This research demonstrates how the Decision Based Design (DBD) approach can be used for determining a system’s lifecycle value when including excess. Prior research has shown that excess (the degree to which a component or attribute is sized beyond the minimum required to support the initially fielded system) can reduce the cost of changing a system. Theoretically, excess inhibits change propagation within a system and could be strategically added to increase the value of that system. Including excess, however, also adds cost and potentially impacts system performance. Prior research has not quantitatively linked excess as a means of limiting change propagation to system lifecycle value. This work advances the existing literature by considering how excess is imbedded in a system and what impact excess has on the system’s total value. After being introduced, the method is demonstrated on a desktop computer example. Results from the study are used to show how decisions about power supply capacity can be optimized by incorporating excess to achieve flexibility.
{"title":"An Excess Based Approach to Change Propagation","authors":"D. Long, S. Ferguson","doi":"10.1115/detc2019-98404","DOIUrl":"https://doi.org/10.1115/detc2019-98404","url":null,"abstract":"\u0000 This research demonstrates how the Decision Based Design (DBD) approach can be used for determining a system’s lifecycle value when including excess. Prior research has shown that excess (the degree to which a component or attribute is sized beyond the minimum required to support the initially fielded system) can reduce the cost of changing a system. Theoretically, excess inhibits change propagation within a system and could be strategically added to increase the value of that system. Including excess, however, also adds cost and potentially impacts system performance. Prior research has not quantitatively linked excess as a means of limiting change propagation to system lifecycle value. This work advances the existing literature by considering how excess is imbedded in a system and what impact excess has on the system’s total value. After being introduced, the method is demonstrated on a desktop computer example. Results from the study are used to show how decisions about power supply capacity can be optimized by incorporating excess to achieve flexibility.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114602115","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}
Cosima du Pasquier, Pascal Koller, T. Stanković, K. Shea
With advances in 3D printing and digital fabrication an opportunity is presented to realize highly customized designs whose shape can change and adapt to facilitate their functionality. A computational design method to determine the configuration of 2D pneumatic shape morphing lattices using a direct search method is implemented and assessed. The method is tested using a Kagome unit cell lattice structure, which is particularly well suited for shape morphing. To achieve shape change, beams are replaced by linear actuators such as those found in pneumatic 4D printing, whose number and placement are optimized to replicate a given target shape. The actuator placement and deformation accuracy are given for four main curvature changes: linear, convex, concave and the transition from one to the other. The results are assessed in terms accuracy of deformation and computational effort. It is shown that the method proposed produces structures that can replicate complex shape changes within 1% of the desired shape. Reducing the number of actuators for robustness purposes is shown to affect the results minimally.
{"title":"Computational Design of Active Lattice Structures for 4D Printed Pneumatic Shape Morphing","authors":"Cosima du Pasquier, Pascal Koller, T. Stanković, K. Shea","doi":"10.1115/detc2019-97775","DOIUrl":"https://doi.org/10.1115/detc2019-97775","url":null,"abstract":"\u0000 With advances in 3D printing and digital fabrication an opportunity is presented to realize highly customized designs whose shape can change and adapt to facilitate their functionality. A computational design method to determine the configuration of 2D pneumatic shape morphing lattices using a direct search method is implemented and assessed. The method is tested using a Kagome unit cell lattice structure, which is particularly well suited for shape morphing. To achieve shape change, beams are replaced by linear actuators such as those found in pneumatic 4D printing, whose number and placement are optimized to replicate a given target shape. The actuator placement and deformation accuracy are given for four main curvature changes: linear, convex, concave and the transition from one to the other. The results are assessed in terms accuracy of deformation and computational effort. It is shown that the method proposed produces structures that can replicate complex shape changes within 1% of the desired shape. Reducing the number of actuators for robustness purposes is shown to affect the results minimally.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127301643","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}
Darshan Yadav, D. Long, Beshoy Morkos, S. Ferguson
A widely held belief among design practitioners is that an ideal design solution is the one that meets all the requirements while minimizing surplus cost incurred by exceeding requirements. In this research, we challenge this notion by exploring if providing design “excess”, the ability of a solution to exceed certain requirements, can increase the value of a solution to its end users. A case study is performed in the video game industry to explore if design excess is prevalent and its impact on the industry. This study is performed by examining various PC builds (budget, mid-range, and high-end dream) and gaming consoles (Microsoft Xbox and Sony PlayStation) over an 18-year period. Based on a thorough investigation of video game requirements and capacity of different hardware, we find that design excess has existed in computer hardware and is intentionally used as a design property. The results indicate that mid-range solution provide the greatest value to its customers. Further, PC excess based value is adjusted during years when consoles are released. Using measurements of excess, this study also reveals a shift in technology push versus pull that occurs during the mid-2000s and is observable through the lens of system excess.
{"title":"Estimating the Value of Excess: A Case Study of Gaming Computers, Consoles and the Video Game Industry","authors":"Darshan Yadav, D. Long, Beshoy Morkos, S. Ferguson","doi":"10.1115/detc2019-98428","DOIUrl":"https://doi.org/10.1115/detc2019-98428","url":null,"abstract":"\u0000 A widely held belief among design practitioners is that an ideal design solution is the one that meets all the requirements while minimizing surplus cost incurred by exceeding requirements. In this research, we challenge this notion by exploring if providing design “excess”, the ability of a solution to exceed certain requirements, can increase the value of a solution to its end users. A case study is performed in the video game industry to explore if design excess is prevalent and its impact on the industry. This study is performed by examining various PC builds (budget, mid-range, and high-end dream) and gaming consoles (Microsoft Xbox and Sony PlayStation) over an 18-year period. Based on a thorough investigation of video game requirements and capacity of different hardware, we find that design excess has existed in computer hardware and is intentionally used as a design property. The results indicate that mid-range solution provide the greatest value to its customers. Further, PC excess based value is adjusted during years when consoles are released. Using measurements of excess, this study also reveals a shift in technology push versus pull that occurs during the mid-2000s and is observable through the lens of system excess.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127845385","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}
Product service systems (PSS), such as DVD rental stations or the subway, face a unique problem slowing their adoption and growth: they are uniquely dependent upon timely or expensive user data for system planning, yet user datasets are only accurate for a small part of the entire PSS. Thus, methods to use the available data effectively and use data collected in one portion of a PSS for system design in another portion could transform PSS design. PSS allow customers to purchase use of a product rather than the product itself, resulting in improved environmental sustainability. The central question examined by this work is: how can designers compensate for situations where the design environment has changed and limited user data is available to inform demand estimations? Our hypothesis is that publicly available socio-demographic and environmental variables can be used to estimate the demand outside of the boundaries previously constrained by available user data. This approach was validated by applying multivariable regressions to a major Bike Share System (BSS) Expansion, outperforming the methods utilized by the BSS operators. The approach is tested in four different design scenarios. When examining all 174 stations added in 2015, our approach shows a moderate correlation with the ideal ordering (Rho = .566, Stations = 174, p < .01), while the implemented operator ordering was only weakly correlated (Rho = .334, Stations = 174, p < .01). This work demonstrates a partial solution to the problem of transforming available user data into demand for new situations.
产品服务系统(PSS)，如DVD出租站或地铁，面临着一个减缓其采用和发展的独特问题:它们独特地依赖于及时或昂贵的用户数据进行系统规划，然而用户数据集仅对整个PSS的一小部分准确。因此，有效使用可用数据的方法以及将PSS的一部分收集的数据用于另一部分的系统设计可以改变PSS设计。PSS允许客户购买产品的使用而不是产品本身，从而提高了环境的可持续性。这项工作研究的中心问题是:设计师如何补偿设计环境已经改变的情况和有限的用户数据可用来通知需求估计?我们的假设是，可以使用公开可用的社会人口和环境变量来估计先前受可用用户数据约束的边界之外的需求。通过将多变量回归应用于大型共享单车系统(BSS)的扩展，验证了该方法的有效性，优于BSS运营商使用的方法。该方法在四种不同的设计场景中进行了测试。在对2015年新增的全部174个台站进行检验时，我们的方法显示出与理想排序的中度相关性(Rho = .566, stations = 174, p < 0.01)，而实现的运营商排序仅弱相关(Rho = .334, stations = 174, p < 0.01)。这项工作展示了将可用用户数据转换为新情况需求问题的部分解决方案。
{"title":"Product Service System Design in New Situations: Prediction of Demand Surfaces From Environment","authors":"B. C. Watson, Cassandra Telenko","doi":"10.1115/detc2019-97691","DOIUrl":"https://doi.org/10.1115/detc2019-97691","url":null,"abstract":"\u0000 Product service systems (PSS), such as DVD rental stations or the subway, face a unique problem slowing their adoption and growth: they are uniquely dependent upon timely or expensive user data for system planning, yet user datasets are only accurate for a small part of the entire PSS. Thus, methods to use the available data effectively and use data collected in one portion of a PSS for system design in another portion could transform PSS design. PSS allow customers to purchase use of a product rather than the product itself, resulting in improved environmental sustainability. The central question examined by this work is: how can designers compensate for situations where the design environment has changed and limited user data is available to inform demand estimations? Our hypothesis is that publicly available socio-demographic and environmental variables can be used to estimate the demand outside of the boundaries previously constrained by available user data. This approach was validated by applying multivariable regressions to a major Bike Share System (BSS) Expansion, outperforming the methods utilized by the BSS operators. The approach is tested in four different design scenarios. When examining all 174 stations added in 2015, our approach shows a moderate correlation with the ideal ordering (Rho = .566, Stations = 174, p < .01), while the implemented operator ordering was only weakly correlated (Rho = .334, Stations = 174, p < .01). This work demonstrates a partial solution to the problem of transforming available user data into demand for new situations.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121456175","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. Amaria, Ryan Nguyen, Joshua A. Davison, Souma Chowdhury, John F. Hall
Over the past several years, microgrids have been setup in remote villages in developing countries such as India, Kenya and China to boost the standards of living of the less privileged citizens, mostly by private companies. However, these systems succumb to increase in demand and maintenance issues over time. A method for scaling the capacity of solar powered microgrids is presented in this paper. The scaling is based on both the needs of the owner and those of the consumers. Data acquired from rural villages characterizes the electrical use with respect to time. Further, it employees a Long-Short Term Memory (LSTM) deep learning model that can help the owner predict future demand trends. This is followed by a model to determine the optimum increase in capacity required to meet the predicted demand. The model is based on empowering the owner to make informed decisions and the equity of energy distribution is the key motivation for this paper. The models are applied to a village in Eastern India to test its applicability. Acknowledging the highly varying nature of demand for electricity and its applications, we propose a rule-based adaptive power management strategy which can be tailored specifically in accordance to the preference of the communities. This will ensure a fair distribution of power for everyone using the system, thereby making it applicable anywhere in the world. We propose to incorporate social and demographic conditions of the user in the optimization to ensure that the profit of the owner does not outweigh the needs of the users.
{"title":"Optimization Model for Owner-Based Microgrids Using LSTM Predicted Demand for Rural Development","authors":"A. Amaria, Ryan Nguyen, Joshua A. Davison, Souma Chowdhury, John F. Hall","doi":"10.1115/detc2019-97964","DOIUrl":"https://doi.org/10.1115/detc2019-97964","url":null,"abstract":"\u0000 Over the past several years, microgrids have been setup in remote villages in developing countries such as India, Kenya and China to boost the standards of living of the less privileged citizens, mostly by private companies. However, these systems succumb to increase in demand and maintenance issues over time. A method for scaling the capacity of solar powered microgrids is presented in this paper. The scaling is based on both the needs of the owner and those of the consumers. Data acquired from rural villages characterizes the electrical use with respect to time. Further, it employees a Long-Short Term Memory (LSTM) deep learning model that can help the owner predict future demand trends. This is followed by a model to determine the optimum increase in capacity required to meet the predicted demand. The model is based on empowering the owner to make informed decisions and the equity of energy distribution is the key motivation for this paper. The models are applied to a village in Eastern India to test its applicability. Acknowledging the highly varying nature of demand for electricity and its applications, we propose a rule-based adaptive power management strategy which can be tailored specifically in accordance to the preference of the communities. This will ensure a fair distribution of power for everyone using the system, thereby making it applicable anywhere in the world. We propose to incorporate social and demographic conditions of the user in the optimization to ensure that the profit of the owner does not outweigh the needs of the users.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128194709","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}
Decision-makers often rely on heuristics and experience to make complex decisions in the industrial context. Often, integrating implicit or expert knowledge as well as uncertainties can lead to decisions that are not necessarily the best ones. Moreover, in engineering design, the decision-making approaches focus on the product itself and do not investigate the necessary effort that is needed to gather additional data in order to devise more precise decision-making models. In our research, we propose to integrate this estimation of additional effort needed for data gathering and decision-making refinement in order to support design teams. This research has been conducted in collaboration with a major car manufacturing company, and in particular in the development process through Modeling and Simulation. The objective is to propose a decision-making model that integrates data-gathering estimation, hence integrating also the estimation of postponing one decision. A decision problem model based upon expected utility combined with the value of information theory is proposed to address this issue. The model has been developed and tested on 4 case studies. We define a decision support framework by integrating the model into a tool and by proposing roles in the decision-making process. We finally present its application on a concrete example.
{"title":"A Proposal for a Decision Support Framework to Solve Design Problems in the Automotive Industry","authors":"T. Sissoko, M. Jankovic, C. Paredis, E. Landel","doi":"10.1115/detc2019-98035","DOIUrl":"https://doi.org/10.1115/detc2019-98035","url":null,"abstract":"\u0000 Decision-makers often rely on heuristics and experience to make complex decisions in the industrial context. Often, integrating implicit or expert knowledge as well as uncertainties can lead to decisions that are not necessarily the best ones. Moreover, in engineering design, the decision-making approaches focus on the product itself and do not investigate the necessary effort that is needed to gather additional data in order to devise more precise decision-making models. In our research, we propose to integrate this estimation of additional effort needed for data gathering and decision-making refinement in order to support design teams. This research has been conducted in collaboration with a major car manufacturing company, and in particular in the development process through Modeling and Simulation. The objective is to propose a decision-making model that integrates data-gathering estimation, hence integrating also the estimation of postponing one decision. A decision problem model based upon expected utility combined with the value of information theory is proposed to address this issue. The model has been developed and tested on 4 case studies. We define a decision support framework by integrating the model into a tool and by proposing roles in the decision-making process. We finally present its application on a concrete example.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134116129","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}
R. Bostanabad, Yu-Chin Chan, Liwei Wang, Ping Zhu, Wei Chen
Our main contribution is to introduce a novel method for Gaussian process (GP) modeling of massive datasets. The key idea is to build an ensemble of independent GPs that use the same hyperparameters but distribute the entire training dataset among themselves. This is motivated by our observation that estimates of the GP hyperparameters change negligibly as the size of the training data exceeds a certain level, which can be found in a systematic way. For inference, the predictions from all GPs in the ensemble are pooled to efficiently exploit the entire training dataset for prediction. We name our modeling approach globally approximate Gaussian process (GAGP), which, unlike most largescale supervised learners such as neural networks and trees, is easy to fit and can interpret the model behavior. These features make it particularly useful in engineering design with big data. We use analytical examples to demonstrate that GAGP achieves very high predictive power that matches or exceeds that of state-of-the-art machine learning methods. We illustrate the application of GAGP in engineering design with a problem on data-driven metamaterials design where it is used to link reduced-dimension geometrical descriptors of unit cells and their properties. Searching for new unit cell designs with desired properties is then accomplished by employing GAGP in inverse optimization.
{"title":"Gaussian Process Emulation for Big Data in Data-Driven Metamaterials Design","authors":"R. Bostanabad, Yu-Chin Chan, Liwei Wang, Ping Zhu, Wei Chen","doi":"10.1115/detc2019-98027","DOIUrl":"https://doi.org/10.1115/detc2019-98027","url":null,"abstract":"\u0000 Our main contribution is to introduce a novel method for Gaussian process (GP) modeling of massive datasets. The key idea is to build an ensemble of independent GPs that use the same hyperparameters but distribute the entire training dataset among themselves. This is motivated by our observation that estimates of the GP hyperparameters change negligibly as the size of the training data exceeds a certain level, which can be found in a systematic way. For inference, the predictions from all GPs in the ensemble are pooled to efficiently exploit the entire training dataset for prediction. We name our modeling approach globally approximate Gaussian process (GAGP), which, unlike most largescale supervised learners such as neural networks and trees, is easy to fit and can interpret the model behavior. These features make it particularly useful in engineering design with big data. We use analytical examples to demonstrate that GAGP achieves very high predictive power that matches or exceeds that of state-of-the-art machine learning methods. We illustrate the application of GAGP in engineering design with a problem on data-driven metamaterials design where it is used to link reduced-dimension geometrical descriptors of unit cells and their properties. Searching for new unit cell designs with desired properties is then accomplished by employing GAGP in inverse optimization.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115321274","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}
Human- or expert-generated records that describe the behavior of engineered systems over a period of time can be useful for statistical learning techniques like pattern detection or output prediction. However, such data often assumes familiarity of a reader with the relationships between entities within the system — that is, knowledge of the system’s structure. This required, but unrecorded “tacit” knowledge makes it difficult to reliably learn patterns of system behavior using statistical modeling techniques on these written records. Part of this difficulty stems from a lack of good models for how engineers generate written records of a system, given their expertise, since they often create such records under time pressure using shorthand notation or internal jargon. In this paper, we model the process of maintenance work order creation as a modified semantic fluency task, to build a probabilistic generative model that can uncover underlying relationships between entities referenced within a complex system. Compared to more traditional similarity-metric-based methods for structure recovery, we directly model a possible cognitive process by which technicians may record work-orders. Mathematically, we represent this as a censored local random walk over a latent network structure representing tacit engineering knowledge. This allows us to recover implied engineering knowledge about system structure by processing written records. Additionally, we show that our model leads to improved generative capabilities for synthesizing plausible data.
{"title":"Using Semantic Fluency Models Improves Network Reconstruction Accuracy of Tacit Engineering Knowledge","authors":"Thurston Sexton, M. Fuge","doi":"10.1115/DETC2019-98429","DOIUrl":"https://doi.org/10.1115/DETC2019-98429","url":null,"abstract":"\u0000 Human- or expert-generated records that describe the behavior of engineered systems over a period of time can be useful for statistical learning techniques like pattern detection or output prediction. However, such data often assumes familiarity of a reader with the relationships between entities within the system — that is, knowledge of the system’s structure. This required, but unrecorded “tacit” knowledge makes it difficult to reliably learn patterns of system behavior using statistical modeling techniques on these written records. Part of this difficulty stems from a lack of good models for how engineers generate written records of a system, given their expertise, since they often create such records under time pressure using shorthand notation or internal jargon. In this paper, we model the process of maintenance work order creation as a modified semantic fluency task, to build a probabilistic generative model that can uncover underlying relationships between entities referenced within a complex system. Compared to more traditional similarity-metric-based methods for structure recovery, we directly model a possible cognitive process by which technicians may record work-orders. Mathematically, we represent this as a censored local random walk over a latent network structure representing tacit engineering knowledge. This allows us to recover implied engineering knowledge about system structure by processing written records. Additionally, we show that our model leads to improved generative capabilities for synthesizing plausible data.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134620981","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}
While advances in metals additive manufacturing continue to make additive a viable option in more scenarios, these processes are generally slower and more expensive than subtractive methods, like machining. The combination of both additive and subtractive, often called hybrid manufacturing, can be used to get the benefits of both processes, while reducing cost. However, dividing a part into the most cost effective additive and subtractive features is often time-consuming and non-intuitive. In this paper, we present a new approach that optimizes the type, size, and position of a substrate within a part. The resulting hybrid manufacturing configuration enables engineers to reach the most cost-effective compromise between additive and machining. A fully implemented method has been developed and tested on several realistic engineering parts. The results are intuitively useful and push the state-of-the-art forward in generating hybrid manufacturing process plans.
{"title":"Substrate Optimization for Hybrid Manufacturing","authors":"Brandon R. Massoni, M. Campbell","doi":"10.1115/detc2019-98068","DOIUrl":"https://doi.org/10.1115/detc2019-98068","url":null,"abstract":"\u0000 While advances in metals additive manufacturing continue to make additive a viable option in more scenarios, these processes are generally slower and more expensive than subtractive methods, like machining. The combination of both additive and subtractive, often called hybrid manufacturing, can be used to get the benefits of both processes, while reducing cost. However, dividing a part into the most cost effective additive and subtractive features is often time-consuming and non-intuitive. In this paper, we present a new approach that optimizes the type, size, and position of a substrate within a part. The resulting hybrid manufacturing configuration enables engineers to reach the most cost-effective compromise between additive and machining. A fully implemented method has been developed and tested on several realistic engineering parts. The results are intuitively useful and push the state-of-the-art forward in generating hybrid manufacturing process plans.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131800762","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}
Lattice Heat Transfer (LHT) structures provide superior structural support while improving the heat transfer coefficient through their high surface-to-volume ratios. By using current Additive Manufacturing (AM) technologies, LHT with highly complex structures is possible. In this study, the design concept of LHT is further improved by implementing a thermomechanical topology optimization method. With utilization of design-dependent heat source, the method can be applied to generate stiffer LHT structures under mechanical and thermomechanical loads, without decreasing their thermal performance; relative to a design made of a uniform LHT having the same mass fraction. Two numerical examples are presented to illustrate how to use the proposed approach to design LHT sections. The results show that the mechanical performance can be improved more than 50% compared to a uniform LHT with the same mass fraction, without decreasing the thermal performance. The method does not require a fluid mechanics model, thus it is computational effective and particularly suitable for the conceptual design stage. The resulting optimized lattice is made possible by utilizing additive manufacturing technologies.
{"title":"Thermomechanical Topology Optimization of Lattice Heat Transfer Structure Including Natural Convection and Design Dependent Heat Source","authors":"Tong Wu, Joel C. Najmon, A. Tovar","doi":"10.1115/detc2019-97628","DOIUrl":"https://doi.org/10.1115/detc2019-97628","url":null,"abstract":"\u0000 Lattice Heat Transfer (LHT) structures provide superior structural support while improving the heat transfer coefficient through their high surface-to-volume ratios. By using current Additive Manufacturing (AM) technologies, LHT with highly complex structures is possible. In this study, the design concept of LHT is further improved by implementing a thermomechanical topology optimization method. With utilization of design-dependent heat source, the method can be applied to generate stiffer LHT structures under mechanical and thermomechanical loads, without decreasing their thermal performance; relative to a design made of a uniform LHT having the same mass fraction. Two numerical examples are presented to illustrate how to use the proposed approach to design LHT sections. The results show that the mechanical performance can be improved more than 50% compared to a uniform LHT with the same mass fraction, without decreasing the thermal performance. The method does not require a fluid mechanics model, thus it is computational effective and particularly suitable for the conceptual design stage. The resulting optimized lattice is made possible by utilizing additive manufacturing technologies.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121552166","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}
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