Pub Date : 2022-02-09DOI: 10.1017/S0890060421000329
J. Sivaramakrishnan, Gareth Lee, D. Parlevliet, Kok Wai Wong
Abstract 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 design data, using an unsupervised machine learning approach. The method is based on a modified GRNN that estimates extremal margin boundaries of design parameters by self-learning the features from datasets. These extremal permissible margin boundaries are determined by “stretching out” the upper and lower GRNN surfaces using an iterative application of stretch factors (a second kernel weighting factor). The method creates a variable insensitive band surrounding the data cloud, interlinked with the normal regression function, providing upper and lower margin boundaries. These boundaries can then be used to determine outliers and to predict a range of permissible values of design parameters during design. Pushing out extremal margin boundaries reduce the false identification of outliers. This classification technique could be used by industrial engineers to detect likely outliers and to predict a range of permissible output limits for chosen design parameters. The efficacy of this method has been validated against the widespread Parzen window method by comparing experimental results from three multivariate datasets. It was found that the two methods have different but complementary capabilities. The MB-GRNN also uses a modified algorithm for estimating the smoothing parameter using a combination of clustering, k-nearest neighbor, and localized covariance matrix.
{"title":"Margin-based approach for outlier detection of industrial design data using a modified general regression neural network","authors":"J. Sivaramakrishnan, Gareth Lee, D. Parlevliet, Kok Wai Wong","doi":"10.1017/S0890060421000329","DOIUrl":"https://doi.org/10.1017/S0890060421000329","url":null,"abstract":"Abstract 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 design data, using an unsupervised machine learning approach. The method is based on a modified GRNN that estimates extremal margin boundaries of design parameters by self-learning the features from datasets. These extremal permissible margin boundaries are determined by “stretching out” the upper and lower GRNN surfaces using an iterative application of stretch factors (a second kernel weighting factor). The method creates a variable insensitive band surrounding the data cloud, interlinked with the normal regression function, providing upper and lower margin boundaries. These boundaries can then be used to determine outliers and to predict a range of permissible values of design parameters during design. Pushing out extremal margin boundaries reduce the false identification of outliers. This classification technique could be used by industrial engineers to detect likely outliers and to predict a range of permissible output limits for chosen design parameters. The efficacy of this method has been validated against the widespread Parzen window method by comparing experimental results from three multivariate datasets. It was found that the two methods have different but complementary capabilities. The MB-GRNN also uses a modified algorithm for estimating the smoothing parameter using a combination of clustering, k-nearest neighbor, and localized covariance matrix.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44147738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S0890060421000378
G. Koronis, Arlindo Silva, Jacob Kang Kai Siang, C. Yogiaman
Abstract This academic-based investigation is focused on identifying elements that contribute toward the generation of efficient design briefs and their correlation with design outcomes of a sketching exercise. Four conditions are compared: a baseline group, an abstract group, a contextual information group, and a group that was given various example solutions. Via more in-depth surveys, we sought to elicit correlations between the students’ design creativity and stimuli permutations of the different design conditions. Results show that the contextual information groups, which were presented with higher levels of stimulus fidelity, had higher novelty scores, while abstract groups performed well in usefulness. These findings contribute to the formulation of design briefs where the goal is to stimulate the creativity of design outcomes and examine their relationships with student's perceptions of design exercises.
{"title":"A study on the link between design brief structure and stimulus fidelity to optimize novelty and usefulness","authors":"G. Koronis, Arlindo Silva, Jacob Kang Kai Siang, C. Yogiaman","doi":"10.1017/S0890060421000378","DOIUrl":"https://doi.org/10.1017/S0890060421000378","url":null,"abstract":"Abstract This academic-based investigation is focused on identifying elements that contribute toward the generation of efficient design briefs and their correlation with design outcomes of a sketching exercise. Four conditions are compared: a baseline group, an abstract group, a contextual information group, and a group that was given various example solutions. Via more in-depth surveys, we sought to elicit correlations between the students’ design creativity and stimuli permutations of the different design conditions. Results show that the contextual information groups, which were presented with higher levels of stimulus fidelity, had higher novelty scores, while abstract groups performed well in usefulness. These findings contribute to the formulation of design briefs where the goal is to stimulate the creativity of design outcomes and examine their relationships with student's perceptions of design exercises.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46049632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S0890060421000299
Boyeun Lee, R. Cooper, D. Hands, P. Coulton
Abstract 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. In order to contextualize New Product Development (NPD) processes for IoT, this paper comprehensively interrogates existing, and emerging development approaches for products, services, software, and integrated products, and several factors that affect designing IoT. This discussion includes the generic development process, the commonalities and differences of different development approaches, and processes. The paper demonstrates that only a few existing approaches reflect vital characteristics of networked artifacts or the integration of data science within the development model, which is one of the key attributes of IoT design. From these investigations, we propose “The Mobius Strip Model of IoT Development ProcessI,” a conceptual process for IoT design, which is distinctive to others. The continuous loops of the IoT design integrate the attributes and phases of different processes and consist of two different development approaches and strategies. Understanding the particular attributes of the IoT NPD process can help novice and experienced researchers in both feeding and drawing insight from the broader design discourse.
{"title":"Continuous cycles of data-enabled design: reimagining the IoT development process","authors":"Boyeun Lee, R. Cooper, D. Hands, P. Coulton","doi":"10.1017/S0890060421000299","DOIUrl":"https://doi.org/10.1017/S0890060421000299","url":null,"abstract":"Abstract 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. In order to contextualize New Product Development (NPD) processes for IoT, this paper comprehensively interrogates existing, and emerging development approaches for products, services, software, and integrated products, and several factors that affect designing IoT. This discussion includes the generic development process, the commonalities and differences of different development approaches, and processes. The paper demonstrates that only a few existing approaches reflect vital characteristics of networked artifacts or the integration of data science within the development model, which is one of the key attributes of IoT design. From these investigations, we propose “The Mobius Strip Model of IoT Development ProcessI,” a conceptual process for IoT design, which is distinctive to others. The continuous loops of the IoT design integrate the attributes and phases of different processes and consist of two different development approaches and strategies. Understanding the particular attributes of the IoT NPD process can help novice and experienced researchers in both feeding and drawing insight from the broader design discourse.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44891072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S0890060421000172
Yuval Kahlon, Haruyuki Fujii
Abstract 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 of design cognition. We observe designers as they metaphorically interpret design representations during a simple task of spatial configuration, and introduce an approach for modeling this practice, as a means for getting insights into the designer's mental world. In this, we draw on traditional practices of protocol analysis, as well as on state-of-the-art theoretical frameworks for situated design and discourse analysis. Our integrated approach demonstrates how important relations between external and internal reality in design activity can be mapped and visualized. This sheds some light on the cognitive process of interpretation in design. The proposed method can both serve as a basis for detailed analyses of design cognition and for the enhancement of current models for situated design agents.
{"title":"Visualization and inquiry into mental content in design activity: a case study of design interpretation","authors":"Yuval Kahlon, Haruyuki Fujii","doi":"10.1017/S0890060421000172","DOIUrl":"https://doi.org/10.1017/S0890060421000172","url":null,"abstract":"Abstract 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 of design cognition. We observe designers as they metaphorically interpret design representations during a simple task of spatial configuration, and introduce an approach for modeling this practice, as a means for getting insights into the designer's mental world. In this, we draw on traditional practices of protocol analysis, as well as on state-of-the-art theoretical frameworks for situated design and discourse analysis. Our integrated approach demonstrates how important relations between external and internal reality in design activity can be mapped and visualized. This sheds some light on the cognitive process of interpretation in design. The proposed method can both serve as a basis for detailed analyses of design cognition and for the enhancement of current models for situated design agents.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48029880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Machine learning (ML) is increasingly used to enhance intelligent products in the field of product design. However, ML has a never-ending lifecycle in which its capabilities and technical properties iteratively change as new annotated data are utilized. The never-ending lifecycle of ML (which includes data annotation, model training, and other steps) has led to challenges to the prototyping of ML-enhanced products and requires a high level of ML literacy in designers. To facilitate the prototyping of ML-enhanced products and improve the ML literacy of designers, we draw inspiration from a design method called Material Lifecycle Thinking (MLT), which regards ML as a continuously evolving design material. Based on the MLT, we proposed a cyclical prototype workflow and developed inML Kit, a toolkit enabling designers to make functional ML prototypes and improve ML literacy by involving them in the never-ending ML lifecycle. The toolkit was designed, iterated, and implemented through the participatory design process with experienced designers in this field. We evaluated inML Kit by conducting a controlled user study where our toolkit was compared with Google AIY. The evaluation results imply that our inML Kit helps designers to make functional ML prototypes while improving their ML literacy.
{"title":"inML Kit: empowering the prototyping of ML-enhanced products by involving designers in the ML lifecycle","authors":"Ling-yun Sun, Yuyang Zhang, Zhuoshu Li, Zihong Zhou, Zhibin Zhou","doi":"10.1017/S0890060421000391","DOIUrl":"https://doi.org/10.1017/S0890060421000391","url":null,"abstract":"Abstract Machine learning (ML) is increasingly used to enhance intelligent products in the field of product design. However, ML has a never-ending lifecycle in which its capabilities and technical properties iteratively change as new annotated data are utilized. The never-ending lifecycle of ML (which includes data annotation, model training, and other steps) has led to challenges to the prototyping of ML-enhanced products and requires a high level of ML literacy in designers. To facilitate the prototyping of ML-enhanced products and improve the ML literacy of designers, we draw inspiration from a design method called Material Lifecycle Thinking (MLT), which regards ML as a continuously evolving design material. Based on the MLT, we proposed a cyclical prototype workflow and developed inML Kit, a toolkit enabling designers to make functional ML prototypes and improve ML literacy by involving them in the never-ending ML lifecycle. The toolkit was designed, iterated, and implemented through the participatory design process with experienced designers in this field. We evaluated inML Kit by conducting a controlled user study where our toolkit was compared with Google AIY. The evaluation results imply that our inML Kit helps designers to make functional ML prototypes while improving their ML literacy.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47385986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S089006042100038X
Jelena Pejić, P. Pejic
Abstract The main objective of this paper is to develop a novel approach for linear kitchen layout design which utilizes information from existing layouts via machine learning algorithms. With the growing popularity of large-scale virtual 3D environments for architectural visualization and the game industry, the manual interior design of virtual scenes becomes prohibitively expensive in terms of time and resources. In our approach, the machine learning model automatically generates layout suggestions. The proposed procedural kitchen generation (PKG) model is a pipeline of six Machine Learning (ML) classifiers that are trained and tested on a kitchen layout dataset created by interior designers. The performances of the model are evaluated for the following classifiers: Random forest, Decision tree, AdaBoost, Naive Bayes, MLP, SVM, and L2 Logistic regression. Random forest, as the best performing classifier is used in the final PKG model, and integrated into Unity Engine for automatic 3D kitchen generation and presentation. The PKG model is evaluated in the quantitative and perceptual study, showing better performance than the prior rule-based method. The perceptual study results demonstrate that our tool can be used to speed up designer's work, improve communication with clients, and educate interior design students.
{"title":"Linear kitchen layout design via machine learning","authors":"Jelena Pejić, P. Pejic","doi":"10.1017/S089006042100038X","DOIUrl":"https://doi.org/10.1017/S089006042100038X","url":null,"abstract":"Abstract The main objective of this paper is to develop a novel approach for linear kitchen layout design which utilizes information from existing layouts via machine learning algorithms. With the growing popularity of large-scale virtual 3D environments for architectural visualization and the game industry, the manual interior design of virtual scenes becomes prohibitively expensive in terms of time and resources. In our approach, the machine learning model automatically generates layout suggestions. The proposed procedural kitchen generation (PKG) model is a pipeline of six Machine Learning (ML) classifiers that are trained and tested on a kitchen layout dataset created by interior designers. The performances of the model are evaluated for the following classifiers: Random forest, Decision tree, AdaBoost, Naive Bayes, MLP, SVM, and L2 Logistic regression. Random forest, as the best performing classifier is used in the final PKG model, and integrated into Unity Engine for automatic 3D kitchen generation and presentation. The PKG model is evaluated in the quantitative and perceptual study, showing better performance than the prior rule-based method. The perceptual study results demonstrate that our tool can be used to speed up designer's work, improve communication with clients, and educate interior design students.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49580959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S0890060421000263
T. Hong, A. Economou
Abstract 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 points, four for determinate cases and ten for indeterminate ones, and an approach is sketched to take on the complexities underlying the indeterminate cases. All visual calculations are done with shapes consisting of straight lines in the Euclidean plane within the algebra Uij for i = 1 the dimension of lines and j = 2 the dimension of space in which the lines are defined, transformed and combined. Aspects of interface design and integration to current work design workflows are deliberately left aside.
{"title":"What shape grammars do that CAD should: the 14 cases of shape embedding","authors":"T. Hong, A. Economou","doi":"10.1017/S0890060421000263","DOIUrl":"https://doi.org/10.1017/S0890060421000263","url":null,"abstract":"Abstract 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 points, four for determinate cases and ten for indeterminate ones, and an approach is sketched to take on the complexities underlying the indeterminate cases. All visual calculations are done with shapes consisting of straight lines in the Euclidean plane within the algebra Uij for i = 1 the dimension of lines and j = 2 the dimension of space in which the lines are defined, transformed and combined. Aspects of interface design and integration to current work design workflows are deliberately left aside.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"36 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57251268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-09DOI: 10.1017/S0890060421000317
Dimitrios Paris Darzentas, Harriet R. Cameron, H. Wagner, Peter J. Craigon, Edgar Bodiaj, J. Spence, P. Tennent, Steve Benford
Abstract 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 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.
{"title":"Data-inspired co-design for museum and gallery visitor experiences","authors":"Dimitrios Paris Darzentas, Harriet R. Cameron, H. Wagner, Peter J. Craigon, Edgar Bodiaj, J. Spence, P. Tennent, Steve Benford","doi":"10.1017/S0890060421000317","DOIUrl":"https://doi.org/10.1017/S0890060421000317","url":null,"abstract":"Abstract 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 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49202930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-03DOI: 10.1017/S0890060421000342
X. Diao, Michael C. Pietrykowski, Fuqun Huang, Chetan Mutha, C. Smidts
Abstract Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.
{"title":"An ontology-based fault generation and fault propagation analysis approach for safety-critical computer systems at the design stage","authors":"X. Diao, Michael C. Pietrykowski, Fuqun Huang, Chetan Mutha, C. Smidts","doi":"10.1017/S0890060421000342","DOIUrl":"https://doi.org/10.1017/S0890060421000342","url":null,"abstract":"Abstract Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43841862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1017/S0890060422000257
Fox Furrokh, Nic Zhang
Abstract 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 first application of stochastic mechanisms in the design of 3D fluid–solid geometrical interfaces. The optimization algorithm uses the continuous adjoint method for sensitivity analysis and is optimized against an objective function for fluid power dissipation. The paper details the methodology behind a vanilla gradient descent approach before introducing stochastic behavior through a minibatch-based system. Both algorithms are then applied to a novel case study for an internal combustion engine's piston cooling gallery before the performance of each algorithm's resulting geometry is analyzed and compared. The vanilla gradient descent algorithm achieves an 8.9% improvement in pressure loss through the case study, and this is surpassed by the stochastic descent algorithm which achieved a 9.9% improvement, however this improvement came with a large time cost. Both approaches produced similarly unintuitive geometry solutions to successfully improve the performance of the cooling gallery.
{"title":"A stochastic topology optimization algorithm for improved fluid dynamics systems","authors":"Fox Furrokh, Nic Zhang","doi":"10.1017/S0890060422000257","DOIUrl":"https://doi.org/10.1017/S0890060422000257","url":null,"abstract":"Abstract 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 first application of stochastic mechanisms in the design of 3D fluid–solid geometrical interfaces. The optimization algorithm uses the continuous adjoint method for sensitivity analysis and is optimized against an objective function for fluid power dissipation. The paper details the methodology behind a vanilla gradient descent approach before introducing stochastic behavior through a minibatch-based system. Both algorithms are then applied to a novel case study for an internal combustion engine's piston cooling gallery before the performance of each algorithm's resulting geometry is analyzed and compared. The vanilla gradient descent algorithm achieves an 8.9% improvement in pressure loss through the case study, and this is surpassed by the stochastic descent algorithm which achieved a 9.9% improvement, however this improvement came with a large time cost. Both approaches produced similarly unintuitive geometry solutions to successfully improve the performance of the cooling gallery.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"36 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57250978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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