Real-world designs usually consist of parts with hierarchical dependencies, i.e., the geometry of one component (a child shape) is dependent on another (a parent shape). We propose a method for synthesizing this type of design. It decomposes the problem of synthesizing the whole design into synthesizing each component separately but keeping the inter-component dependencies satisfied. This method constructs a two-level generative adversarial network to train two generative models for parent and child shapes, respectively. We then use the trained generative models to synthesize or explore parent and child shapes separately via a parent latent representation and infinite child latent representations, each conditioned on a parent shape. We evaluate and discuss the disentanglement and consistency of latent representations obtained by this method. We show that shapes change consistently along any direction in the latent space. This property is desirable for design exploration over the latent space.
{"title":"Synthesizing Designs With Inter-Part Dependencies Using Hierarchical Generative Adversarial Networks","authors":"Wei Chen, A. Jeyaseelan, M. Fuge","doi":"10.1115/DETC2018-85339","DOIUrl":"https://doi.org/10.1115/DETC2018-85339","url":null,"abstract":"Real-world designs usually consist of parts with hierarchical dependencies, i.e., the geometry of one component (a child shape) is dependent on another (a parent shape). We propose a method for synthesizing this type of design. It decomposes the problem of synthesizing the whole design into synthesizing each component separately but keeping the inter-component dependencies satisfied. This method constructs a two-level generative adversarial network to train two generative models for parent and child shapes, respectively. We then use the trained generative models to synthesize or explore parent and child shapes separately via a parent latent representation and infinite child latent representations, each conditioned on a parent shape. We evaluate and discuss the disentanglement and consistency of latent representations obtained by this method. We show that shapes change consistently along any direction in the latent space. This property is desirable for design exploration over the latent space.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"393 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988720","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}
Phyo Htet Hein, Nathaniel Voris, J. Dai, Beshoy Morkos
Design for Assembly (DFA) time estimation method developed by G. Boothroyd and P. Dewhurst allows for estimating the assembly time of artifacts based on analysis of component features using handling and insertion tables by an assembler, who is assumed to assemble the artifact one-part-at-a-time. Using the tables, each component is assigned an assembly time which is based on the time required for the assembler to manipulate (handling time) and the time required for it to interface with the rest of the components (insertion time). Using this assembly time and the ideal assembly time (i.e. the absolute time it takes to assemble the artifact, assuming each component takes the ideal time of three seconds to handle and insert), this method allows to calculate the efficiency of a design’s assembly process. Another tool occasionally used in Design for Manufacturing (DFM) is Failure Modes and Effects Analysis (FMEA). FMEA is used to evaluate and document failure modes and their impact on system performance. Each failure mode is ranked based on its severity, occurrence, and detectability scores, and corrective actions that can be taken to control risk items. FMEA scores of components can address the manufacturing operations and how much effort should be put into each specific component. In this paper, the authors attempt to answer the following two research questions (RQs) to determine the relationships between FMEA scores and the DFA assembly time to investigate if part failure’s severity, occurrence, and detectability can be estimated if handling time and insertion time are known. RQ (1): Can DFA metrics (handling time and insertion time) be utilized to estimate Failure Mode and Effects scores (severity, occurrence, and detectability)? RQ (2): How does each response metric relate to predictor metrics (positive, negative, or no relationship)? This is accomplished by performing Boothroyd and Dewhurst’s DFA time estimation and FMEA on select set of simple products. Since DFA metrics are based on combination of designer’s subjectivity and part’s geometric specifications and FMEA scores are based only on designer’s subjectivity, this paper attempts to estimate part failure severity, occurrence, and detectability less subjectively by using the handling time and insertion time. This will also allow for earlier and faster acquisition of potential part failure information for use in design and manufacturing processes.
{"title":"Identifying Failure Modes and Effects Through Design for Assembly Analysis","authors":"Phyo Htet Hein, Nathaniel Voris, J. Dai, Beshoy Morkos","doi":"10.1115/DETC2018-86314","DOIUrl":"https://doi.org/10.1115/DETC2018-86314","url":null,"abstract":"Design for Assembly (DFA) time estimation method developed by G. Boothroyd and P. Dewhurst allows for estimating the assembly time of artifacts based on analysis of component features using handling and insertion tables by an assembler, who is assumed to assemble the artifact one-part-at-a-time. Using the tables, each component is assigned an assembly time which is based on the time required for the assembler to manipulate (handling time) and the time required for it to interface with the rest of the components (insertion time). Using this assembly time and the ideal assembly time (i.e. the absolute time it takes to assemble the artifact, assuming each component takes the ideal time of three seconds to handle and insert), this method allows to calculate the efficiency of a design’s assembly process. Another tool occasionally used in Design for Manufacturing (DFM) is Failure Modes and Effects Analysis (FMEA). FMEA is used to evaluate and document failure modes and their impact on system performance. Each failure mode is ranked based on its severity, occurrence, and detectability scores, and corrective actions that can be taken to control risk items. FMEA scores of components can address the manufacturing operations and how much effort should be put into each specific component. In this paper, the authors attempt to answer the following two research questions (RQs) to determine the relationships between FMEA scores and the DFA assembly time to investigate if part failure’s severity, occurrence, and detectability can be estimated if handling time and insertion time are known. RQ (1): Can DFA metrics (handling time and insertion time) be utilized to estimate Failure Mode and Effects scores (severity, occurrence, and detectability)? RQ (2): How does each response metric relate to predictor metrics (positive, negative, or no relationship)? This is accomplished by performing Boothroyd and Dewhurst’s DFA time estimation and FMEA on select set of simple products. Since DFA metrics are based on combination of designer’s subjectivity and part’s geometric specifications and FMEA scores are based only on designer’s subjectivity, this paper attempts to estimate part failure severity, occurrence, and detectability less subjectively by using the handling time and insertion time. This will also allow for earlier and faster acquisition of potential part failure information for use in design and manufacturing processes.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130813390","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 concept of resilience has emerged from various domains to address how systems, people and organizations can handle uncertainty. This paper presents a method to improve the resilience of an engineering system by maximizing the system economic lifecycle value, as measured by Net Present Value, under uncertainty. The method is applied to a Waste-to-Energy system based in Singapore and the impact of combining robust and flexible design strategies to improve resilience are discussed. Robust strategies involve optimizing the initial capacity of the system while Bayesian Networks are implemented to choose the flexible expansion strategy that should be deployed given the current observations of demand uncertainties. The Bayesian Network shows promise and should be considered further where decisions are more complex. Resilience is further assessed by varying the volatility of the stochastic demand in the simulation. Increasing volatility generally made the system perform worse since not all demand could be converted to revenue due to capacity constraints. Flexibility shows increased value compared to a fixed design. However, when the system is allowed to upgrade too often, the costs of implementation negates the revenue increase. The better design is to have a high initial capacity, such that there is less restriction on the demand with two or three expansions.
{"title":"Towards the Design of Resilient Waste-to-Energy Systems Using Bayesian Networks","authors":"W. H. J. Mak, M. Cardin, Liu Ziqi, P. Clarkson","doi":"10.1115/DETC2018-85452","DOIUrl":"https://doi.org/10.1115/DETC2018-85452","url":null,"abstract":"The concept of resilience has emerged from various domains to address how systems, people and organizations can handle uncertainty. This paper presents a method to improve the resilience of an engineering system by maximizing the system economic lifecycle value, as measured by Net Present Value, under uncertainty. The method is applied to a Waste-to-Energy system based in Singapore and the impact of combining robust and flexible design strategies to improve resilience are discussed. Robust strategies involve optimizing the initial capacity of the system while Bayesian Networks are implemented to choose the flexible expansion strategy that should be deployed given the current observations of demand uncertainties. The Bayesian Network shows promise and should be considered further where decisions are more complex. Resilience is further assessed by varying the volatility of the stochastic demand in the simulation. Increasing volatility generally made the system perform worse since not all demand could be converted to revenue due to capacity constraints. Flexibility shows increased value compared to a fixed design. However, when the system is allowed to upgrade too often, the costs of implementation negates the revenue increase. The better design is to have a high initial capacity, such that there is less restriction on the demand with two or three expansions.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130827594","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}
In design process of a complex engineered system, studying the behavior of the system prior to manufacturing plays a key role to reduce cost of design and enhance the efficiency of the system during its lifecycle. To study the behavior of the system in the early design phase, it is required to model the characterization of the system and simulate the system’s behavior. The challenge is the fact that in early design stage, there is no or little information from the real system’s behavior, therefore there is not enough data to use to validate the model simulation and make sure that the model is representing the real system’s behavior appropriately. In this paper, we address this issue and propose methods to validate the model developed in the early design stage. First we propose a method based on FMEA and show how to quantify expert’s knowledge and validate the model simulation in the early design stage. Then, we propose a non-parametric technique to test if the observed behavior of one or more subsystems which currently exist, and the model simulation are the same. In addition, a local sensitivity analysis search tool is developed that helps the designers to focus on sensitive parts of the system in further design stages, particularly when mapping the conceptual model to a component model. We apply the proposed methods to validate the output of failure simulation developed in the early stage of designing a monopropellant propulsion system design.
{"title":"Model Validation in Early Phase of Designing Complex Engineered Systems","authors":"E. Keshavarzi, K. Goebel, I. Tumer, C. Hoyle","doi":"10.1115/detc2018-85137","DOIUrl":"https://doi.org/10.1115/detc2018-85137","url":null,"abstract":"In design process of a complex engineered system, studying the behavior of the system prior to manufacturing plays a key role to reduce cost of design and enhance the efficiency of the system during its lifecycle. To study the behavior of the system in the early design phase, it is required to model the characterization of the system and simulate the system’s behavior. The challenge is the fact that in early design stage, there is no or little information from the real system’s behavior, therefore there is not enough data to use to validate the model simulation and make sure that the model is representing the real system’s behavior appropriately. In this paper, we address this issue and propose methods to validate the model developed in the early design stage. First we propose a method based on FMEA and show how to quantify expert’s knowledge and validate the model simulation in the early design stage. Then, we propose a non-parametric technique to test if the observed behavior of one or more subsystems which currently exist, and the model simulation are the same. In addition, a local sensitivity analysis search tool is developed that helps the designers to focus on sensitive parts of the system in further design stages, particularly when mapping the conceptual model to a component model. We apply the proposed methods to validate the output of failure simulation developed in the early stage of designing a monopropellant propulsion system design.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131602406","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}
This paper extends a recently developed adjoint framework for an efficient overhang filtering to projection-based methods for overhang constraints. The developed approach offers a fast and efficient computational methodology and enables a minimum allowable self-supporting angle and a minimum allowable feature size within the formulation of the optimization problem, and designs components that can be manufactured without using support structures. The adjoint-based sensitivity formulation are derived to eliminate the computational intensities associated with the direct differentiation in the formulation of overhang constraints, which become prohibitive in large scale 2D and 3D problems. The developed formulation is tested on structural problems and numerical examples are provided to present efficiency of the proposed methodology.
{"title":"Projection-Based Overhang Constraints: Implementing an Efficient Adjoint Formulation for Sensitivity Analysis","authors":"Reza Behrou, James K. Guest, Andrew T. Gaynor","doi":"10.1115/DETC2018-86266","DOIUrl":"https://doi.org/10.1115/DETC2018-86266","url":null,"abstract":"This paper extends a recently developed adjoint framework for an efficient overhang filtering to projection-based methods for overhang constraints. The developed approach offers a fast and efficient computational methodology and enables a minimum allowable self-supporting angle and a minimum allowable feature size within the formulation of the optimization problem, and designs components that can be manufactured without using support structures. The adjoint-based sensitivity formulation are derived to eliminate the computational intensities associated with the direct differentiation in the formulation of overhang constraints, which become prohibitive in large scale 2D and 3D problems. The developed formulation is tested on structural problems and numerical examples are provided to present efficiency of the proposed methodology.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128425053","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}
Salar Safarkhani, Ilias Bilionis, Jitesh H. Panchal
Systems engineering processes coordinate the efforts of many individuals to design a complex system. However, the goals of the involved individuals do not necessarily align with the system-level goals. Everyone, including managers, systems engineers, subsystem engineers, component designers, and contractors, is self-interested. It is not currently understood how this discrepancy between organizational and personal goals affects the outcome of complex systems engineering processes. To answer this question, we need a systems engineering theory that accounts for human behavior. Such a theory can be ideally expressed as a dynamic hierarchical network game of incomplete information. The nodes of this network represent individual agents and the edges the transfer of information and incentives. All agents decide independently on how much effort they should devote to a delegated task by maximizing their expected utility; the expectation is over their beliefs about the actions of all other individuals and the moves of nature. An essential component of such a model is the quality function, defined as the map between an agent’s effort and the quality of their job outcome. In the economics literature, the quality function is assumed to be a linear function of effort with additive Gaussian noise. This simplistic assumption ignores two critical factors relevant to systems engineering: (1) the complexity of the design task, and (2) the problem-solving skills of the agent. Systems engineers establish their beliefs about these two factors through years of job experience. In this paper, we encode these beliefs in clear mathematical statements about the form of the quality function. Our approach proceeds in two steps: (1) we construct a generative stochastic model of the delegated task, and (2) we develop a reduced order representation suitable for use in a more extensive game-theoretic model of a systems engineering process. Focusing on the early design stages of a systems engineering process, we model the design task as a function maximization problem and, thus, we associate the systems engineer’s beliefs about the complexity of the task with their beliefs about the complexity of the function being maximized. Furthermore, we associate an agent’s problem solving-skills with the strategy they use to solve the underlying function maximization problem. We identify two agent types: “naïve” (follows a random search strategy) and “skillful” (follows a Bayesian global optimization strategy). Through an extensive simulation study, we show that the assumption of the linear quality function is only valid for small effort levels. In general, the quality function is an increasing, concave function with derivative and curvature that depend on the problem complexity and agent’s skills.
{"title":"Understanding the Effect of Task Complexity and Problem-Solving Skills on the Design Performance of Agents in Systems Engineering","authors":"Salar Safarkhani, Ilias Bilionis, Jitesh H. Panchal","doi":"10.1115/DETC2018-85941","DOIUrl":"https://doi.org/10.1115/DETC2018-85941","url":null,"abstract":"Systems engineering processes coordinate the efforts of many individuals to design a complex system. However, the goals of the involved individuals do not necessarily align with the system-level goals. Everyone, including managers, systems engineers, subsystem engineers, component designers, and contractors, is self-interested. It is not currently understood how this discrepancy between organizational and personal goals affects the outcome of complex systems engineering processes. To answer this question, we need a systems engineering theory that accounts for human behavior. Such a theory can be ideally expressed as a dynamic hierarchical network game of incomplete information. The nodes of this network represent individual agents and the edges the transfer of information and incentives. All agents decide independently on how much effort they should devote to a delegated task by maximizing their expected utility; the expectation is over their beliefs about the actions of all other individuals and the moves of nature. An essential component of such a model is the quality function, defined as the map between an agent’s effort and the quality of their job outcome. In the economics literature, the quality function is assumed to be a linear function of effort with additive Gaussian noise. This simplistic assumption ignores two critical factors relevant to systems engineering: (1) the complexity of the design task, and (2) the problem-solving skills of the agent. Systems engineers establish their beliefs about these two factors through years of job experience. In this paper, we encode these beliefs in clear mathematical statements about the form of the quality function. Our approach proceeds in two steps: (1) we construct a generative stochastic model of the delegated task, and (2) we develop a reduced order representation suitable for use in a more extensive game-theoretic model of a systems engineering process. Focusing on the early design stages of a systems engineering process, we model the design task as a function maximization problem and, thus, we associate the systems engineer’s beliefs about the complexity of the task with their beliefs about the complexity of the function being maximized. Furthermore, we associate an agent’s problem solving-skills with the strategy they use to solve the underlying function maximization problem. We identify two agent types: “naïve” (follows a random search strategy) and “skillful” (follows a Bayesian global optimization strategy). Through an extensive simulation study, we show that the assumption of the linear quality function is only valid for small effort levels. In general, the quality function is an increasing, concave function with derivative and curvature that depend on the problem complexity and agent’s skills.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130621940","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}
This paper presents a time-dependent reliability estimation method for engineering system based on machine learning and simulation method. Due to the stochastic nature of the environmental loads and internal incentive, the physics of failure for mechanical system is complex, and it is challenging to include uncertainties for the physical modeling of failure in the engineered system’s life cycle. In this paper, an efficient time-dependent reliability assessment framework for mechanical system is proposed using a machine learning algorithm considering stochastic dynamic loads in the mechanical system. Firstly, stochastic external loads of mechanical system are analyzed, and the finite element model is established. Secondly, the physics of failure mode of mechanical system at a time location is analyzed, and the distribution of time realization under each load condition is calculated. Then, the distribution of fatigue life can be obtained based on high-cycle fatigue theory. To reduce the calculation cost, a machine learning algorithm is utilized for physical modeling of failure by integrating uniform design and Gaussian process regression. The probabilistic fatigue life of gear transmission system under different load conditions can be calculated, and the time-varying reliability of mechanical system is further evaluated. Finally, numerical examples and the fatigue reliability estimation of gear transmission system is presented to demonstrate the effectiveness of the proposed method.
{"title":"A Time-Dependent Reliability Estimation Method Based on Gaussian Process Regression","authors":"Han Wang, Zhang Xiaoling, Huang Xiesi, Haiqing Li","doi":"10.1115/DETC2018-86294","DOIUrl":"https://doi.org/10.1115/DETC2018-86294","url":null,"abstract":"This paper presents a time-dependent reliability estimation method for engineering system based on machine learning and simulation method. Due to the stochastic nature of the environmental loads and internal incentive, the physics of failure for mechanical system is complex, and it is challenging to include uncertainties for the physical modeling of failure in the engineered system’s life cycle. In this paper, an efficient time-dependent reliability assessment framework for mechanical system is proposed using a machine learning algorithm considering stochastic dynamic loads in the mechanical system. Firstly, stochastic external loads of mechanical system are analyzed, and the finite element model is established. Secondly, the physics of failure mode of mechanical system at a time location is analyzed, and the distribution of time realization under each load condition is calculated. Then, the distribution of fatigue life can be obtained based on high-cycle fatigue theory. To reduce the calculation cost, a machine learning algorithm is utilized for physical modeling of failure by integrating uniform design and Gaussian process regression. The probabilistic fatigue life of gear transmission system under different load conditions can be calculated, and the time-varying reliability of mechanical system is further evaluated. Finally, numerical examples and the fatigue reliability estimation of gear transmission system is presented to demonstrate the effectiveness of the proposed method.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133396525","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}
Understanding and integrating a user’s decision-making process into design and implementation strategies for clean energy technologies may lead to higher product adoption rates and ultimately increased impacts, particularly for those products that require a change in habit or behavior. To evaluate the key attributes that formulate a user’s decision-making behavior to adopt a new clean technology, this study presents the application of the Theory of Planned Behavior, a method to quantify the main psychological attributes that make up a user’s intention for health and environmental behaviors. This theory was applied to the study of biomass cookstoves. Surveys in two rural communities in Honduras and Uganda were conducted to evaluate households’ intentions regarding adoption of improved biomass cookstoves. Multiple ordered logistic regressions method presented the most statistically significant results for the collected data of the case studies. Baseline results showed users had a significant positive mindset to replace their traditional practices. In Honduras, users valued smoke reduction more than other attributes and in average the odds for a household with slightly higher attitude toward reducing smoke emissions were 2.1 times greater to use a clean technology than someone who did not value smoke reduction as much. In Uganda, less firewood consumption was the most important attribute and on average the odds for households were 1.9 times more to adopt a clean technology to save fuel than someone who did not value fuelwood saving as much. After two months of using a cookstove, in Honduras, households’ perception of the feasibility of replacing traditional stoves, or perceived behavioral control, slightly decreased suggesting that as users became more familiar with the clean technology they perceived less hindrances to change their traditional habits. Information such as this could be utilized for design of the technologies that require user behavior changes to be effective.
{"title":"Evaluating User Intention for Uptake of Clean Technologies Using the Theory of Planned Behavior","authors":"M. Pakravan, Nordica A. MacCarty","doi":"10.1115/DETC2018-85992","DOIUrl":"https://doi.org/10.1115/DETC2018-85992","url":null,"abstract":"Understanding and integrating a user’s decision-making process into design and implementation strategies for clean energy technologies may lead to higher product adoption rates and ultimately increased impacts, particularly for those products that require a change in habit or behavior. To evaluate the key attributes that formulate a user’s decision-making behavior to adopt a new clean technology, this study presents the application of the Theory of Planned Behavior, a method to quantify the main psychological attributes that make up a user’s intention for health and environmental behaviors. This theory was applied to the study of biomass cookstoves. Surveys in two rural communities in Honduras and Uganda were conducted to evaluate households’ intentions regarding adoption of improved biomass cookstoves. Multiple ordered logistic regressions method presented the most statistically significant results for the collected data of the case studies. Baseline results showed users had a significant positive mindset to replace their traditional practices. In Honduras, users valued smoke reduction more than other attributes and in average the odds for a household with slightly higher attitude toward reducing smoke emissions were 2.1 times greater to use a clean technology than someone who did not value smoke reduction as much. In Uganda, less firewood consumption was the most important attribute and on average the odds for households were 1.9 times more to adopt a clean technology to save fuel than someone who did not value fuelwood saving as much. After two months of using a cookstove, in Honduras, households’ perception of the feasibility of replacing traditional stoves, or perceived behavioral control, slightly decreased suggesting that as users became more familiar with the clean technology they perceived less hindrances to change their traditional habits. Information such as this could be utilized for design of the technologies that require user behavior changes to be effective.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127766788","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}
Effective short-term load forecasting (STLF) plays an important role in demand-side management and power system operations. In this paper, STLF with three aggregation strategies are developed, which are information aggregation (IA), model aggregation (MA), and hierarchy aggregation (HA). The IA, MA, and HA strategies aggregate inputs, models, and forecasts, respectively, at different stages in the forecasting process. To verify the effectiveness of the three aggregation STLF, a set of 10 models based on 4 machine learning algorithms, i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest, are developed in each aggregation group to predict 1-hour-ahead load. Case studies based on 2-year of university campus data with 13 individual buildings showed that: (a) STLF with three aggregation strategies improves forecasting accuracy, compared with benchmarks without aggregation; (b) STLF-IA consistently presents superior behavior than STLF based on weather data and STLF based on individual load data; (c) MA reduces the occurrence of unsatisfactory single-algorithm STLF models, therefore enhancing the STLF robustness; (d) STLF-HA produces the most accurate forecasts in distinctive load pattern scenarios due to calendar effects.
{"title":"Short-Term Load Forecasting With Different Aggregation Strategies","authors":"C. Feng, Jie Zhang","doi":"10.1115/DETC2018-86084","DOIUrl":"https://doi.org/10.1115/DETC2018-86084","url":null,"abstract":"Effective short-term load forecasting (STLF) plays an important role in demand-side management and power system operations. In this paper, STLF with three aggregation strategies are developed, which are information aggregation (IA), model aggregation (MA), and hierarchy aggregation (HA). The IA, MA, and HA strategies aggregate inputs, models, and forecasts, respectively, at different stages in the forecasting process. To verify the effectiveness of the three aggregation STLF, a set of 10 models based on 4 machine learning algorithms, i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest, are developed in each aggregation group to predict 1-hour-ahead load. Case studies based on 2-year of university campus data with 13 individual buildings showed that: (a) STLF with three aggregation strategies improves forecasting accuracy, compared with benchmarks without aggregation; (b) STLF-IA consistently presents superior behavior than STLF based on weather data and STLF based on individual load data; (c) MA reduces the occurrence of unsatisfactory single-algorithm STLF models, therefore enhancing the STLF robustness; (d) STLF-HA produces the most accurate forecasts in distinctive load pattern scenarios due to calendar effects.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117041087","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 growing interest in metal additive manufacturing, one area of interest for design for additive manufacturing is the ability to understand how part geometry combined with the manufacturing process will affect part performance. In addition, many researchers are pursuing design for additive manufacturing with the goal of generating designs for stiff and lightweight applications as opposed to tailored compliance. A compliant mechanism has unique advantages over traditional mechanisms but previously, complex 3D compliant mechanisms have been limited by manufacturability. Recent advances in additive manufacturing enable fabrication of more complex and 3D metal compliant mechanisms, an area of research that is relatively unexplored. In this paper, a design for additive manufacturing workflow is proposed that incorporates feedback to a designer on both the structural performance and manufacturability. Specifically, a cellular contact-aided compliant mechanism for energy absorption is used as a test problem. Insights gained from finite element simulations of the energy absorbed as well as the thermal history from an AM build simulation are used to further refine the design. Using the proposed workflow, several trends on the performance and manufacturability of the test problem are determined and used to redesign the compliant unit cell. When compared to a preliminary unit cell design, a redesigned unit cell showed decreased energy absorption capacity of only 7.8% while decreasing thermal distortion by 20%. The workflow presented provides a systematic approach to inform a designer about methods to redesign an AM part.
{"title":"Design for Additive Manufacturing of Cellular Compliant Mechanism Using Thermal History Feedback","authors":"Jivtesh B. Khurana, Bradley Hanks, M. Frecker","doi":"10.1115/DETC2018-85819","DOIUrl":"https://doi.org/10.1115/DETC2018-85819","url":null,"abstract":"With growing interest in metal additive manufacturing, one area of interest for design for additive manufacturing is the ability to understand how part geometry combined with the manufacturing process will affect part performance. In addition, many researchers are pursuing design for additive manufacturing with the goal of generating designs for stiff and lightweight applications as opposed to tailored compliance. A compliant mechanism has unique advantages over traditional mechanisms but previously, complex 3D compliant mechanisms have been limited by manufacturability. Recent advances in additive manufacturing enable fabrication of more complex and 3D metal compliant mechanisms, an area of research that is relatively unexplored. In this paper, a design for additive manufacturing workflow is proposed that incorporates feedback to a designer on both the structural performance and manufacturability. Specifically, a cellular contact-aided compliant mechanism for energy absorption is used as a test problem. Insights gained from finite element simulations of the energy absorbed as well as the thermal history from an AM build simulation are used to further refine the design. Using the proposed workflow, several trends on the performance and manufacturability of the test problem are determined and used to redesign the compliant unit cell. When compared to a preliminary unit cell design, a redesigned unit cell showed decreased energy absorption capacity of only 7.8% while decreasing thermal distortion by 20%. The workflow presented provides a systematic approach to inform a designer about methods to redesign an AM part.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125698774","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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