Improve Design Efficiency Research Articles

Intelligent Design Method and System of Tin Spraying Steel Mesh for Complex Combiner

ABSTRACT In the process of spraying tin into the combiner cavity, employing the Tin Spraying Steel Mesh (TSSM) is crucial. This operation is akin to covering a complex, maze-like area with multiple islands and branches. Manual operation is labor-intensive, time-consuming, and challenging to ensure high-quality results. Therefore, this paper presents an intelligent design method and system for the TSSM, based on graphic processing. Initially, isolated islands are used as nodes to establish the topology structure of complex paths. Then, an improved Delaunay triangulation method is utilized to extract intermediate paths for regions with branching paths, resulting in the division into multiple single path regions. Following this, the island regions undergo equal arc length segmentation, while the single path regions are subjected to random bidirectional segmentation to obtain the final TSSM. Finally, a TSSM intelligent segmentation system is developed by seamlessly integrating this method into the AutoCAD software platform using ObjectARX technology. The TSSM obtained by this method meets the quality requirements, demonstrating consistent segmentation direction and uniform gaps. Furthermore, the system can adjust segmentation parameters to cater to different combiner specifications, thereby significantly improving design efficiency and establishing itself as an indispensable auxiliary tool in the automated design of combiners.

Flow field reconstruction of compressor blade cascade based on deep learning methods

In the design process of compressors, calculating the S1 flow surface involves solving the Navier-Stokes equations, which results in slow convergence and makes determining cascade characteristics time-consuming. However, deep learning offers significant advantages in flow field reconstruction by not only automatically extracting complex flow features and reducing prediction time but also providing high accuracy in reconstruction. This paper implements the rapid reconstruction of the compressor S1 flow surface cascade flow field using two deep learning models: U-Net and 1D-CNN. Using a double-circular arc airfoil as an example, we selected four key design parameters that define the geometry and position of the airfoil, ultimately designing 5,292 sets of cascade geometries. By performing batch meshing and CFD simulations, we built a cascade flow field dataset. The U-Net neural network uses design parameters as input and outputs the aerodynamic distribution of the cascade flow field. After training, it can directly predict the flow field based on the design parameters. Since the U-Net model cannot directly obtain the aerodynamic parameter distribution and flow field aerodynamic coefficients on the airfoil surface, a 1D-CNN model is used as a complementary approach. The 1D-CNN model takes the design parameters as input and outputs the aerodynamic parameter distribution on the airfoil surface and the flow field aerodynamic coefficients. The prediction results show that the U-Net model achieves an average relative error of less than 1% in cascade flow field reconstruction, while the 1D-CNN model achieves an average relative error of less than 1% in predicting the pressure recovery coefficient and less than 2% in predicting the total pressure loss coefficient. This study presents a method for the rapid reconstruction of compressor blade cascade flow fields, which helps improve design efficiency and shorten the design cycle.

Aerodynamic optimization design and experimental verification of a high-load axial flow compressor

The performance and stable operating range of compressors are critical to the efficient operation of various turbomachinery systems. This paper proposes a multi-level optimization strategy combining the uni-uniform direct free deformation method, Linux partitioned CPU accelerated parallel computing technology, multi-objective particle swarm optimization algorithm and downhill simplex algorithm, which improves design efficiency. Numerical results show that after optimization design, the average value of peak efficiency and stall margin increases. The flow mechanism of compressor performance improvement after optimization lies in the reduction of a low-velocity separation zone in stator hub region. Moreover, the experiment study confirms the reliability and accuracy of the optimization design method and found that flow instability triggering mode, propagation characteristics of the stall cell, and surge frequency are changed after optimization design. Setting a probability distribution threshold for autocorrelation coefficient and cross-correlation coefficients can be used to predict the arrival of the surge condition.

Can Stylized Products Generated by AI Better Attract User Attention? Using Eye-Tracking Technology for Research

The emergence of AIGC has significantly improved design efficiency, enriched creativity, and promoted innovation in the design industry. However, whether the content generated from its own database meets the preferences of target users still needs to be determined through further testing. This study investigates the appeal of AI-generated stylized products to users, utilizing 12 images as stimuli in conjunction with eye-tracking technology. The stimulus is composed of top-selling gender-based stylized Bluetooth earphones from the Taobao shopping platform and the gender-based stylized earphones generated by the AIGC software GPT4.0, categorized into three experimental groups. An eye-tracking experiment was conducted in which 44 participants (22 males and 22 females, mean age = 21.75, SD = 2.45, range 18–27 years) observed three stimuli groups. The eye movements of the participants were measured while viewing product images. The results indicated that variations in stimuli category and gender caused differences in fixation durations and counts. When presenting a mix of the two types of earphones, the AIGC-generated earphones and earphones from the Taobao shopping platform, the two gender groups both showed a significant effect in fixation duration with F (2, 284) = 3.942, p = 0.020 < 0.05, and η = 0.164 for the female group and F (2, 302) = 8.824, p < 0.001, and η = 0.235 for the male group. They all had a longer fixation duration for the AI-generated earphones. When presenting exclusively the two types of AI-generated gender-based stylized earphones, there was also a significant effect in fixation duration with F (2, 579) = 4.866, p = 0.008 < 0.05, and η = 0.129. The earphones generated for females had a longer fixation duration. Analyzing this dataset from a gender perspective, there was no significant effect when the male participants observed the earphones, with F (2, 304) = 1.312 and p = 0.271, but there was a significant difference in fixation duration when the female participants observed the earphones (F (2, 272) = 4.666, p = 0.010 < 0.05, and η = 0.182). The female participants had a longer fixation duration towards the earphones that the AI generated for females.

Sustainable design: Circular innovation design method under process reengineering

The theory of sustainable business process reengineering has been widely applied in various disciplines and has achieved outstanding results. The theory is used to the research of product innovation design method in this work, and the entire process of product innovation design method is reshaped, bringing new development and new direction for product innovation design. Redesign processes in line with societal trends and in a sustainable direction. Furthermore, it may effectively address the inadequacies of conventional design methodologies during the application process. Then, increase corporate efficiency in order to accomplish significant improvements in enterprise cost, quality, service, and speed. The study applies the systematic induction method, research analysis method, and other comprehensive research methods to analyze and study the existing design process and method, as well as excavate the shortcomings and limitations that exist in the process's application of the existing design method. Based on the BPR theory, the sustainable innovation design method for full chain products is divided into five design processes: scenario implantation, requirement extraction, functional determination, structural optimization, formal innovation, and material selection. In addition, the feasibility and accuracy of this method are verified through lifesaving equipment cases. Secondly, three principles for sustainable product innovation design were proposed. On the one hand, products made by designers and college students can truly meet customer needs, generate disruptive innovative designs, and improve the market competitiveness of industrial products. On the other hand, design is no longer a single stage, but a complete, systematic, and comprehensive set of design processes aimed at improving design efficiency and achieving sustainable product development.

A systematic problem analysis network for product conceptual design

Problem analysis is a starting point of product development. The effective problem analysis can narrow the scope of potential problems and improve design efficiency. Although different methods have been proposed for the design problem analysis, the existing tools of the problem analysis are not systematic and effective. This paper presents a product design problem analysis and solution method based on problem networks. The proposed method conducts the problem decomposition, derivation and solving by considering the problem generation, exploratory and hypotheticals. Computer-aided analysis tools are integrated to improve reproducibility of the analysis solution and reduce the workload of designers. The method is applied in developing a new type of the charging gun cable with a granted patent. Feasibility and effectiveness of the proposed method are proved.

BIM – CONSTRUCTION LIFECYCLE

Engineering, construction, and architecture have all been impacted by building information modeling, or BIM. The use of BIM technology during a building's operational phase is only now beginning to gain popularity among building owners looking for creative ways to increase the effectiveness of their facility operations. Lifespan The process of creating, storing, and using building data to manage operations and maintenance of buildings over the course of their operational lives is known as building information modeling, or BIM. Many facets of building operations that gain from improved data are of interest to facility managers. The goal is for stakeholders to produce and utilize consistent digital information at every stage of the asset's lifecycle. BIM Lifecycle is the process of creating, utilizing and maintaining the building information. BIM technology has the potential to significantly increase the efficiency of project maintenance as well as building design, production, construction, operation, and maintenance. Specifically, information management throughout the manufacturing stage optimizes the production process, and collaborative design using BIM technology improves design efficiency. Its data management function increases the effectiveness of building operations and maintenance, and its 3D visualization tool lowers construction errors and encourages efficiency in the constructing phase.

Pattern search algorithm-aided structural color Sb2S3-based dynamic hybrid all-dielectric metasurface

The versatility and dynamic tunability of reconfigurable photonic devices hold paramount significance in optics. Nevertheless, most such dynamically adjustable all-dielectric metasurfaces, particularly those utilized in structural color applications, are usually resonators made exclusively of phase-change materials. The elevated absorption coefficients exhibited by phase-change materials in the short-wavelength band (particularly within the blue light range) often culminate in suboptimal performance for generating colors. To address this challenge, we designed a hybrid all-dielectric metasurface that achieved reflectivities of 60%, 75%, and 96% across blue, green, and red-light bands, respectively and achieved dynamic tunability of the metasurface by generating chromatic aberrations of 19.626, 3.341, and 17.354 via the reversible phase transition of Sb2S3. The device exhibits good angular robustness, maintaining nearly constant color within a viewing angle range of ±20°. Meanwhile, we employ a pattern search algorithm to accelerate the inverse design process of metasurfaces. The algorithm can reduce the design time by a factor of at least two orders of magnitude relative to the genetic algorithm, substantially improving design efficiency. The algorithm applies to high degree-of-freedom inverse design problems for intricate physical systems with similar design relationships. It offers a promising avenue for rapid and dependable optical device design.

Engineering information format utilisation across building design stages: An exploration of BIM applicability in China

Building Information Modelling (BIM) has been extensively advocated in the construction industry for its potential to improve design efficiency and quality. However, its application continues to encounter considerable resistance from designers because BIM is less efficient than conventional 2D and 3D data formats in some design tasks. Consequently, a flexible approach that integrates BIM into existing workflows, instead of fully replacing traditional data formats, is believed to better enhance BIM application. Given this concern, this study conducts a questionnaire survey among Chinese designers to investigate the engineering information formats used in practice and to assess the potential and applicability of this flexible approach. Analysis of the survey responses using relevance analysis, logistic regression, and multiple correspondence analysis reveals that BIM is more frequently used for coordinating multi-disciplinary data in the later stages of project development than in early design phases. Conversely, conventional 2D and 3D formats are preferred for creating and comparing design alternatives during the concept and developed design stages. A practical application for BIM emerges in the technical design stage of prefabricated construction projects. These results suggest a promising approach for BIM application: allowing the use of conventional data format in the concept and developed design stages, integrating these designs into a shared BIM model in the technical design stage, and maintaining them in the BIM model thereafter. Besides offering an in-depth analysis of engineering information format utilisation, this study offers practical strategies for contractors, policymakers, and BIM developers to extend BIM application and promotion.

Interior Scene Coloring Design Model Combining Improved K-means and SAA

Due to technological progress and changes in people’s aesthetic standards, traditional design models need to be constantly broken through, seeking more efficient and accurate design methods. Seeking effective design models to improve design efficiency and prediction accuracy is an important task. Therefore, this study proposes an indoor scene coloring design model that combines improved K-means clustering and simulated annealing algorithm for this important task. Based on the analysis of indoor scene coloring, particle swarm optimization algorithm is used to optimize K-means clustering to achieve color classification. Combined with simulated annealing algorithm, adaptive adjustment of lighting conditions is achieved to enhance the naturalness and realism of coloring. These results confirmed that the proposed method had the highest average F-value, with an average F-value of 92.524 and 143.601 on both datasets, respectively. The average ARI values were 0.361 and 0.897, respectively. The designed algorithm performed the best and converged faster than other three. Therefore, the proposed method can effectively ensure the consistency between the distribution of data objects after clustering and the actual situation. For indoor scene coloring design, it has important practical significance and provides new possible paths for improving design efficiency and prediction accuracy.

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.

A closed-loop human-computer interactive design method based on sequential human intention prediction and knowledge recommendation

Designers working on large-scale complex engineering projects may encounter design errors due to limited experience and expertise. Therefore, during the design process, accurately identifying the designer’s design intention and recommending relevant design knowledge can enhance design efficiency and reduce errors. In this paper, we propose a closed-loop human–computer interactive design method based on sequential human intention prediction and knowledge recommendation. This method leverages the Function-Behaviour-Structure (FBS) model to reduce the dimensionality of design action sequences, so as to facilitate the analysis of potential design patterns. The processed action sequences are used to train Transformer to predict design intentions. In different input sequences, Transformer achieved a highest prediction accuracy of 92.09%. We construct a Design Knowledge Recommendation Framework (DKRF) and its corresponding design knowledge matching algorithm. This framework accurately recommends design knowledge to designers, solving design stagnation and improving design efficiency. Finally, a case study of four types of mechanical model design is conducted to demonstrate the feasibility and wide applicability of the proposed closed-loop human–computer interactive design method.

A top-down lashing bridge collaborative design method

ABSTRACT The short detailed design cycle of lashing bridges on container ships needs to ensure that various designs have different requirements at multiple locations on the ship, making the design methodology for complex products challenging. Currently, research on the design methodology of lashing bridges lacks in-depth investigation into detailed design aspects. This work addressed research gaps through a top-down collaborative design approach and analysed the principles of propagating design changes in computer-aided design (CAD), as well as proposed a design methodology consisting of four core steps, namely, skeleton decomposition, feature aggregation, detailed design and product structure (PS) expansion. The effectiveness of this methodology was validated through practical design examples and specific variant design instances of a certain type of container ship. The validation demonstrated that the proposed design methodology, in contrast to traditional three-dimensional design methods, supported design process reuse and significantly improved design efficiency in complex product designs.

Experimental investigation and simulation analysis of cast-steel joints under vertical pressure

The joint made of cast steel is frequently utilized within a treelike column structure to ensure a smooth transition. It is of great significance in ensuring the overall structural safety, but currently, the mechanical property and bearing capacity of this type of joint cannot be fully understood. This study investigates the load characteristics of three-forked cast steel joints through concrete experiments, finite element analysis, and regression method formula derivation, filling the gap in mechanical properties and calculation formulas of forked cast steel joints. Initially, a comprehensive model of the cast-steel joint, sourced from a practical engineering, underwent vertical load testing. Detailed scrutiny of stress distribution and vertical displacement of the tested joint was conducted based on the experimental outcomes. Subsequently, a finite element model of the tested joint was constructed using SolidWorks and subjected to analysis via ANSYS. The numerical findings were juxtaposed with experimental data and extrapolated to encompass other parametric scenarios. Ultimately, a regression analysis method was employed to derive a calculation formula for the load-carrying capacity of branch-bearing cast-steel joints. The regression analysis method can accurately obtain the load-bearing capacity calculation formula for tree-shaped joint models and can be extended to determine corresponding branch and main pipe dimensions, as well as the deviation angle between branches and the main pipe, under known load conditions. This improves design efficiency and accuracy. Comparative analysis reveals a substantial concurrence among experimental, finite element analysis, and formula-based predictive outcomes. The maximum error between experimental results and those obtained from finite element analysis is 9.02%. The maximum error between the results calculated using the load-bearing capacity formula derived from regression methods and those from finite element analysis is only 1.9%.

A new approach to derive variation shares by combining the C&C² approach and the PGE model

AbstractThis paper introduces a method to derive variation shares in engineering design, merging the Contact & Channel Approach (C&C²) with the model of PGE - Product Generation Engineering. It focuses on one-piece parts, enhancing precision in identifying component variations. The integration allows for detailed qualitative modeling and subcomponent analysis, improving design efficiency and innovation, illustrated with bottle examples. This research advances engineering design by enabling more accurate categorization of system variations.

A forward design approach for fault thresholds in redundant flight control systems

Modern fly-by-wire flight control systems use redundancy configurations to meet high safety requirements. The fault threshold value of the redundancy monitoring and voting algorithm affects the false alarm rate and missing alarm rate of the flight control system and the excessive false alarm rate and missing alarm rate reduce the reliability of the flight control system. Traditionally, the design of fault threshold is based on the empirical trial-and-error method, which suffers from the problem of unclear optimization direction. Forward design is needed to improve design efficiency and quality. In this paper, a forward design method for fault detection threshold is proposed to determine the upper limit value of the fault threshold by analyzing the effect of the voting signal change on the transient response of the aircraft when a channel fault occurs. The lower limit value of the fault threshold is determined by analyzing the channel history data. The effectiveness of the design method is demonstrated by digital simulation.

Inspiration from hyperbolic paraboloid folding for clothing design

PurposeThis study aims to increase the novelty of clothing design and fabric texture. The element library that can be used for design is systematically summarized. The element database can also be continuously filled according to the existing logic to realize the diversity of design. Improve the theory of fashion design, expand the designer's design ideas and improve design efficiency. Clear design steps and logic can help students and machines learn the design process and promote the development of intelligent design. And verify the feasibility of the simulation software to assist pleated clothing design.Design/methodology/approachFirstly, according to the logical framework of origami theory, different innovative designs and combined designs are made for the basic units of hyperbolic paraboloid, and the element library that can be used for design is systematically summarized. This database can also be continuously filled according to the existing logic to realize the diversity of design. Secondly, it summarizes three methods of pleated element filling clothing – uniform filling method, the irregular filling method and geometric addition method – that improve the theory of fashion design, expand the designer's design ideas and improve design efficiency. Clear design steps and logic can help students and machines learn the design process and promote the development of intelligent design. Finally, the virtual software is used to simulate the effect of pleated clothing, and the three-dimensional simulation software 3dclo is used to make an empirical study on the application of hyperbolic paraboloid origami in clothing pleated design to verify the feasibility of the simulation software to assist pleated clothing design.FindingsThe theoretical results of hyperbolic paraboloid origami are collected and arranged to establish the element library of hyperbolic paraboloid origami. The results expand the designer's design ideas and auxiliary design technology and improve the design efficiency using a sample of hyperbolic paraboloid fabric to verify its practicability and three-dimensional clothing simulation software for exploring the design. The design rules of hyperbolic paraboloid clothing and the realization method of fabric are summarized, including the expansion and combing of elements, the application of size and shape and the method of combination.Research limitations/implicationsOwing to the hyperbolic paraboloid origami’s length shrinkage, the loose computation of clothing requires targeted computation. This paper solely applies a paper model for estimating the shrinkage, and then we tend to subsequently explore the way to precisely compute the porosity, to determine the existing differences in the two-dimensional shrinkage of hyperbolic paraboloid creases of varying materials and to know if the clothing after large-scale production is capable of reaching the anticipated value.Practical implicationsThe exploration of this experiment brings a new 3D experiment process to the design process.Social implicationsThis experiment brings new possibilities for the development of virtual fitting and virtual display in the industry.Originality/valueThis study combines hyperbolic paraboloid origami and clothing and combs and expands the unit with logical thinking to expand the designer's design ideas.

Application of AIGC in the design of film and television props

This paper discusses the application, influence and long-term value of AIGC in film and television props design. Through deep learning and intelligent algorithms, AIGC technology can quickly generate high-quality film and television prop design schemes, improve design efficiency and accuracy, and save time and labor costs for film and television production. At the same time, AIGC technology can also promote the innovation of film and television props design, stimulate the creative inspiration of designers, and create more diverse design styles. In addition, AIGC technology can intelligently recommend and optimize the design scheme according to the audience 's preferences and needs, enhance the audience 's viewing experience, and enhance the attractiveness of film and television works. All in all, AIGC has important application value in the design of film and television props, and has injected new impetus into the innovation and development of the film and television industry. With the continuous progress of technology and the continuous improvement of application mode, it is believed that the application of AIGC in the design of film and television props will play a more important role and promote the innovation and development of the industry.

Optimizing Space Telescopes’ Thermal Performance through Uncertainty Analysis: Identification of Critical Parameters and Shaping Test Strategy Development

The integration of uncertainty analysis methodologies allows for improving design efficiency, particularly in the context of instruments that demand precise pointing accuracy, such as space telescopes. Focusing on the VINIS Earth observation telescope developed by the Instituto de Astrofísica de Canarias (IAC), this paper reports an uncertainty analysis on a thermal model aimed at improving cost savings in the future testing phases. The primary objective was to identify critical parameters impacting thermal performance and reduce overdesign. Employing the Statistical Error Analysis (SEA) method across several operational scenarios, the research identifies key factors, including the Earth’s infrared temperature and albedo, and the spacecraft’s attitude and environmental conditions, as the variables with major influences on the system’s thermal performance. Ultimately, the findings suggest that uncertainty-based analysis is a potent tool for guiding thermal control system design in space platforms, promoting efficiency and reliability. This methodology not only provides a framework for optimizing thermal design and testing in space missions but also ensures that instruments like the VINIS telescope maintain optimal operating temperatures in diverse space environments, thereby increasing mission robustness and enabling precise resource allocation.

Interactive design generation and optimization from generative adversarial networks in spatial computing

This paper focuses on exploring the application possibilities and optimization problems of Generative Adversarial Networks (GANs) in spatial computing to improve design efficiency and creativity and achieve a more intelligent design process. A method for icon generation is proposed, and a basic architecture for icon generation is constructed. A system with generation and optimization capabilities is constructed to meet various requirements in spatial design by introducing the concept of interactive design and the characteristics of requirement conditions. Next, the generated icons can effectively maintain diversity and innovation while meeting the conditional features by integrating multi-feature recognition modules into the discriminator and optimizing the structure of conditional features. The experiment uses publicly available icon datasets, including LLD-Icon and Icons-50. The icon shape generated by the model proposed here is more prominent, and the color of colored icons can be more finely controlled. The Inception Score (IS) values under different models are compared, and it is found that the IS value of the proposed model is 7.05, which is higher than that of other GAN models. The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. After introducing multi-feature recognition modules into the network model, the peak error of the recognition network is only 2.000 in the initial stage, while the initial error of the ordinary GAN without multi-feature recognition modules is as high as 5.000. It indicates that the improved model effectively helps the discriminative network recognize the core information of icon images more quickly. The research results provide a reference basis for achieving more efficient and innovative interactive space design.