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Identifying Brand Consistency by Product Differentiation Using CNN

Computer Modeling in Engineering & Sciences 2024年第7期140卷 685-709页

作者： Hung-Hsiang Wang Chih-Ping Chen department of industrial design National Taipei University of TechnologyTaipeiTaiwan department of Product design Ming Chuan UniversityTaoyuanTaiwan

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

关键词： Machine learning product differentiation brand consistency principal component analysis convolutional neural network computer mouse

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Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

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Journal of Magnesium and Alloys 2024年第5期12卷 1931-1943页

作者： Farid Bahari-Sambran Fernando Carre?o C.M.Cepeda-Jiménez Alberto Orozco-Caballero Physical Metallurgy department CENIM-CSIC department of Mechanical Engineering Chemistry and Industrial Design Polytechnic University of Madrid

The aim of this work is to predict, for the first time, the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models, random forest（RF） and artificial neural network（ANN）. With that purpose, a ZK30 magnesium alloy was friction stir processed（FSP） using three different severe conditions to obtain fine grain microstructures（with average grain sizes between 2 and 3 μm） prone to extensive superplastic response. The three friction stir processed samples clearly deformed by grain boundary sliding（GBS） deformation mechanism at high temperatures. The maximum elongations to failure, well over 400% at high strain rate of 10-2s-1, were reached at 400 ℃ in the material with coarsest grain size of 2.8 μm, and at 300 ℃ for the finest grain size of 2 μm. Nevertheless, the superplastic response decreased at 350 ℃ and 400 ℃ due to thermal instabilities and grain coarsening, which makes it difficult to assess the operative deformation mechanism at such temperatures. This work highlights that the machine learning models considered, especially the ANN model with higher accuracy in predicting flow stress values, allow determining adequately the superplastic creep behavior including other possible grain size scenarios.

关键词： Machine learning Artificial intelligence Magnesium alloys Superplasticity Friction stir processing Grain coarsening

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A study of the effect of local scour on the flow field near the spur dike

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Theoretical & Applied Mechanics Letters 2024年第2期14卷 98-105页

作者： Yu-Tian Li Jie-Min Zhan Wing-Hong Onyx Wai department of industrial design School of Industrial Design and Ceramic ArtFoshan UniversityFoshan 528000China department of Applied Mechanics and Engineering Shenzhen Campus of Sun Yat-sen UniversityShenzhen 518107China department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China

The flow field near a spur dike such as down flow and horseshoe vortex system(HVS)are susceptible to the topographic changes in the local scouring process,resulting in variation of the sediment transport with time.In this study,large eddy simulations with fixed-bed at different scouring stages were conducted to investigate the changes in flow field.The results imply that the bed deformation leads to an increase in flow rate per unit area,which represent the capability of sediment transportation by water,in the scour hole.Moreover,the intensity of turbulent kinetic energy and bimodal motion near the sand bed induced by the HVS were also varied.However,the peak moments between the two sediment transport mechanisms were different.Hence,understanding the complex feedback mechanism between topography and flow field is essential for the local scour problem.

关键词： Spur dike Local scour Large eddy simulation Down flow Horseshoe vortex system

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DEM investigation of strength and critical state behaviours of granular materials under true triaxial loadings

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Particuology 2024年第2期85卷 198-212页

作者： Minyi Zhu Guobin Gong Jun Xia Stephen Wilkinson department of Civil Engineering Xi'an Jiaotong-Liverpool UniversityChina department of Civil Engineering and industrial design University of LiverpoolLiverpoolUK Faculty of Engineering and Information Sciences University of Wollongong in DubaiUnited Arab Emirates

This paper investigates the strength and critical state(CS)behaviours of granular materials via DEM simulations of true triaxial drained tests under three different loading modes including constant b(in-termediate stress ratio)tests with constantσ'1(major principal stress),constant p(mean pressure)and constantσ'3(minor principal stress)respectively.To this end,a series of samples are generated with the same particle size distribution,and with the confining stresses ranging from 100 kPa to 900 kPa.The CS is achieved for all samples.Both the macroscopic behaviours and the microscopic behaviours are examined and compared considering different loading modes,confining stresses and intermediate stress ratios(b).The critical state lines(CSLs)are found to be unique and independent of the loading modes,but dependent on the b values.The CSLs with b=0 and b=1 form the two boundaries of CSLs respectively beyond which CSLs under all other b tests cannot go beyond.Six different strength criteria are examined and compared in terms of both peak and CS failures.The Mohr-Coulomb strength criterion is found to be only suitable for axisymmetric loading conditions.The Lade-Duncan criterion is only suitable for describing peak strengths,which is dependent on the loading modes and confining stresses.The Satake criterion and Matsuoka-Nakai criterion are the more appropriate strength criteria for describing CS failures,indicating that the CS values of both the Satake parameter and the Matsuoka-Nakai parameter describe an inherent property that characterizes the CS failure for a given type of soil.The CS mechanical coordination number is fitted by a curved line for a given b value,which is unique regardless of the loading modes.The peak and CS values of both major and minor principal fabric tensors decrease with increasing b values,while they increase with increasing b values for the intermediate principal fabric tensor.

关键词： Discrete element method Intermediate stress ratio Dilation Critical state Failure criterion

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Combining Deep Learning with Knowledge Graph for design Knowledge Acquisition in Conceptual Product design

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工程与科学中的计算机建模(英文) 2024年第1期138卷 167-200页

作者： Yuexin Huang Suihuai Yu Jianjie Chu Zhaojing Su Yangfan Cong Hanyu Wang Hao Fan Key Laboratory of industrial design and Ergonomics Ministry of Industry and Information TechnologyNorthwestern Polytechnical UniversityXi’an710072China School of industrial design Engineering Delft University of TechnologyDelft2628 CEThe Netherlands department of industrial design College of ArtsShandong University of Science and TechnologyTsingtao266590China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

关键词： Conceptual product design design knowledge acquisition knowledge graph entity extraction relation extraction

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Restimate:Recovery Estimation Tool for Resilience Planning

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Journal of Safety Science and Resilience 2024年第1期5卷 47-63页

作者： Scott Miles Megan Ly Nick Terry Youngjun Choe department of Human Centered design&Engineering Campus Box 352315University of WashingtonSeattleWA 98195United States of America department of industrial&Systems Engineering Box 352650University of WashingtonSeattleWA 98195United States of America

The U.S.National Institute of Standards and Technology(NIST)published the Community Resilience Planning Guide in 2016.The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction’s resilience against a scenario hazard.The framework centers around tables of expected and desired recovery times for selected community assets,such as electricity,water,and natural gas infrastructures.The NIST Guide does not provide a method for estimating the expected recovery times.However,building high-fidelity computer models for such estimations requires substantial resources that even larger ju-risdictions cannot cost-justify.The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the(knowledgeable)crowd.This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data.This work is the first in the literature to examine people’s ability to estimate recovery curves and how design in-fluences such estimations.Its main contribution to resilience planning is three-fold:development of a new elicitation tool called Restimate,understanding its potential user base,and providing insights into how it can facilitate resilience planning.Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning.Beyond resilience planning,those who facilitate high-stakes planning activities under large uncertainties(e.g.,mission-critical system design and planning)will benefit from a similar research-through-design process.

关键词： expert elicitation disaster natural hazard infrastructure community resilience restoration user-centered design computer-supported cooperative work

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Evaluating innovation ecosystem resiliency using agent-based modeling and systems dynamics

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Journal of Safety Science and Resilience 2024年第2期5卷 204-221页

作者： Soheila Abdi Mehdi Yazdani Esmaeil Najafi department of industrial Engineering Science and Research BranchIslamic Azad UniversityTehranIran department of industrial Engineering Qazvin BranchIslamic Azad UniversityQazvinIran

Evaluating the resilience of the innovation ecosystem to maintain its performance,in the sense of resistance to disruption and recovery after it,has recently received more attention.Several studies have been conducted to model different ecosystems and evaluate their resilience.However,modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention.This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps.In the first step,a case study related to the innovation ecosystem of Iran's Ministry of Energy,called the Power Innovation Ecosystem,is modeled by combining system dynamics and agent-based modeling.Upon validating the model in the second step,the disruption of the loss of experts is investigated in the third step,and all possible actions to recover each actor are analyzed.In the fourth step,the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps.Finally,resilience is calculated in two different ways in the fifth step.Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level.This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model.By applying strategic changes to this model,they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem.

关键词： Resilience index innovation ecosystem System dynamics Agent-based modeling

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A stable implicit nodal integration-based particle finite element method(N-PFEM)for modelling saturated soil dynamics

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第6期16卷 2172-2183页

作者： Liang Wang Xue Zhang Jingjing Meng Qinghua Lei department of Earth Sciences ETH ZürichZürich8092Switzerland department of Civil Engineering and industrial design University of LiverpoolLiverpoolL693BXUK department of Civil Environmental and Natural Resources EngineeringLuleåUniversity of TechnologyLuleå97187Sweden department of Earth Sciences Uppsala UniversityUppsala75105Sweden

In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.

关键词： Particle finite element method Nodal integration Dynamic saturated media Second-order cone programming(SOCP)

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Stimulated Raman scattering tomography for rapid three-dimensional chemical imaging of cells and tissue

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Advanced Photonics 2024年第2期6卷 20-31页

作者： Weiqi Wang Zhiwei Huang National University of Singapore College of Design and EngineeringOptical Bioimaging LaboratoryDepartment of Biomedical EngineeringSingapore

Three-dimensional(3D)imaging is essential for understanding intricate biological and biomedical systems,yet live cell and tissue imaging applications still face challenges due to constrained imaging speed and strong scattering in turbid media.Here,we present a unique phase-modulated stimulated Raman scattering tomography(PM-SRST)technique to achieve rapid label-free 3D chemical imaging in cells and tissue.To accomplish PM-SRST,we utilize a spatial light modulator to electronically manipulate the focused Stokes beam along the needle Bessel pump beam for SRS tomography without the need for mechanical z scanning.We demonstrate the rapid 3D imaging capability of PM-SRST by real-time monitoring of 3D Brownian motion of polystyrene beads in water with 8.5 Hz volume rate,as well as the instant biochemical responses to acetic acid stimulants in MCF-7 cells.Further,combining the Bessel pump beam with a longer wavelength Stokes beam(NIR-II window)provides a superior scattering resilient ability in PM-SRST,enabling rapid tomography in deeper tissue areas.The PM-SRST technique providestwofold enhancement in imaging depth in highly scattering media(e.g.,polymer beads phantom and biotissue like porcine skin and brain tissue)compared with conventional point-scan SRS.We also demonstrate the rapid 3D imaging ability of PM-SRST by observing the dynamic diffusion and uptake processes of deuterium oxide molecules into plant roots.The rapid PM-SRST developed can be used to facilitate label-free 3D chemical imaging of metabolic activities and functional dynamic processes of drug delivery and therapeutics in live cells and tissue.

关键词： stimulated Raman scattering tomography deep tissue Raman imaging spatial light modulation

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On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

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Journal of Magnesium and Alloys 2024年第4期12卷 1608-1618页

作者： Shailendra Pawanr Kapil Gupta department of Mechanical and industrial Engineering Technology University of JohannesburgDoornfontein CampusJohannesburg 2028 South Africa

Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

关键词： Magnesium alloy Dry machining Textured tools Flank wear Sustainability

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