Pub Date : 2020-12-04DOI: 10.1017/S0890060420000335
Mohammadali Ashrafganjouei, J. Gero
Abstract This paper presents the results of a study that explores the effect of a visual constraint on design behaviors of architecture students. To examine this effect, 24 second-year architecture students volunteered to participate. Each of them undertook similar conceptual design briefs in two different conditions, one with and another without a visual constraint. Retrospective reporting was used to collect the verbalization of participants. The FBS ontology was used to model the design cognition of the participants by coding their design protocols. A dynamic analysis was used to study the differences between the two conditions based on the problem–solution index. A further index, the pre-structure–post-structure index, was proposed to measure design behavior differences between the two conditions. The correspondence analysis was used to explore the effect of gender. There were statistically significant differences in the distributions of cognitive effort between the two groups. These differences include in the visual constraint group a decrease in the focus on behavior before structure and in the processes related to it, compared to the non-visual constraint group. The non-visual constraint group changed their focus on problem framing and solving while adding a visual constraint led participants to focus simultaneously on both framing and solving. The visual constraint group had a different attention temporally to pre- and post-structure design processes during designing than the non-visual constraint group. The order of experiencing the two design sessions had only a small effect. The results of correspondence analysis demonstrate that there are categorical gender differences not found using statistical testing.
{"title":"Exploring the effect of a visual constraint on students’ design cognition","authors":"Mohammadali Ashrafganjouei, J. Gero","doi":"10.1017/S0890060420000335","DOIUrl":"https://doi.org/10.1017/S0890060420000335","url":null,"abstract":"Abstract This paper presents the results of a study that explores the effect of a visual constraint on design behaviors of architecture students. To examine this effect, 24 second-year architecture students volunteered to participate. Each of them undertook similar conceptual design briefs in two different conditions, one with and another without a visual constraint. Retrospective reporting was used to collect the verbalization of participants. The FBS ontology was used to model the design cognition of the participants by coding their design protocols. A dynamic analysis was used to study the differences between the two conditions based on the problem–solution index. A further index, the pre-structure–post-structure index, was proposed to measure design behavior differences between the two conditions. The correspondence analysis was used to explore the effect of gender. There were statistically significant differences in the distributions of cognitive effort between the two groups. These differences include in the visual constraint group a decrease in the focus on behavior before structure and in the processes related to it, compared to the non-visual constraint group. The non-visual constraint group changed their focus on problem framing and solving while adding a visual constraint led participants to focus simultaneously on both framing and solving. The visual constraint group had a different attention temporally to pre- and post-structure design processes during designing than the non-visual constraint group. The order of experiencing the two design sessions had only a small effect. The results of correspondence analysis demonstrate that there are categorical gender differences not found using statistical testing.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"3 - 19"},"PeriodicalIF":2.1,"publicationDate":"2020-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0890060420000335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"57251087","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 Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.
{"title":"Evaluating aircraft cockpit emotion through a neural network approach","authors":"Yanhao Chen, Suihuai Yu, Jianjie Chu, Dengkai Chen, Mingjiu Yu","doi":"10.1017/S0890060420000475","DOIUrl":"https://doi.org/10.1017/S0890060420000475","url":null,"abstract":"Abstract Studies show that there are shortcomings in applying conventional methods for the emotional evaluation of the aircraft cockpit. In order to resolve this problem, a more efficient cockpit emotion evaluation system is established in the present study to simply and quickly obtain the cockpit emotion evaluation value. To this end, the neural network is applied to construct an emotional model to evaluate the emotional prediction of the interior design of the aircraft cockpit. Moreover, several technologies and the Kansei engineering method are applied to acquire the cockpit interior emotional evaluation data for typical aircraft models. In this regard, the radical basis function neural network (RBFNN), Elman neural network (ENN), and the general regression neural network (GRNN) are applied to construct the sentimental prediction evaluation model. Then, the three models are comprehensively compared through factors such as the model evaluation criteria, network structure, and network parameters. Obtained experimental results indicate that the GRNN not only has the highest classification accuracy but also has the highest stability in comparison to the other two neural networks, so that it is a more appropriate method for the emotional evaluation of the aircraft cockpit. Results of the present study provide decision supports for the emotional evaluation of the cockpit interior space.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"81 - 98"},"PeriodicalIF":2.1,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0890060420000475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44404914","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}
{"title":"Investigating vehicle interior designs using models that evaluate user sensory experience and perceived value – CORRIGENDUM","authors":"Ching-Chien Liang, Ya-Hsueh Lee, Chun-Heng Ho, Kuo-Hsiang Chen","doi":"10.1017/s0890060420000244","DOIUrl":"https://doi.org/10.1017/s0890060420000244","url":null,"abstract":"","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"34 1","pages":"531 - 531"},"PeriodicalIF":2.1,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/s0890060420000244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48264314","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 : 2020-10-26DOI: 10.1017/S0890060420000347
S. Stojadinovic, V. Majstorovic, N. Durakbasa
Abstract Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.
{"title":"Toward a cyber-physical manufacturing metrology model for industry 4.0","authors":"S. Stojadinovic, V. Majstorovic, N. Durakbasa","doi":"10.1017/S0890060420000347","DOIUrl":"https://doi.org/10.1017/S0890060420000347","url":null,"abstract":"Abstract Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"20 - 36"},"PeriodicalIF":2.1,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0890060420000347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45216167","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 : 2020-10-26DOI: 10.1017/S0890060420000396
Wenbin Zhou, Xuhui Xia, Zelin Zhang, Lei Wang
Abstract The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.
{"title":"Association rules mining between service demands and remanufacturing services","authors":"Wenbin Zhou, Xuhui Xia, Zelin Zhang, Lei Wang","doi":"10.1017/S0890060420000396","DOIUrl":"https://doi.org/10.1017/S0890060420000396","url":null,"abstract":"Abstract The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"240 - 250"},"PeriodicalIF":2.1,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0890060420000396","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43283343","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 : 2020-10-07DOI: 10.1017/S089006042000044X
Jizhuang Hui, Jingyuan Lei, Kai Ding, Fuqiang Zhang, Jingxiang Lv
Abstract In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.
{"title":"Autonomous resource allocation of smart workshop for cloud machining orders","authors":"Jizhuang Hui, Jingyuan Lei, Kai Ding, Fuqiang Zhang, Jingxiang Lv","doi":"10.1017/S089006042000044X","DOIUrl":"https://doi.org/10.1017/S089006042000044X","url":null,"abstract":"Abstract In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"226 - 239"},"PeriodicalIF":2.1,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S089006042000044X","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48766553","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 : 2020-09-24DOI: 10.1017/S0890060420000414
J. Erichsen, Heikki Sjöman, M. Steinert, T. Welo
Abstract Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.
{"title":"Protobooth: gathering and analyzing data on prototyping in early-stage engineering design projects by digitally capturing physical prototypes","authors":"J. Erichsen, Heikki Sjöman, M. Steinert, T. Welo","doi":"10.1017/S0890060420000414","DOIUrl":"https://doi.org/10.1017/S0890060420000414","url":null,"abstract":"Abstract Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"65 - 80"},"PeriodicalIF":2.1,"publicationDate":"2020-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0890060420000414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43753246","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 : 2020-09-21DOI: 10.1017/S0890060420000384
Y. L. Cio, Yuan-Zhuo Ma, A. Vadean, G. Beltrame, S. Achiche
Abstract The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.
{"title":"Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies","authors":"Y. L. Cio, Yuan-Zhuo Ma, A. Vadean, G. Beltrame, S. Achiche","doi":"10.1017/S0890060420000384","DOIUrl":"https://doi.org/10.1017/S0890060420000384","url":null,"abstract":"Abstract The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.","PeriodicalId":50951,"journal":{"name":"Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing","volume":"35 1","pages":"49 - 64"},"PeriodicalIF":2.1,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S0890060420000384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44862908","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：