Additive manufacturing (AM) is a revolutionary manufacturing technology that can produce products in a layer by layer manner. Because of its significant merits in complex geometry and fast fabrication, AM has received worldwide attentions from both industries and academia. Although extensive studies have been conducted on the aspects of process design, prototyping, quality control and reliability, the study of adopting AM in the application is still not fully investigated, which motives this study. In order to close this gap, this study proposes a fuzzy Bayesian Network based approach to discover the applicability of AM. Twelve features of AM applicability obtained from existing literature have been considered in the analysis; fuzzy linguistic description was used to capture the users’ perception; fuzzy Bayesian Network based causation model was developed to study the AM’s adaptiveness. The jet engine blade case study was applied to demonstrate the applicability of the proposed approach. The results showed that fuzzy Bayesian Network based causation approach is able to provide the robust and reliable results of applicability analysis and could also be extended to other risk assessment related design decision making process.
{"title":"Additive Manufacturing Adaptiveness Analysis Using Fuzzy Bayesian Network","authors":"Liting Jing, Junfeng Ma","doi":"10.1115/detc2020-22535","DOIUrl":"https://doi.org/10.1115/detc2020-22535","url":null,"abstract":"\u0000 Additive manufacturing (AM) is a revolutionary manufacturing technology that can produce products in a layer by layer manner. Because of its significant merits in complex geometry and fast fabrication, AM has received worldwide attentions from both industries and academia. Although extensive studies have been conducted on the aspects of process design, prototyping, quality control and reliability, the study of adopting AM in the application is still not fully investigated, which motives this study. In order to close this gap, this study proposes a fuzzy Bayesian Network based approach to discover the applicability of AM. Twelve features of AM applicability obtained from existing literature have been considered in the analysis; fuzzy linguistic description was used to capture the users’ perception; fuzzy Bayesian Network based causation model was developed to study the AM’s adaptiveness. The jet engine blade case study was applied to demonstrate the applicability of the proposed approach. The results showed that fuzzy Bayesian Network based causation approach is able to provide the robust and reliable results of applicability analysis and could also be extended to other risk assessment related design decision making process.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130408760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A build-up process is used to manufacture printed wiring boards (PWBs) for high-density circuits. Presently, CO2 laser beams are used to drill blind via holes (BVHs) that connect copper foils. The Cu-direct drilling process has received considerable attention but is problematic because it produces a copper overhang due to the complex processing phenomena. This report focuses on monitoring scattered matter by Cu-direct laser drilling with a high-speed camera and clarifying the factors related to processing quality while verifying the results by CFD (Computational Fluid Dinamics) analysis. Previous research has shown that processing progress can be made from temperature information using the two-color image method that can measure temperature without contact. However, the two-color image method generates noise in the temperature range (500–3000 °C) which is treated in this research. Filtering was possible by using the RGB data of each pixel on the image. By focusing on laser fluence, it became possible to estimate the laser irradiation time that can guarantee the quality in the drilled hole (BVH) in single pulse continuous irradiation.
{"title":"Monitoring Method for Laser Via Hole Processing of Printed Circuit Boards Based on Two-Color Method With a High-Speed Video Camera","authors":"W. Nakagawa, R. Yamaguchi, T. Hirogaki, E. Aoyama","doi":"10.1115/detc2020-22125","DOIUrl":"https://doi.org/10.1115/detc2020-22125","url":null,"abstract":"\u0000 A build-up process is used to manufacture printed wiring boards (PWBs) for high-density circuits. Presently, CO2 laser beams are used to drill blind via holes (BVHs) that connect copper foils. The Cu-direct drilling process has received considerable attention but is problematic because it produces a copper overhang due to the complex processing phenomena. This report focuses on monitoring scattered matter by Cu-direct laser drilling with a high-speed camera and clarifying the factors related to processing quality while verifying the results by CFD (Computational Fluid Dinamics) analysis. Previous research has shown that processing progress can be made from temperature information using the two-color image method that can measure temperature without contact. However, the two-color image method generates noise in the temperature range (500–3000 °C) which is treated in this research. Filtering was possible by using the RGB data of each pixel on the image. By focusing on laser fluence, it became possible to estimate the laser irradiation time that can guarantee the quality in the drilled hole (BVH) in single pulse continuous irradiation.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"54 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114019473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brian Chell, Steven Hoffenson, Benjamin Kruse, M. Blackburn
Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.
{"title":"Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios","authors":"Brian Chell, Steven Hoffenson, Benjamin Kruse, M. Blackburn","doi":"10.1115/detc2020-22454","DOIUrl":"https://doi.org/10.1115/detc2020-22454","url":null,"abstract":"\u0000 Mission engineering is a growing field with many practical opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce life cycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for their mission context is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces, motivates, and proposes a new approach for performing mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when determining the best possible system design.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127812843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Besides the explicit economic and environmental impacts, the product development process also produces an implicit social value — known as social impact. To help product designers better understand and plan for the social impact that their product may have, we present a social impact checklist table. This checklist table was constructed after a simple study was conducted on the design and reuse of corrugated cardboard. The checklist table provides the designer the opportunity to more deeply consider eleven social impact categories, map those categories to key indicators, and ultimately design parameters that influence social impact. We introduce this checklist table at the early stages of the product development process, aiming to make the otherwise implicit notion of social impact more explicit and recognizable. The checklist table has the potential to make the social dimension of sustainability more accessible to design engineers; they can then better conceive of sustainable solutions and create products that generate positive social impact.
{"title":"Consideration of Social Impacts During the Early Stages of Product Development for Sustainable Design","authors":"H. Jia, C. Mattson, Gabrielle Johnson","doi":"10.1115/detc2020-22237","DOIUrl":"https://doi.org/10.1115/detc2020-22237","url":null,"abstract":"\u0000 Besides the explicit economic and environmental impacts, the product development process also produces an implicit social value — known as social impact. To help product designers better understand and plan for the social impact that their product may have, we present a social impact checklist table. This checklist table was constructed after a simple study was conducted on the design and reuse of corrugated cardboard. The checklist table provides the designer the opportunity to more deeply consider eleven social impact categories, map those categories to key indicators, and ultimately design parameters that influence social impact. We introduce this checklist table at the early stages of the product development process, aiming to make the otherwise implicit notion of social impact more explicit and recognizable. The checklist table has the potential to make the social dimension of sustainability more accessible to design engineers; they can then better conceive of sustainable solutions and create products that generate positive social impact.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131636247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The designers of mechanical products are generally not experts in machining. Therefore, they often design parts with inherent machining difficulties. Although various design for manufacturability tools have been developed to avoid such problems, their use in practice remains limited due to their lack of versatility. We develop a novel piece of software that can automatically detect difficult-to-machine shapes in a part. Using this software, the designer can determine which shapes are difficult to produce using conventional cutting by themselves, and can modify the shape on the spot. In the Internet-based part manufacturing business, the same software can be used to check whether the given part can be produced using the standard milling operations predetermined in a company. Our system is based on “milling simulation”. It detects any shapes that cannot be produced using the prepared cutting tools by executing the milling simulations with the tools, and then visualizing shapes that remain unmachined after all simulations. In this study, the acceleration of the processing is realized using graphics processing unit technology, and it is possible to extract difficult-to-machine shapes in several minutes using a standard PC.
{"title":"Milling Simulation-Based Method to Evaluate Manufacturability of Machine Parts","authors":"M. Inui, Tong Zhang, Nobuyuki Umezu","doi":"10.1115/detc2020-22124","DOIUrl":"https://doi.org/10.1115/detc2020-22124","url":null,"abstract":"\u0000 The designers of mechanical products are generally not experts in machining. Therefore, they often design parts with inherent machining difficulties. Although various design for manufacturability tools have been developed to avoid such problems, their use in practice remains limited due to their lack of versatility. We develop a novel piece of software that can automatically detect difficult-to-machine shapes in a part. Using this software, the designer can determine which shapes are difficult to produce using conventional cutting by themselves, and can modify the shape on the spot. In the Internet-based part manufacturing business, the same software can be used to check whether the given part can be produced using the standard milling operations predetermined in a company. Our system is based on “milling simulation”. It detects any shapes that cannot be produced using the prepared cutting tools by executing the milling simulations with the tools, and then visualizing shapes that remain unmachined after all simulations. In this study, the acceleration of the processing is realized using graphics processing unit technology, and it is possible to extract difficult-to-machine shapes in several minutes using a standard PC.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130002292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Praveen Kumare Gopalakrishnan, John Hall, S. Behdad
Waste tracking is becoming an important concern for developed countries as well as developing regions, where municipalities aim to assure proper waste management considering environmental and economic objectives. Waste tracking is important not only for a transparent reporting system compatible with environmental regulations but also for economically viable waste collection and recovery solutions. In this paper, a waste tracking system based on the blockchain technology is introduced where different entities involved in the system will be able to retrieve required data from the platform and decide on their level of contributions. The conventional technologies do not provide a sufficient level of transparency and coordination among different entities. With the introduction of blockchain as a tamper-proof technology, municipalities can enhance the efficiency of their waste management efforts. The proposed blockchain technology can connect proper stakeholders towards collaboration and sharing information. The concept of a smart contract for waste management is discussed and further, a decision-making framework is developed to guide users of the system select proper services available to them, depending on the level of data sharing, cost, reliability, and the security level that they expect from the system.
{"title":"A Blockchain-Based Traceability System for Waste Management in Smart Cities","authors":"Praveen Kumare Gopalakrishnan, John Hall, S. Behdad","doi":"10.1115/detc2020-22553","DOIUrl":"https://doi.org/10.1115/detc2020-22553","url":null,"abstract":"\u0000 Waste tracking is becoming an important concern for developed countries as well as developing regions, where municipalities aim to assure proper waste management considering environmental and economic objectives. Waste tracking is important not only for a transparent reporting system compatible with environmental regulations but also for economically viable waste collection and recovery solutions. In this paper, a waste tracking system based on the blockchain technology is introduced where different entities involved in the system will be able to retrieve required data from the platform and decide on their level of contributions. The conventional technologies do not provide a sufficient level of transparency and coordination among different entities. With the introduction of blockchain as a tamper-proof technology, municipalities can enhance the efficiency of their waste management efforts. The proposed blockchain technology can connect proper stakeholders towards collaboration and sharing information. The concept of a smart contract for waste management is discussed and further, a decision-making framework is developed to guide users of the system select proper services available to them, depending on the level of data sharing, cost, reliability, and the security level that they expect from the system.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134482355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of machine tools trending toward high precision, intelligence, multi-axis, and high speed, the improvement of the processing performance and rigidity of the machine is considerably important. The objective of this study is to design of a high-speed five-axis moving-column machine tool and perform structural analysis and optimization. We study the static and dynamic characteristics of the five-axis machine tool, design and improve the mechanical structure, and optimize the structural configuration of the machine. The entire machine structure is further analyzed and enhanced to improve its static and dynamic characteristics, including static rigidity, modal, transient, and spectral response characteristics. The static and dynamic characteristics of the machine structure directly affect the machine processing performance, and further affect the work piece precision machined by the tool. Through this study, the design technology for speed, accuracy, and surface roughness of the machine tool are further improved.
{"title":"Optimized Design and Performance Study of High Speed Five-Axis Machine Tools","authors":"T. Chan, Jyun-Sian Yang","doi":"10.1115/detc2020-22253","DOIUrl":"https://doi.org/10.1115/detc2020-22253","url":null,"abstract":"\u0000 With the development of machine tools trending toward high precision, intelligence, multi-axis, and high speed, the improvement of the processing performance and rigidity of the machine is considerably important.\u0000 The objective of this study is to design of a high-speed five-axis moving-column machine tool and perform structural analysis and optimization. We study the static and dynamic characteristics of the five-axis machine tool, design and improve the mechanical structure, and optimize the structural configuration of the machine. The entire machine structure is further analyzed and enhanced to improve its static and dynamic characteristics, including static rigidity, modal, transient, and spectral response characteristics. The static and dynamic characteristics of the machine structure directly affect the machine processing performance, and further affect the work piece precision machined by the tool. Through this study, the design technology for speed, accuracy, and surface roughness of the machine tool are further improved.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132521007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Current research and literature lack the discussion of how production automation is introduced to existing lines from the perspective of change management. This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen semi-structured interviews of eight engineers from three functional backgrounds, a process model was created to understand how the company of study introduced a new automation system into their existing production line, while also noting obstacles identified in the process. This process model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in literature to understand critical elements found within change management. The process that was revealed in the case study appeared to contain some elements of a design process as compared to traditional change management processes found in literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the objective analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the process.
{"title":"Alignment of a Collaborative Resistance Model With a Change Management Process in Industry: A Case Study on Production Automation","authors":"Nicole Zero, J. Summers","doi":"10.1115/detc2020-22183","DOIUrl":"https://doi.org/10.1115/detc2020-22183","url":null,"abstract":"\u0000 Current research and literature lack the discussion of how production automation is introduced to existing lines from the perspective of change management. This paper presents a case study conducted to understand the change management process for a large-scale automation implementation in a manufacturing environment producing highly complex products. Through a series of fifteen semi-structured interviews of eight engineers from three functional backgrounds, a process model was created to understand how the company of study introduced a new automation system into their existing production line, while also noting obstacles identified in the process. This process model illustrates the duration, sequencing, teaming, and complexity of the project. This model is compared to other change process models found in literature to understand critical elements found within change management. The process that was revealed in the case study appeared to contain some elements of a design process as compared to traditional change management processes found in literature. Finally, a collaborative resistance model is applied to the process model to identify and estimate the resistance for each task in the process. Based on the objective analysis of the collaborative situations, the areas of highest resistance are identified. By comparing the resistance model to the interview data, the results show that the resistance model does identify the challenges found in interviews. This means that the resistance model has the potential to identify obstacles within the process and open the opportunity to mitigate those challenges before they are encountered within the process.","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114374720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johan Thoft Krogshave, Till Boettjer, Devarajan Ramanujan
This paper discusses a method for machine-specific energy estimation in milling processes using the unit process life cycle inventory (UPLCI) model. To this end, we develop a standard methodology for constructing an adjusted UPLCI model that includes adjustment factors for uncertainties in machine tool specifications and the specific cutting energy of a workpiece material. The adjustment factors are calculated through experimental measurement of energy consumption for a standard test part on a specific machine tool. To validate the adjusted UPLCI model, we conducted a case study which experimentally measured the energy consumption for machining three parts made of Aluminum 6082 on a Chevalier QP2040-L three-axis vertical milling machine. Results show that the UPLCI model consistently overestimated the total energy consumption for machining the three parts and had significant estimation errors (314%, 499%, 286%). The largest sources of error in the UPLCI model were from overestimating the idle and basic power consumption of the machine tool. The adjusted UPLCI model significantly reduced the estimation errors for the same tests (27%, 0.3%, 36%).
{"title":"Machine-Specific Energy Estimation Using the Unit Process Life Cycle Inventory (UPLCI) Model","authors":"Johan Thoft Krogshave, Till Boettjer, Devarajan Ramanujan","doi":"10.1115/detc2020-22483","DOIUrl":"https://doi.org/10.1115/detc2020-22483","url":null,"abstract":"\u0000 This paper discusses a method for machine-specific energy estimation in milling processes using the unit process life cycle inventory (UPLCI) model. To this end, we develop a standard methodology for constructing an adjusted UPLCI model that includes adjustment factors for uncertainties in machine tool specifications and the specific cutting energy of a workpiece material. The adjustment factors are calculated through experimental measurement of energy consumption for a standard test part on a specific machine tool. To validate the adjusted UPLCI model, we conducted a case study which experimentally measured the energy consumption for machining three parts made of Aluminum 6082 on a Chevalier QP2040-L three-axis vertical milling machine. Results show that the UPLCI model consistently overestimated the total energy consumption for machining the three parts and had significant estimation errors (314%, 499%, 286%). The largest sources of error in the UPLCI model were from overestimating the idle and basic power consumption of the machine tool. The adjusted UPLCI model significantly reduced the estimation errors for the same tests (27%, 0.3%, 36%).","PeriodicalId":131252,"journal":{"name":"Volume 6: 25th Design for Manufacturing and the Life Cycle Conference (DFMLC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115751744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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：