved in the article: review and analysis of the current state of the problem of supporting decision-making in the tasks of optimization of TP at the stages of their design or reengineering; decomposition of the problem of making project decisions; formalization of the task of comparing intervals for selection of Pareto fronts using comparison indices based on the generalized Hukuhari difference; development of a mathematical model of the problem for the method based on Carlin's lemma; development of a mathematical model of the problem for the method based on Hermeyer's theorem; determination of the Pareto front in the task of optimization of TP by the method of pairwise comparisons. The following methods were used: system approach, theories of systems, theories of usefulness, theories of decision-making, system design, optimization and operations research. Results. The place and connections of the problem of determining the Pareto front in the problem of making project decisions are determined. A formalized interval comparison procedure for the selection of Pareto fronts using Hukuhari total difference comparison indices. Mathematical models of the problem of selection of Pareto fronts using methods based on Carlin's lemma and Hermeyer's theorem have been developed for the case of interval publication with the value of local criteria. An example of the formation of the Pareto front in the problem of optimization of the technological process by the method of pairwise comparison according to the indicators of the duration of the technological cycle, reliability and specified costs is given. Conclusions. The proposed mathematical models expand the methodological bases of the automation of TP design processes. They make it possible to correctly reduce the set of alternative options for construction of TP for the final choice, taking into account the knowledge, experience of designers and factors that are difficult to formalize. The practical use of mathematical models will allow to increase the degree of automation of design or control processes, to reduce the time of decision-making in conditions of incomplete certainty of input data and to guarantee their quality by selecting them only from a subset of effective ones.

In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology.

Various analog design automation attempts have addressed the shortcomings of the still largely manual and, thus, inefficient and risky analog design approach. These methods can roughly be divided into synthesis and procedural generation. An important key aspect has, however, rarely been considered: usability. While synthesis requires sophisticated constraints, procedural generators require expert programmers. Both prevent users from adopting the respective method. Thus, we propose a new approach to automatically create procedural generators in a user-driven way. First, analog generators, which also create symbols and layouts, are utilized during schematic entry to encapsulate common analog building blocks. Second, automatic code creation builds a hierarchical generator for all views with the schematic as input. Third, the approach links the building block generators with the layout through an object-oriented template library that is accessible through generator parameters, allowing the user to control the arrangement. No programming is required to reach this state. We believe that our approach will significantly ease the transition of analog designers to procedural generation. At the same time, the templates allow for a "bridge" to open frameworks and synthesis approaches so that the methodologies can be both better spread and combined. This way, comprehensive frameworks of both synthesis-based and procedural-based analog automation methods can be built in a user-driven way, and designers are enabled to gain early layout insight and ease IP reusability. [ABSTRACT FROM AUTHOR]

The use of local natural and recycled feedstock is promising for sustainable construction. However, unlike versatile engineered bricks, natural and recycled feedstock involves design challenges due to their stochastic, sequential, and heterogeneous nature. For example, the practical use of stone masonry is limited, as it still relies on human experts with holistic domain knowledge to determine the sequential organization of natural stones with different sizes/shapes. Reinforcement learning (RL) is expected to address such design challenges, as it allows artificial intelligence (AI) agents to autonomously learn design policy, that is, identifying the best design decision at each time step. As a proof-of-concept RL framework for design automation involving heterogeneous feedstock, a stone masonry design framework is presented. The proposed framework is founded upon a virtual design environment, MasonTris , inspired by the analogy between stone masonry and Tetris. MasonTris provides a Tetris-like virtual environment combined with a finite element analysis (FEA), where AI agents learn effective design policies without human intervention. Also, a new data collection policy, almost-greedy policy , is designed to address the sparsity of feasible designs for faster/stable learning. As computation bottleneck occurs when parallel agents evaluate designs with different complexities, a modification of the RL framework is proposed that FEA is held until training data are retrieved for training. The feasibility and adaptability of the proposed framework are demonstrated by continuously improving stone masonry design policy in simplified design problems. The framework can be generalizable to different natural and recycled feedstock by incorporating more realistic assumptions, opening opportunities in design automation for sustainability. [ABSTRACT FROM AUTHOR]

Silicon photonics (Si-photonics) has been established as a potential technology that integrates both electronic and optical circuits on single integrated circuits (ICs) in order to satisfy the increasing demand for high speed and low power in the emerging market of ICs. It has opened up the research directions in the domain of design automation for photonic ICs. On the physical layout of the optical circuits, it is a challenging task to obtain the optimal routing of optical waveguides, while minimizing all the parameters like the number of tracks, total bend loss, worst signal loss, total propagation loss and total crossing loss. In this paper, we proposed two non-Manhattan grid-based methods for reducing the bend loss, worst signal loss and tracks in optical channel routing. First, a 0–1 integer linear programming-based algorithm called minimizing bend loss (MBL) is proposed, which minimizes the total bend loss (TBL) and the worst signal loss (WSL) while reducing the number of tracks (T) over the state-of-the-art technique. The execution time of MBL is very high for the large input. Hence next, a scalable heuristic called reducing bend loss (RBL) is presented that provides a better balance between the reduction of the TBL and T over the state-of-the-art and MBL algorithms. Simulation results show that MBL can reduce the TBL and the WSL by an average of 57.9% and 63.1%, respectively, with an average increase of 12% in T over state-of-the-art algorithms. The simulation results show that the RBL reduces the TBL and the WSL by an average of 39.7% and 41.3%, respectively, with an average increase of 23.7% in T over state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Building information modeling (BIM) is one of the most promising new technological developments that can be used to facilitate design and construction automation. Prior BIM research has primarily focused on permanent building components; temporary structures such as scaffolding and concrete formwork have received little attention. The design and planning of concrete formwork systems, however, would substantially benefit from the capabilities of BIM. Formwork design is repetitive and can be very time-consuming due to the need to analyze multiple parameters of individual permanent components, the latest formwork design guidelines, methods, and procedures, and the available materials. In this study, a BIM-based tool is developed and proposed to assist designers and builders in planning, designing, and generating concrete formwork design models for planning purposes. By integrating the design procedures recommended by the American Concrete Institute (ACI), the streamlining tool automates the design and modeling process for timber concrete formwork through an application programming interface (API) in Revit. The workability of the proposed tool was demonstrated through a case study. The study also employed the survey method to investigate the effectiveness of the proposed tool and its potential for improving worker safety and health from the perspectives of formwork professionals. Using the tool, form designers and planners will be able to determine the most applicable formwork design based on the permanent facility structure along with the availability of construction materials, site conditions present, and worker safety and health considerations. The study also provides contractors with a new tool for planning concrete operations and expands the scope of BIM design. [ABSTRACT FROM AUTHOR]

Adder with high efficiency and accuracy is the major requirement for electronic circuit design. Here the optical logic gate based adder circuit is designed for better performance analysis of optical input signals varied with the wavelength. Efficiency of the adder can be improved by increasing the speed of operation, reducing the complexity and power consumption. To maintain the high efficiency with accuracy, a new combination of adder has been proposed and tested in this work. A new adder by combining the logics of Brent Kung, Sklansky and Kogge Stone adders by Tree Grafting Technique (BSKTGT) has been tested along with individual Brent Kung, Sklansky, Kogge Stone, Knowles, Han Carlson and Ladner Fischer adders. All the existing and proposed adders have been designed and tested for efficiency with the help of Cadence platform with 45 nm technology. Efficiency in terms of Size reduction, Power reduction, Power Delay Product (PDP) and accuracy in adding 8 bit, 16 bit and 32 bit values had been tested for all the adders and found that the 32 bit BSKTGT adder performed well in all aspects and have produced better efficiency with the power consumption of 52.512426 μW with 3.16% of power saving over Brent Kung adder, utilised an area of 631.191 with 8.55% reduction over Kogge Stone Adder, has the cell count of 132 which is 10.61% reduction over Brent Kung Adder and PDP value of 122.6695 J, which is 0.46% less than that of the Han Carlson Adder. [ABSTRACT FROM AUTHOR]

Specialization of configurable heterogeneous ICs for specific customer proposals requires the flexibility of design and technological solutions. This, in turn, leads to the necessity of the automatic adjustment of CAD, including software placement tools, on the changes made. The development of efficient methods and tuning algorithms is impossible without relying on the appropriate mathematical apparatus. This study presents a set-theoretic mathematical model of a hierarchical project and formalizes a hierarchical approach to the netlist using the apparatus of set theory, mathematical logic, and graph theory. To ensure quick configuration of clustering and placement software tools, taking into account the changes made to the basic design, the correspondence between the elements of the basic FPGA design and user-designed circuits from the end user is formalized. The obtained results can serve as a base for the development of the efficient methods and algorithms for the automatic tuning of software clustering and placement tools. [ABSTRACT FROM AUTHOR]

Recently, online user-generated data have emerged as a valuable source for industrial applications. In the consumer product area, many studies analyze online data and draw implications on product design. However, most of them treat online customers as one group with the same preferences, while customer segmentation is a key strategy in conventional market analysis. This paper proposes a new methodology based on text mining and network analysis for online customer segmentation. First, the method extracts customer attributes from online review data. Then, a customer network is constructed based on these attributes and predefined networking rules. For networking, a new concept of "topic similarity" is proposed to reflect social meaning in the customer network. Finally, the network is partitioned by modularity clustering, and the resultant clusters are analyzed to understand segment properties. We validate our methodology using real-world data sets of smartphone reviews. The result shows that the proposed methodology properly reflects the heterogeneity of the online customers in the segmentation result. The practical application of customer segmentation is presented, illustrating how it can help companies design target-customer-oriented products. [ABSTRACT FROM AUTHOR]

Three-dimensional spatial packaging of interconnected systems with physical interactions (SPI2) design plays a vital role in the functionality, operation, energy usage, and life cycle of practically all engineered systems, from chips to ships. SPI2 design problems are highly nonlinear, involving tightly constrained component placement, governed by coupled physical phenomena (thermal, hydraulic, electromagnetic, etc.), and involve energy and material transfer through intricate geometric interconnects. While many aspects of engineering system design have advanced rapidly in the last few decades through breakthroughs in computational support, SPI2 design has largely resisted automation and in practice requires at least some human-executed design steps. SPI2 system reasoning and design decisions can quickly exceed human cognitive abilities at even moderate complexity levels, thwarting efforts to accelerate design cycles and tackle increasingly complex systems. Existing design methods treat pieces of the SPI2 problem separately without a fundamental systems approach, are sometimes inefficient to evaluate various possible designs, and present barriers to effective adoption in practice. This article explores a vision of a holistic SPI2 design approach needed to develop next-generation automated design methods capable of rapidly producing viable SPI2 design candidates. We review several technical domains related to holistic SPI2 design, discuss existing knowledge gaps and practical challenges, examine exciting opportunities at the intersection of multiple domains that can enable comprehensive exploration of SPI2 design spaces, and present one viable two-stage SPI2 design automation framework. Holistic SPI2 design opens up a new direction of high industrial and societal relevance for the design research community. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have became appreciably compact and lightweight over the years, imposing stricter geometric and stability constraints. The new-generation UAVs are required to carry numerous sensors and peripheral components for a variety of tasks. These components are placed in accordance with the requirements for use and stability conditions, which complicates the determination of an optimal layout configuration. This article presents the design automation framework for determining favourable sensor placement locations under specific stability constraints. The proposed methodology is a unique end-to-end framework that determines convenient locations for sensor placement, considering the finite element model of UAVs and payload-related parameters as inputs. The design automation routine proposes an integrated approach using the support vector machine and extended pattern search algorithms to identify the design space and relevant cost function. The efficacy of the proposed framework is examined by leveraging finite element solvers to optimize payload placements on a simulated UAV model. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) technology improves engineering activities, enabling a better understanding of products and processes. A complete conversion from the existing CAx software to a VR environment could improve product and process design activities, leading to new practices. This article aims to provide key insights, technical evidences, and related impacts on the engineering activities of industrial companies while integrating the existing systems with VR as a supporting tool for product and process design. The results indicate improved design and limited negative impacts related to errors during the manufacturing phase. An action research study involving Oil&Gas manufacturing companies was conducted to explore the following four CAx-VR scenarios: CAD–VR for ergonomics/reachability, CAE(CFD)–VR and CAE(FEM)–VR for abstract data visualization, and CAM–VR for packaging. For each scenario, applicative cases focusing on Oil&Gas products, such as multistage centrifugal pumps and ball valves, have been investigated. Furthermore, a detailed discussion of the involved chain of software applications and impacts on the engineering activities for each scenario establishes the reliability of the integrated environment for CAD, CAE, and CAM data immersive visualization. [ABSTRACT FROM AUTHOR]

Power electronics converters operating at elevated temperature usually have degraded performance, such as reduced efficiency, lower noise immunity, and decreased system reliability, compared to those operating at room temperature. Nevertheless, harsh-environment applications, such as deep earth drilling, automotive, avionics, and space exploration, have been utilizing converters in high-temperature environments and are continuously pushing the temperature limits higher and higher with the advances in device technologies and converter design methodologies. This article starts with a literature study to identify the temperature dependence and the temperature limits of individual components. Then, published works about high-temperature converters are examined. Their selection of components is compared. Experimental results are analyzed to reflect on the efficacy of different component selections. Finally, since the latest published high-temperature converter was developed more than five years ago, a 1-kV input, 520-V output, 1-kW, $150$ °C-ambient-temperature-rated dc–dc converter is designed with state-of-the-art devices. Based on the literature review, and the test of the $150$ °C prototype, the main challenges of designing high-temperature power electronics converters are identified and concluded to be time-consuming, difficult, and expensive characterization of components, lack of accurate and computationally efficient models of converters, and optimizing component selection within short development time. [ABSTRACT FROM AUTHOR]

Microfluidics has shown great potential in cell analysis, where the flowing path in the microfluidic device is important for the final study results. However, the design process is time-consuming and labor-intensive. Therefore, we proposed an ANN method with three dense layers to analyze particle trajectories at the critical intersections and then put them together with the particle trajectories in straight channels. The results showed that the ANN prediction results are highly consistent with COMSOL simulation results, indicating the applicability of the proposed ANN method. In addition, this method not only shortened the simulation time but also lowered the computational expense, providing a useful tool for researchers who want to receive instant simulation results of particle trajectories. [ABSTRACT FROM AUTHOR]

Smart design and construction have emerged as pivotal forces in the construction industry. Numerous studies have been conducted in the realm of design optimization, using artificial intelligence data-driven approaches and optimization theories. This increase in research has sparked interest in generative design, a process that automatically generates algorithm-based design alternatives, thereby reducing human effort and time by a significant margin. The objective of this study was to explore the potential of generative design to boost productivity within architectural practices and reduce redundant and unnecessary tasks for an aging construction workforce. Specifically, it illustrates the process of selecting superior alternatives by generating various three-dimensional layouts, using a generative design methodology. This occurs during the creation of a building layout concept with subsequent partial evaluations. The methodology of this study was mainly divided into four stages: objective setting, design algorithm development, the establishment of evaluation methodology, and the comparison of the results’ values. The findings of this study confirmed that the design algorithm and evaluation methodology form a single loop, generating a multitude of design alternatives that satisfy the algorithm designer’s evaluation criteria.

Electric motor development is a challenging task, as higher efficiency requirements and various interdependencies between different engineering domains must be considered. Established design approaches often lack the ability to address these interdependencies because they focus on specific domains and properties. Automated, multidisciplinary design approaches hold untapped potential for optimizing motors in terms of diverse requirements and advancing the development of more efficient and reliable motors. This paper presents a systematic literature review of the current state of research in the multidisciplinary design automation of electric motors. The literature basis comprises 1005 publications that are identified by a systematic internet search. The review of the existing approaches is based on twelve criteria that characterize the design automation task in general, such as knowledge representation or reasoning methods used, as well as criteria specific to electric motor design, such as domains considered and their coupling. The analysis reveals what current approaches are lacking: Consequent analysis and integration of domains, applicability of suggested methods, incorporation of established multidisciplinary design optimization (MDO) architectures, alongside the consideration of passive components in the motor. Aside from the introduction of twelve criteria for systematic charaterization of multidisciplinary design automation of electric motors, this article expands the state of the art by proposing an initial framework to establish process chains tackling the identified gaps in the review.

The synchronous design paradigm dominates today’s semiconductor industry. However, this clocked approach is facing major challenges with today’s high-speed, low-power design expectations, using processes with ever-increasing physical level variability. Several clock related issues surface in designs operating at higher frequencies, which make clock management increasingly difficult. Quasi-delay insensitive (QDI) asynchronous (clockless) designs have proved to be effective in circumventing the major limiting factors associated with the clocked designs. NULL Convention Logic (NCL) is one such QDI asynchronous design paradigm, which presents itself as a promising alternative to conventional synchronous circuits and has already found numerous commercial applications due to its low power, robust architecture, and ease of design reuse. This paper presents the evolution of NCL based asynchronous paradigm over the past two decades, primarily focusing on existing fundamental research in NCL design automation, spanning over NCL synthesis, optimization, testing, and verification. The methods are systematically analyzed to determine their limitations and future research directions.