-
Evaluating large-language-model chatbots to engage communities in large-scale design projects AI EDAM (IF 2.1) Pub Date : 2024-03-18 Jonathan Dortheimer, Nik Martelaro, Aaron Sprecher, Gerhard Schubert
Recent advances in machine learning have enabled computers to converse with humans meaningfully. In this study, we propose using this technology to facilitate design conversations in large-scale urban development projects by creating chatbot systems that can automate and streamline information exchange between stakeholders and designers. To this end, we developed and evaluated a proof-of-concept chatbot
-
Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems AI EDAM (IF 2.1) Pub Date : 2024-01-18 Xiaoxu Diao, Yunfei Zhao, Pavan K. Vaddi, Michael Pietrykowski, Marat Khafizov, Carol Smidts
Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example
-
Optimal configurations of Minimally Intelligent additive manufacturing machines for Makerspace production environments AI EDAM (IF 2.1) Pub Date : 2024-01-17 James Gopsill, Mark Goudswaard, Chris Snider, Lorenzo Giunta, Ben Hicks
Additive manufacturing (AM) has transformed job shop production and catalysed the growth of Makerspaces, FabLabs, Hackspaces, and Repair Cafés. AM has enabled the handling and manufacturing of a wide variety of components, and its accessibility has enabled more individuals to make. While smaller than their production-scale counterparts, the objectives of minimizing technician overhead, capital expenditure
-
A one-step calibration method without redundant parameters for a laser stripe sensor AI EDAM (IF 2.1) Pub Date : 2024-01-08 Yang Mao, Yu He, Chengyi Yu, Honghui Zhang, Ke Zhang, Xiaojun Sun
A laser stripe sensor has two kinds of calibration methods. One is based on the homography model between the laser stripe plane and the image plane, which is called the one-step calibration method. The other is based on the simple triangular method, which is named as the two-step calibration method. However, the geometrical meaning of each element in the one-step calibration method is not clear as
-
Automatic weld joint type recognition in intelligent welding using image features and machine learning algorithms AI EDAM (IF 2.1) Pub Date : 2024-01-02 Satish Sonwane, Shital Chiddarwar
Welding is the most basic and widely used manufacturing process. Intelligent robotic welding is an area that has received much consideration owing to the widespread use of robots in welding operations. With the dawn of Industry 4.0, machine learning is substantially developing to alleviate issues around applying robotic welding intelligently. Identifying the correct weld joint type is essential for
-
A user model to directly compare two unmodified interfaces: a study of including errors and error corrections in a cognitive user model AI EDAM (IF 2.1) Pub Date : 2024-01-02 Farnaz Tehranchi, Amirreza Bagherzadeh, Frank E. Ritter
User models that can directly use and learn how to do tasks with unmodified interfaces would be helpful in system design to compare task knowledge and times between interfaces. Including user errors can be helpful because users will always make mistakes and generate errors. We compare three user models: an existing validated model that simulates users’ behavior in the Dismal spreadsheet in Emacs, a
-
Mapping artificial intelligence-based methods to engineering design stages: a focused literature review AI EDAM (IF 2.1) Pub Date : 2023-12-12 Pranav Milind Khanolkar, Ademir Vrolijk, Alison Olechowski
Engineering design has proven to be a rich context for applying artificial intelligence (AI) methods, but a categorization of such methods applied in AI-based design research works seems to be lacking. This paper presents a focused literature review of AI-based methods mapped to the different stages of the engineering design process and describes how these methods assist the design process. We surveyed
-
A semantic similarity-based method to support the conversion from EXPRESS to OWL AI EDAM (IF 2.1) Pub Date : 2023-11-03 Yan Liu, Qingquan Jian, Claudia M. Eckert
Product data sharing is fundamental for collaborative product design and development. Although the STandard for Exchange of Product model data (STEP) enables this by providing a unified data definition and description, it lacks the ability to provide a more semantically enriched product data model. Many researchers suggest converting STEP models to ontology models and propose rules for mapping EXPRESS
-
Does empathy lead to creativity? A simulation-based investigation on the role of team trait empathy on nominal group concept generation and early concept screening AI EDAM (IF 2.1) Pub Date : 2023-09-04 Mohammad Alsager Alzayed, Scarlett Miller, Jessica Menold, Jacquelyn Huff, Christopher McComb
Research on empathy has been surging in popularity in the engineering design community since empathy is known to help designers develop a deeper understanding of the users’ needs. Because of this, the design community has become more invested in devising and assessing empathic design activities. However, research on empathy has been primarily limited to individuals, meaning we do not know how it impacts
-
A knowledge-enabled approach for user experience-driven product improvement at the conceptual design stage AI EDAM (IF 2.1) Pub Date : 2023-08-17 Jun Li, Xin Guo, Kai Zhang, Wu Zhao
Improving existing products plays a vital role in enhancing customer satisfaction and coping with changes in the market. Analyzing user experience (UX) to find the deficiencies of existing products and establishing improved schemes is the key to UX-driven product improvement, especially at the conceptual design stage. Although some tools used in conceptual design, such as requirements analysis and
-
Free-text inspiration search for systematic bio-inspiration support of engineering design AI EDAM (IF 2.1) Pub Date : 2023-08-14 Mart Willocx, Joost R. Duflou
Current supportive bio-inspired design methods focus on handcrafting the inspiration engineers use to speed up bio-inspired design. However promising, such methods are not scalable as the time investment is shifted to an up-front investment. Furthermore, most proposed methods require the engineer to adopt a new design process. The current study presents FISh, a scalable search method based on the standard
-
Tool life prediction via SMB-enabled monitor based on BPNN coupling algorithms for sustainable manufacturing AI EDAM (IF 2.1) Pub Date : 2023-07-03 Wen-Yang Chang, Bo-Yao Hsu
The predictive methods of tool wear are usually based on different algorithm predictors, different source data, and different sensing devices for remaining useful life (RUL). In general, it has challenges to model and ensure all of the cutting conditions that are suitable in the actual cutting process for sustainable manufacturing. In order to overcome the doing large amount of experimental data and
-
A comparative review on the role of stimuli in idea generation AI EDAM (IF 2.1) Pub Date : 2023-06-24 Graziana Blandino, Francesca Montagna, Marco Cantamessa, Samuele Colombo
This paper reports a systematic literature review with the aim of determining the role of stimuli and other factors, such as timing, the designers’ background, expertise, and experience, in the idea generation phase of conceptual design related to engineering and industrial design and architecture. “Stimulus” is a general expression for a source of information characterized by several features, including
-
Visualizing design project team and individual progress using NLP: a comparison between latent semantic analysis and Word2Vector algorithms AI EDAM (IF 2.1) Pub Date : 2023-06-14 Matt Chiu, Siska Lim, Arlindo Silva
Design has always been seen as an inherently human activity and hard to automate. It requires a lot of traits that are seldom attributable to machines or algorithms. Consequently, the act of designing is also hard to assess. In particular in an educational context, the assessment of progress of design tasks performed by individuals or teams is difficult, and often only the outcome of the task is assessed
-
Exploring the impact of set-based concurrent engineering through multi-agent system simulation AI EDAM (IF 2.1) Pub Date : 2023-06-13 Sean Rismiller, Jonathan Cagan, Christopher McComb
Set-based concurrent engineering (SBCE), a process that develops sets of many design candidates for each subproblem throughout a design project, proposes several benefits compared to point-based processes, where only one design candidate for each subproblem is chosen for further development. These benefits include reduced rework, improved design quality, and retention of knowledge to use in future
-
Stone masonry design automation via reinforcement learning AI EDAM (IF 2.1) Pub Date : 2023-06-13 SungKu Kang, Jennifer G. Dy, Michael B. Kane
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
-
Prediction of the onset of shear localization based on machine learning AI EDAM (IF 2.1) Pub Date : 2023-06-08 Samet Akar, Ece Aylı, Oğuzhan Ulucak, Doruk Uğurer
Predicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial
-
Measuring ideation effectiveness in bioinspired design AI EDAM (IF 2.1) Pub Date : 2023-04-28 Sunil Sharma, Suraj Gururani, Prabir Sarkar
Analogies provide better concept generation in engineering design. This ideation can be measured by metrics such as usefulness, novelty, variety, quality, completeness, and quantity. In bioinspired design, biological analogies are used to inspire design concepts. Biological analogies have been provided in earlier studies to measure ideation effectiveness. Tools like IDEA-INSPIRE, DANE, etc., allow
-
Towards the conceptual design of ML-enhanced products: the UX value framework and the CoMLUX design process AI EDAM (IF 2.1) Pub Date : 2023-03-30 Lingyun Sun, Zhuoshu Li, Zhibin Zhou, Shanghua Lou, Wenan Li, Yuyang Zhang
With the increasing utilization of machine learning (ML) to enhance products’ capabilities, the design research community has begun to explore how to support the conceptual design of ML-enhanced products. However, UX value creation of ML-enhanced products is still challenging because of ML's unique characteristics and numerous complex factors in conceptual design. To help designers create UX value
-
An EEG-based method to decode cognitive factors in creative processes AI EDAM (IF 2.1) Pub Date : 2023-03-27 Yuan Yin, Haoyu Zuo, Peter R. N. Childs
Neurotechnology has been applied to gain insights on creativity-related cognitive factors. Prior research has identified relations between cognitive factors and creativity qualitatively; while quantitative relations, such as the relative importance of cognitive factors and creativity, have not been fully determined. Therefore, taking the creative design process as an example, this study using electroencephalography
-
A study of the evaluation metrics for generative images containing combinational creativity AI EDAM (IF 2.1) Pub Date : 2023-03-23 Boheng Wang, Yunhuai Zhu, Liuqing Chen, Jingcheng Liu, Lingyun Sun, Peter Childs
In the field of content generation by machine, the state-of-the-art text-to-image model, DALL⋅E, has advanced and diverse capacities for the combinational image generation with specific textual prompts. The images generated by DALL⋅E seem to exhibit an appreciable level of combinational creativity close to that of humans in terms of visualizing a combinational idea. Although there are several common
-
Evaluating the feeling of control in virtual object translation on 2D interfaces AI EDAM (IF 2.1) Pub Date : 2023-03-02 Wenxin Sun, Mengjie Huang, Chenxin Wu, Rui Yang, Ji Han, Yong Yue
Computer-aided design (CAD) plays an essential role in creative idea generation on 2D screens during the design process. In most CAD scenarios, virtual object translation is an essential operation, and it is commonly used when designers simulate their innovative solutions. The degrees of freedom (DoF) of virtual object translation modes have been found to directly impact users’ task performance and
-
Understanding the effects of hand design on embodiment in virtual reality AI EDAM (IF 2.1) Pub Date : 2023-03-02 Jingjing Zhang, Mengjie Huang, Rui Yang, Yiqi Wang, Xiaohang Tang, Ji Han, Hai-Ning Liang
Understanding user perceptions of interacting with the virtual world is one of the research focuses in recent years, given the rapid proliferation of virtual reality (VR) and driven to establish the metaverse. Users can generate a familiar connection between their bodies and the virtual world by being embodied in virtual hands, and hand representations can induce users’ embodiment in VR. The sense
-
Uncovering hidden patterns of design ideation using hidden Markov modeling and neuroimaging AI EDAM (IF 2.1) Pub Date : 2023-02-27 Mo Hu, Christopher McComb, Kosa Goucher-Lambert
The study presented in this paper applies hidden Markov modeling (HMM) to uncover the recurring patterns within a neural activation dataset collected while designers engaged in a design concept generation task. HMM uses a probabilistic approach that describes data (here, fMRI neuroimaging data) as a dynamic sequence of discrete states. Without prior assumptions on the fMRI data's temporal and spatial
-
Graph models for engineering design: Model encoding, and fidelity evaluation based on dataset and other sources of knowledge AI EDAM (IF 2.1) Pub Date : 2023-02-20 Eric Coatanéa, Hari Nagarajan, Hossein Mokhtarian, Di Wu, Suraj Panicker, Andrés Morales-Forero, Samuel Bassetto
Automatically extracting knowledge from small datasets with a valid causal ordering is a challenge for current state-of-the-art methods in machine learning. Extracting other type of knowledge is important but challenging for multiple engineering fields where data are scarce and difficult to collect. This research aims to address this problem by presenting a machine learning-based modeling framework
-
Graph transformation in engineering design: an overview of the last decade AI EDAM (IF 2.1) Pub Date : 2023-02-02 Christopher Voss, Frank Petzold, Stephan Rudolph
In engineering and architecture, different approaches have been developed that share the use of graph transformation to automate design processes or to search for design solutions by means of computational design synthesis. In order to give an overview of these approaches, we provide a review of articles published in the last decade. Forty-eight articles were reviewed to determine similarities and
-
Optimal robotic assembly sequence planning with tool integrated assembly interference matrix AI EDAM (IF 2.1) Pub Date : 2023-01-18 Chiranjibi Champatiray, M. V. A. Raju Bahubalendruni, Rabindra Narayan Mahapatra, Debasisha Mishra
Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible
-
Multidisciplinary concurrent optimization framework for multi-phase building design process AI EDAM (IF 2.1) Pub Date : 2023-01-13 Naveen Kumar Muthumanickam, Jose Pinto Duarte, Timothy W. Simpson
Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings
-
Comparative analysis of machine learning algorithms for predicting standard time in a manufacturing environment AI EDAM (IF 2.1) Pub Date : 2023-01-12 Erman Çakıt, Metin Dağdeviren
Determining accurate standard time using direct measurement techniques is especially challenging in companies that do not have a proper environment for time measurement studies or that manufacture items requiring complex production schedules. New and specific time measurement techniques are required for such companies. This research developed a novel time estimation approach based on several machine
-
Neural networks with dimensionality reduction for predicting temperature change due to plastic deformation in a cold rolling simulation AI EDAM (IF 2.1) Pub Date : 2023-01-06 Chun Kit Jeffery Hou, Kamran Behdinan
Cold rolling involves large deformation of the workpiece leading to temperature increase due to plastic deformation. This process is highly nonlinear and leads to large computation times to fully model the process. This paper describes the use of dimension-reduced neural networks (DR-NNs) for predicting temperature changes due to plastic deformation in a two-stage cold rolling process. The main objective
-
A stochastic topology optimization algorithm for improved fluid dynamics systems AI EDAM (IF 2.1) Pub Date : 2023-01-03 Fox Furrokh, Nic Zhang
The use of topology optimization in the design of fluid dynamics systems is still in its infancy. With the decreasing cost of additive manufacture, the application of topology optimization in the design of structural components has begun to increase. This paper provides a method for using topology optimization to reduce the power dissipation of fluid dynamics systems, with the novelty of it being the
-
Adaptive hyperball Kriging method for efficient reliability analysis AI EDAM (IF 2.1) Pub Date : 2022-12-29 I-Tung Yang, Handy Prayogo
Although an accurate reliability assessment is essential to build a resilient infrastructure, it usually requires time-consuming computation. To reduce the computational burden, machine learning-based surrogate models have been used extensively to predict the probability of failure for structural designs. Nevertheless, the surrogate model still needs to compute and assess a certain number of training
-
An evolutionary form design method based on aesthetic dimension selection and NSGA-II AI EDAM (IF 2.1) Pub Date : 2022-11-04 Lingyu Wang, Siyu Zhu, Jin Qi, Jie Hu
In the era of rapid product update and intense competition, aesthetic design has been increasingly important in various fields, as aesthetic feelings of customers largely influence their purchase preferences. However, the quantification of aesthetic feeling is still a very subjective process due to vague evaluations. The determination of form parameters according to aesthetics is difficult hitherto
-
Machine learning in requirements elicitation: a literature review AI EDAM (IF 2.1) Pub Date : 2022-10-26 Cheligeer Cheligeer, Jingwei Huang, Guosong Wu, Nadia Bhuiyan, Yuan Xu, Yong Zeng
A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What
-
Product redesign considering the sensitivity of customer satisfaction AI EDAM (IF 2.1) Pub Date : 2022-10-17 Kaixin Sha, Yupeng Li, Zhihua Zhao, Na Zhang
Redesign is a widespread strategy for product improvement whose essence is the optimization of design parameters (DPs) considering the trade-off between customer satisfaction and cost concerns. Similar to the relation between customer requirements (CRs) and customer satisfaction, the sensitivity of customer satisfaction is diverse to different DPs. In this study, a sensitivity-enhanced customer satisfaction
-
Gamification of design thinking: a way to enhance effectiveness of learning AI EDAM (IF 2.1) Pub Date : 2022-09-29 Apoorv Naresh Bhatt, Amaresh Chakrabarti
The goal of this paper is to develop and test a gamified design thinking framework, including its pedagogical elements, for supporting various learning objectives for school students. By synthesizing the elements and principles of design, learning and games, the authors propose a framework for a learning tool for school students to fulfil a number of learning objectives; the framework includes a design
-
Machine learning model to predict tensile properties of annealed Ti6Al4V parts prepared by selective laser melting AI EDAM (IF 2.1) Pub Date : 2022-09-29 Zhaotong Yang, Mei Yang, Richard Sisson, Yanhua Li, Jianyu Liang
In this work, an artificial neural network model is established to understand the relationship among the tensile properties of as-printed Ti6Al4V parts, annealing parameters, and the tensile properties of annealed Ti6Al4V parts. The database was established by collecting published reports on the annealing treatment of selective laser melting (SLM) Ti6Al4V, from 2006 to 2020. Using the established model
-
A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation AI EDAM (IF 2.1) Pub Date : 2022-09-19 Vahid Pourmostaghimi, Mohammad Zadshakoyan, Saman Khalilpourazary, Mohammad Ali Badamchizadeh
In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including
-
Procedure for assessing the quality of explanations in failure analysis AI EDAM (IF 2.1) Pub Date : 2022-08-08 Kristian González Barman
This paper outlines a procedure for assessing the quality of failure explanations in engineering failure analysis. The procedure structures the information contained in explanations such that it enables to find weak points, to compare competing explanations, and to provide redesign recommendations. These features make the procedure a good asset for critical reflection on some areas of the engineering
-
Parametric optimization of FDM using the ANN-based whale optimization algorithm AI EDAM (IF 2.1) Pub Date : 2022-08-08 Praveen Kumar, Pardeep Gupta, Indraj Singh
Surface roughness (SR) is one of the major parameters used to govern the quality of the fused deposition modeling (FDM)-printed products, and the FDM process parameters can be easily regulated in order to obtain a good surface finish. The surface quality of the product produced by the FDM is generally affected by the staircase effect that needs to be managed. Also, the production time (PT) to fabricate
-
Extenics enhanced axiomatic design procedure for AI applications AI EDAM (IF 2.1) Pub Date : 2022-08-03 Wenjuan Li, C. Steve Suh, Xiangyang Xu, Zhenghe Song
This paper introduces a method to improve the design procedure of axiomatic design theory (AD) with Extenics. A comprehensive review of the AD indicates that the powerful principle of AD has been widely studied and applied to many areas, however, inexperienced practitioners of the AD theory still find it difficult to follow or apply the principles in their design which inadvertently often leads to
-
Data-enabled sketch search and retrieval for visual design stimuli generation AI EDAM (IF 2.1) Pub Date : 2022-08-02 Zijian Zhang, Yan Jin
Access to vast datasets of visual and textual materials has become significantly easier. How to take advantage of the conveniently available data to support creative design activities remains a challenge. In the phase of idea generation, the visual analogy is considered an effective strategy to stimulate designers to create innovative ideas. Designers can read useful information off vague and incomplete
-
Enabling multi-modal search for inspirational design stimuli using deep learning AI EDAM (IF 2.1) Pub Date : 2022-07-27 Elisa Kwon, Forrest Huang, Kosa Goucher-Lambert
Inspirational stimuli are known to be effective in supporting ideation during early-stage design. However, prior work has predominantly constrained designers to using text-only queries when searching for stimuli, which is not consistent with real-world design behavior where fluidity across modalities (e.g., visual, semantic, etc.) is standard practice. In the current work, we introduce a multi-modal
-
Design change prediction based on social media sentiment analysis AI EDAM (IF 2.1) Pub Date : 2022-07-27 Edwin C.Y. Koh
The use of artificial intelligence (AI) techniques to uncover customer sentiment is not uncommon. However, the integration of sentiment analysis with research in design change prediction remains an untapped potential. This paper presents a method that uses social media sentiment analysis to identify opportunities for design change and the set of product components affected by the change. The method
-
Towards comprehensive digital evaluation of low-carbon machining process planning AI EDAM (IF 2.1) Pub Date : 2022-07-25 Zhaoming Chen, Jinsong Zou, Wei Wang
Low-carbon process planning is the basis for the implementation of low-carbon manufacturing technology. And it is of profound significance to improve process executability, reduce environmental pollution, decrease manufacturing cost, and improve product quality. In this paper, based on the perceptual data of parts machining process, considering the diversity of process planning schemes and factors
-
Artificial intelligence methods for improving the inventive design process, application in lattice structure case study AI EDAM (IF 2.1) Pub Date : 2022-07-18 Masih Hanifi, Hicham Chibane, Remy Houssin, Denis Cavallucci, Naser Ghannad
Nowadays, firms are constantly looking for methodological approaches that help them to decrease the time needed for the innovation process. Among these approaches, it is worth mentioning the TRIZ-based frameworks such as the Inventive Design Methodology (IDM), where the Problem Graph method is used to formulate a problem. However, the application of IDM is time-consuming due to the construction of
-
Breaking up data-enabled design: expanding and scaling up for the clinical context AI EDAM (IF 2.1) Pub Date : 2022-05-19 Renee Noortman, Peter Lovei, Mathias Funk, Eva Deckers, Stephan Wensveen, Berry Eggen
Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This
-
The epsilon-knowledge: an emerging complement of Machlup's types of disciplinary knowledge AI EDAM (IF 2.1) Pub Date : 2022-05-16 Imre Horváth
Machlup used the words alpha, beta, and gamma to identify humanities, science, and social science as three distinct fields of academic learning and knowing, in addition to general knowledge. Gilles and Paquet identified a fourth type of disciplinary knowledge and labeled it as delta. This includes the knowledge of creative disciplines such as design, law, and economy. Since the time of these road-paving
-
Assessment of predictive probability models for effective mechanical design feature reuse AI EDAM (IF 2.1) Pub Date : 2022-05-06 Gokula Vasantha, David Purves, John Quigley, Jonathan Corney, Andrew Sherlock, Geevin Randika
This research envisages an automated system to inform engineers when opportunities occur to use existing features or configurations during the development of new products. Such a system could be termed a "predictive CAD system" because it would be able to suggest feature choices that follow patterns established in existing products. The predictive CAD literature largely focuses on predicting components
-
A method to explore strategies to communicate user experience through storyboards: an automotive design case study AI EDAM (IF 2.1) Pub Date : 2022-03-21 Jacob Rodda, Charlie Ranscombe, Blair Kuys
An engaging user experience is an increasingly important design characteristic in the automotive industry. Compared with physical design characteristics (form, material, mechanical design, appearance), automotive designers find UX (user experience) challenging to communicate during the early stages of the design process without investing in expensive prototypes and/or models. This paper presents the
-
A deep learning-based approach to extraction of filler morphology in SEM images with the application of automated quality inspection AI EDAM (IF 2.1) Pub Date : 2022-03-18 Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin
Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and
-
Potentials and challenges of analyzing use phase data in product planning of manufacturing companies AI EDAM (IF 2.1) Pub Date : 2022-03-01 Maurice Meyer, Timm Fichtler, Christian Koldewey, Roman Dumitrescu
The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address
-
A dynamic model for engineering change propagations in multiple product development stages AI EDAM (IF 2.1) Pub Date : 2022-02-16 Yulaing Li, Wei Zhao, Wenqi Zhang, Meng Chen
To accurately predict propagation dynamics for single or multiple change propagations across different product development stages in a sequential or concurrent way is critical for decision-making of implementing change requests. In this paper, a change propagation dynamic model is built based on the compartmentalization of engineering entities into susceptible engineering entities and affected engineering
-
Brain activity in constrained and open design: the effect of gender on frequency bands AI EDAM (IF 2.1) Pub Date : 2022-02-15 S. Vieira, M. Benedek, J. Gero, S. Li, G. Cascini
This paper presents results from a design neurocognition study on the effect of gender on EEG frequency band power when performing constrained and open design. We used electroencephalography to measure the brain activity of 84 professional designers. We investigated differences in frequency power associated with gender of 38 female and 46 male designers, while performing two prototypical design tasks
-
Margin-based approach for outlier detection of industrial design data using a modified general regression neural network AI EDAM (IF 2.1) Pub Date : 2022-02-09 Jayaram Sivaramakrishnan, Gareth Lee, David Parlevliet, Kok Wai Wong
The choice of components in industrial design involves setting design parameters that typically must reside inside permissible ranges called “design margins”. This paper proposes a novel automated method called the Margin-Based General Regression Neural Network (MB-GRNN) that classifies design errors for design parameters that are outside of permissible ranges as outliers, directly from industrial
-
Data-inspired co-design for museum and gallery visitor experiences AI EDAM (IF 2.1) Pub Date : 2022-02-09 Dimitrios Darzentas, Harriet Cameron, Hanne Wagner, Peter Craigon, Edgar Bodiaj, Jocelyn Spence, Paul Tennent, Steve Benford
The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case that reveals opportunities and challenges for the use of personal data. We present a portfolio of data-inspired visiting experiences that emerged from a
-
Continuous cycles of data-enabled design: reimagining the IoT development process AI EDAM (IF 2.1) Pub Date : 2022-02-09 Boyeun Lee, Rachel Cooper, David Hands, Paul Coulton
With the emergence of Internet of Things (IoT) as a new source of “big” data and value creation, businesses encounter novel opportunities as well as challenges in IoT design. Although recent research argues that digital technology can enable new kinds of development processes that are distinctive from their counterparts in the 20th century, minimal attention has been focused on the IoT design process
-
What shape grammars do that CAD should: the 14 cases of shape embedding AI EDAM (IF 2.1) Pub Date : 2022-02-09 Tzu-Chieh Kurt Hong, Athanassios Economou
Shape queries based on shape embedding under a given Euclidean, affine, or linear transformation are absent from current CAD systems. The only systems that have attempted to implement shape embedding are the shape grammar interpreters albeit with promising but inconclusive results. The work here identifies all possible 14 cases of shape embedding with respect to the number of available registration
-
Diagonal decompositions of shapes and their algebras AI EDAM (IF 2.1) Pub Date : 2022-02-09 Djordje Kristic
The formal approach to shapes and their algebras, as it appears in shape grammar theory, has been reviewed. It starts with geometric elements and their partial algebras, continues to shapes, their algebras, and boundaries, as well as algebras that calculate with shapes and their boundaries. There is a number of new concepts introduced along the way. These include diagonal decompositions and their algebras
-
Visualization and inquiry into mental content in design activity: a case study of design interpretation AI EDAM (IF 2.1) Pub Date : 2022-02-09 Yuval Kahlon, Haruyuki Fujii
The study of interpretation is of major importance for our understanding of design cognition. When interacting with design representations, designers often rely on metaphorical descriptions as interpretive devices, which aid in coping with the task at hand. Consequently, such descriptions can enlighten us regarding the designer's perspective of the situation, and their analysis can deepen our knowledge