The use of additive manufacturing (AM) for fabricating industrial grade components has increased significantly in recent years. Numerous industrial entities are looking to leverage new AM techniques to enable fabrication of components that were typically manufactured previously using conventional manufacturing techniques such as subtractive manufacturing or casting. Therefore, it is becoming increasingly important to be able to rigorously evaluate the technical and economic feasibility of additively manufacturing a component relative to conventional alternatives. In order to support this evaluation, this paper presents a framework that investigates fabrication feasibility for AM from three perspectives: geometric evaluation, build orientation/support generation, and resources necessary (i.e., cost and time). The core functionality of the framework is enabled on voxelized model representation, discrete and binary formats of 3D continuous objects. AM fabrication feasibility analysis is applied to 34 various parts representing a wide range of manifolds and valves manufactured using conventional manufacturing techniques, components commonly found in the aerospace industry. Results obtained illustrate the capability and generalizability of the framework to analyze intricate geometries and provide a primary assessment for the feasibility of the AM process.
{"title":"From Conventional to Additive Manufacturing: Determining Component Fabrication Feasibility","authors":"S. E. Ghiasian, Prakhar Jaiswal, R. Rai, K. Lewis","doi":"10.1115/DETC2018-86238","DOIUrl":"https://doi.org/10.1115/DETC2018-86238","url":null,"abstract":"The use of additive manufacturing (AM) for fabricating industrial grade components has increased significantly in recent years. Numerous industrial entities are looking to leverage new AM techniques to enable fabrication of components that were typically manufactured previously using conventional manufacturing techniques such as subtractive manufacturing or casting. Therefore, it is becoming increasingly important to be able to rigorously evaluate the technical and economic feasibility of additively manufacturing a component relative to conventional alternatives. In order to support this evaluation, this paper presents a framework that investigates fabrication feasibility for AM from three perspectives: geometric evaluation, build orientation/support generation, and resources necessary (i.e., cost and time). The core functionality of the framework is enabled on voxelized model representation, discrete and binary formats of 3D continuous objects. AM fabrication feasibility analysis is applied to 34 various parts representing a wide range of manifolds and valves manufactured using conventional manufacturing techniques, components commonly found in the aerospace industry. Results obtained illustrate the capability and generalizability of the framework to analyze intricate geometries and provide a primary assessment for the feasibility of the AM process.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"269 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":"122135938","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}
One of the challenges in designing for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer’s perspective, these differences can lead to degradation of part performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturability explicitly into the design exploration process. The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of negative stiffness (NS) inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen. In this work, the manufacturing variability of critical features of a NS inclusion fabricated via microstereolithography are characterized experimentally and modelled mathematically. Specifically, the variation in the geometry of the NS inclusions and the Young’s modulus of the photopolymer are measured and modeled by both nonparametric and parametric joint probability distributions. Finally, the quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.
{"title":"Design Exploration of Reliably Manufacturable Materials and Structures With Applications to a Microstereolithography System","authors":"C. Morris, C. Seepersad","doi":"10.1115/DETC2018-85272","DOIUrl":"https://doi.org/10.1115/DETC2018-85272","url":null,"abstract":"One of the challenges in designing for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer’s perspective, these differences can lead to degradation of part performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturability explicitly into the design exploration process.\u0000 The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of negative stiffness (NS) inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen.\u0000 In this work, the manufacturing variability of critical features of a NS inclusion fabricated via microstereolithography are characterized experimentally and modelled mathematically. Specifically, the variation in the geometry of the NS inclusions and the Young’s modulus of the photopolymer are measured and modeled by both nonparametric and parametric joint probability distributions. Finally, the quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.","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":"125427340","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}
Austin Downey, Mohammadkazem Sadoughi, Liang Cao, S. Laflamme, Chao Hu
Structural control systems, including passive, semi-active and active damping systems, are used to increase structural resilience to multi-hazard excitations. While semi-active and active damping systems have been investigated for the mitigation of multi-hazard excitations, their requirement for real-time controllers and power availability limit their usefulness. This work proposes the use of a newly developed passive variable friction device for the mitigation of multi-hazard events. This passive variable friction device, when installed in a structure, is capable of mitigating different hazards from wind and ground motions. In wind events, the device ensures serviceability, while during earthquake events, the device reduces the building’s inter-story drift to maintain strength-based motion requirements. Results show that the passive variable friction device performs better than a traditional friction damper during a seismic event while not compromising any performance during wind events.
{"title":"Passive Variable Friction Damper for Increased Structural Resilience to Multi-Hazard Excitations","authors":"Austin Downey, Mohammadkazem Sadoughi, Liang Cao, S. Laflamme, Chao Hu","doi":"10.1115/DETC2018-85207","DOIUrl":"https://doi.org/10.1115/DETC2018-85207","url":null,"abstract":"Structural control systems, including passive, semi-active and active damping systems, are used to increase structural resilience to multi-hazard excitations. While semi-active and active damping systems have been investigated for the mitigation of multi-hazard excitations, their requirement for real-time controllers and power availability limit their usefulness. This work proposes the use of a newly developed passive variable friction device for the mitigation of multi-hazard events. This passive variable friction device, when installed in a structure, is capable of mitigating different hazards from wind and ground motions. In wind events, the device ensures serviceability, while during earthquake events, the device reduces the building’s inter-story drift to maintain strength-based motion requirements. Results show that the passive variable friction device performs better than a traditional friction damper during a seismic event while not compromising any performance during wind events.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"10 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":"129545668","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}
Recent advances in deep learning enable machines to learn existing designs by themselves and to create new designs. Generative adversarial networks (GANs) are widely used to generate new images and data by unsupervised learning. Certain limitations exist in applying GANs directly to product designs. It requires a large amount of data, produces uneven output quality, and does not guarantee engineering performance. To solve these problems, this paper proposes a design automation process by combining GANs and topology optimization. The suggested process has been applied to the wheel design of automobiles and has shown that an aesthetically superior and technically meaningful design can be automatically generated without human interventions.
{"title":"Design Automation by Integrating Generative Adversarial Networks and Topology Optimization","authors":"Sangeun Oh, Yongsu Jung, Ikjin Lee, Namwoo Kang","doi":"10.1115/DETC2018-85506","DOIUrl":"https://doi.org/10.1115/DETC2018-85506","url":null,"abstract":"Recent advances in deep learning enable machines to learn existing designs by themselves and to create new designs. Generative adversarial networks (GANs) are widely used to generate new images and data by unsupervised learning. Certain limitations exist in applying GANs directly to product designs. It requires a large amount of data, produces uneven output quality, and does not guarantee engineering performance. To solve these problems, this paper proposes a design automation process by combining GANs and topology optimization. The suggested process has been applied to the wheel design of automobiles and has shown that an aesthetically superior and technically meaningful design can be automatically generated without human interventions.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"32 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":"124995251","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}
Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM’s special capabilities into their designs, but are often at a loss as to how to do so. Design for Additive Manufacturing (DfAM) methods are currently in development but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design a process. The authors have previously derived 29 design heuristics for AM. In this paper, the efficacy of these heuristics is tested in the context of a re-design scenario with novice designers. The preliminary results show that the heuristics positively influence the designs generated by the novice designers. Analysis of the use of specific heuristics by the participants and future research to validate the impact of the design heuristics for additive manufacturing with expert designers and in original design scenarios is planned.
{"title":"Preliminary User Study on Design Heuristics for Additive Manufacturing","authors":"Alexandra Blösch-Paidosh, K. Shea","doi":"10.1115/DETC2018-85908","DOIUrl":"https://doi.org/10.1115/DETC2018-85908","url":null,"abstract":"Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM’s special capabilities into their designs, but are often at a loss as to how to do so. Design for Additive Manufacturing (DfAM) methods are currently in development but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design a process. The authors have previously derived 29 design heuristics for AM. In this paper, the efficacy of these heuristics is tested in the context of a re-design scenario with novice designers. The preliminary results show that the heuristics positively influence the designs generated by the novice designers. Analysis of the use of specific heuristics by the participants and future research to validate the impact of the design heuristics for additive manufacturing with expert designers and in original design scenarios is planned.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"34 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":"121948089","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}
Lin Guo, Hamed Zamanisabzi, T. Neeson, J. Allen, F. Mistree
In a multi-reservoir system, ensuring adequate water availability across reservoirs while managing conflicting goals under uncertainties are critical to making the social-ecological system sustainable. The priorities of multiple user-groups and availability of the water resource may vary with time, weather and other factors. Uncertainties such as variation in precipitation bring more complexity, which intensifies the discrepancies between water supply and water demand for each user-group. To reduce such discrepancies, we should satisfice conflicting goals, considering typical uncertainties. We observed that models are incomplete and inaccurate, which challenge the use of the single optimal solution to be robust to uncertainties. So, we explore satisficing solutions that are relatively insensitive to uncertainties, by incorporating different design preferences, identifying sensitive segments and improving the design accordingly. This work is an example of exploring the solution space to enhance sustainability in multidisciplinary systems, when goals conflict, preferences are evolving, and uncertainties add complexity.
{"title":"Managing Conflicting Water Resource Goals and Uncertainties in a Dam-Network by Exploring the Solution Space","authors":"Lin Guo, Hamed Zamanisabzi, T. Neeson, J. Allen, F. Mistree","doi":"10.1115/DETC2018-86018","DOIUrl":"https://doi.org/10.1115/DETC2018-86018","url":null,"abstract":"In a multi-reservoir system, ensuring adequate water availability across reservoirs while managing conflicting goals under uncertainties are critical to making the social-ecological system sustainable. The priorities of multiple user-groups and availability of the water resource may vary with time, weather and other factors. Uncertainties such as variation in precipitation bring more complexity, which intensifies the discrepancies between water supply and water demand for each user-group. To reduce such discrepancies, we should satisfice conflicting goals, considering typical uncertainties.\u0000 We observed that models are incomplete and inaccurate, which challenge the use of the single optimal solution to be robust to uncertainties. So, we explore satisficing solutions that are relatively insensitive to uncertainties, by incorporating different design preferences, identifying sensitive segments and improving the design accordingly. This work is an example of exploring the solution space to enhance sustainability in multidisciplinary systems, when goals conflict, preferences are evolving, and uncertainties add complexity.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"59 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":"130808853","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}
Jun Yu, Zhenjun Ming, Guoxin Wang, Yan Yan, Xiaoping Lan
The development of complex product dynamic simulation models and the integration of design automation systems require knowledge from multiple heterogeneous data sources and tools. Because of the heterogeneity of model data, the integration of tools and data is a time-consuming and error-prone task. The main objective of this study is to provide a unified model of dynamic simulation for engineering design, which serves as a knowledge base to support the development of a dynamic simulation model. The integration of knowledge is realized through (i) definition of the structure and interface during the design phase of the dynamic simulation model, and (ii) definition of a model-driven integrated environment configuration process during the runtime phase. In order to achieve interoperability among the different simulation models in a collaborative design environment, we build a “Demand-Resources-Service-Knowledge-Process (DKRSP)” ontology that formally represents the semantics of dynamic simulation models. Based on the ontology, a knowledge base is created for the management of dynamic simulation knowledge. The efficacy of the ontology and the knowledge base are demonstrated using a transmission design example.
{"title":"Ontology-Based Unified Representation of Dynamic Simulation Models in Engineering Design","authors":"Jun Yu, Zhenjun Ming, Guoxin Wang, Yan Yan, Xiaoping Lan","doi":"10.1115/DETC2018-85536","DOIUrl":"https://doi.org/10.1115/DETC2018-85536","url":null,"abstract":"The development of complex product dynamic simulation models and the integration of design automation systems require knowledge from multiple heterogeneous data sources and tools. Because of the heterogeneity of model data, the integration of tools and data is a time-consuming and error-prone task. The main objective of this study is to provide a unified model of dynamic simulation for engineering design, which serves as a knowledge base to support the development of a dynamic simulation model. The integration of knowledge is realized through (i) definition of the structure and interface during the design phase of the dynamic simulation model, and (ii) definition of a model-driven integrated environment configuration process during the runtime phase. In order to achieve interoperability among the different simulation models in a collaborative design environment, we build a “Demand-Resources-Service-Knowledge-Process (DKRSP)” ontology that formally represents the semantics of dynamic simulation models. Based on the ontology, a knowledge base is created for the management of dynamic simulation knowledge. The efficacy of the ontology and the knowledge base are demonstrated using a transmission design example.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"92 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":"116282355","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 efficient production planning of Additively Manufactured (AM) parts is a key point for industry-scale adoption of AM. This study develops an AM-based production plan for the case of manufacturing a significant number of parts with different shapes and sizes by multiple machines with the ultimate purpose of reducing the cycle time. The proposed AM-based production planning includes three main steps: (1) determination of build orientation; (2) 2D packing of parts within the limited workspace of AM machines; and (3) scheduling parts on multiple AM machines. For making decision about build orientation, two main policies are considered: (1) laying policy in which the focus is on reducing the height of parts; and (2) standing policy which aims at minimizing the projection area on the tray to reduce the number of jobs. A heuristic algorithm is suggested to solve 2D packing and scheduling problems. A numerical example is conducted to identify which policy is more preferred in terms of cycle time. As a result, the standing policy is more preferred than the laying policy as the number of parts increases. In the case of testing 3,000 parts, the cycle time of standing policy is about 6% shorter than laying policy.
{"title":"Production Planning for Mass Customization in Additive Manufacturing: Build Orientation Determination, 2D Packing and Scheduling","authors":"Yosep Oh, Chi Zhou, S. Behdad","doi":"10.1115/DETC2018-85639","DOIUrl":"https://doi.org/10.1115/DETC2018-85639","url":null,"abstract":"The efficient production planning of Additively Manufactured (AM) parts is a key point for industry-scale adoption of AM. This study develops an AM-based production plan for the case of manufacturing a significant number of parts with different shapes and sizes by multiple machines with the ultimate purpose of reducing the cycle time. The proposed AM-based production planning includes three main steps: (1) determination of build orientation; (2) 2D packing of parts within the limited workspace of AM machines; and (3) scheduling parts on multiple AM machines. For making decision about build orientation, two main policies are considered: (1) laying policy in which the focus is on reducing the height of parts; and (2) standing policy which aims at minimizing the projection area on the tray to reduce the number of jobs. A heuristic algorithm is suggested to solve 2D packing and scheduling problems. A numerical example is conducted to identify which policy is more preferred in terms of cycle time. As a result, the standing policy is more preferred than the laying policy as the number of parts increases. In the case of testing 3,000 parts, the cycle time of standing policy is about 6% shorter than laying policy.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"50 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":"127259238","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}
Rohan Prabhu, Scarlett R. Miller, T. Simpson, N. Meisel
Additive Manufacturing (AM) is a novel process that enables the manufacturing of complex geometries through layer-by-layer deposition of material. AM processes provide a stark contrast to traditional, subtractive manufacturing processes, which has resulted in the emergence of design for additive manufacturing (DfAM) to capitalize on AM’s capabilities. In order to support the increasing use of AM in engineering, it is important to shift from the traditional design for manufacturing and assembly mindset, towards integrating DfAM. To facilitate this, DfAM must be included in the engineering design curriculum in a manner that has the highest impact. While previous research has systematically organized DfAM concepts into process capability-based (opportunistic) and limitation-based (restrictive) considerations, limited research has been conducted on the impact of teaching DfAM on the student’s design process. This study investigates this interaction by comparing two DfAM educational interventions conducted at different points in the academic semester. The two versions are compared by evaluating the students’ perceived utility, change in self-efficacy, and the use of DfAM concepts in design. The results show that introducing DfAM early in the semester when students have little previous experience in AM resulted in the largest gains in students perceiving utility in learning about DfAM concepts and DfAM self-efficacy gains. Further, we see that this increase relates to greater application of opportunistic DfAM concepts in student design ideas in a DfAM challenge. However, no difference was seen in the application of restrictive DfAM concepts between the two interventions. These results can be used to guide the design and implementation of DfAM education.
{"title":"The Earlier the Better? Investigating the Importance of Timing on Effectiveness of Design for Additive Manufacturing Education","authors":"Rohan Prabhu, Scarlett R. Miller, T. Simpson, N. Meisel","doi":"10.1115/DETC2018-85953","DOIUrl":"https://doi.org/10.1115/DETC2018-85953","url":null,"abstract":"Additive Manufacturing (AM) is a novel process that enables the manufacturing of complex geometries through layer-by-layer deposition of material. AM processes provide a stark contrast to traditional, subtractive manufacturing processes, which has resulted in the emergence of design for additive manufacturing (DfAM) to capitalize on AM’s capabilities. In order to support the increasing use of AM in engineering, it is important to shift from the traditional design for manufacturing and assembly mindset, towards integrating DfAM. To facilitate this, DfAM must be included in the engineering design curriculum in a manner that has the highest impact. While previous research has systematically organized DfAM concepts into process capability-based (opportunistic) and limitation-based (restrictive) considerations, limited research has been conducted on the impact of teaching DfAM on the student’s design process. This study investigates this interaction by comparing two DfAM educational interventions conducted at different points in the academic semester. The two versions are compared by evaluating the students’ perceived utility, change in self-efficacy, and the use of DfAM concepts in design. The results show that introducing DfAM early in the semester when students have little previous experience in AM resulted in the largest gains in students perceiving utility in learning about DfAM concepts and DfAM self-efficacy gains. Further, we see that this increase relates to greater application of opportunistic DfAM concepts in student design ideas in a DfAM challenge. However, no difference was seen in the application of restrictive DfAM concepts between the two interventions. These results can be used to guide the design and implementation of DfAM education.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"15 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":"128089172","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}
Bias correction is important for model calibration to obtain unbiased calibration parameter estimates and make accurate prediction. However, calibration often relies on insufficient samples, and so bias correction often mostly depends on extrapolation. For example, bias correction with twelve samples in nine-dimensional box generated by Latin Hypercube Sampling (LHS) has less than 0.1% interpolation domain in the box. Since bias correction is coupled with calibration parameter estimation, calibration with extrapolative bias correction can lead a large error in the calibrated parameters. This paper proposes an idea of calibration with minimum bumpiness correction. The bumpiness of bias correction is a good measure of assessing the potential risk of a large error in the correction. By minimizing bumpiness, the risk of extrapolation can be reduced while the accuracy of parameter estimates can be achieved. It was found that this calibration method gave more accurate results than Bayesian calibration for an analytical example. It was also found that there are common denominators between the proposed method and the Bayesian calibration with bias correction.
{"title":"Least Bumpiness Calibration With Extrapolative Bias Correction","authors":"Chanyoung Park, N. Kim, R. Haftka","doi":"10.1115/DETC2018-86163","DOIUrl":"https://doi.org/10.1115/DETC2018-86163","url":null,"abstract":"Bias correction is important for model calibration to obtain unbiased calibration parameter estimates and make accurate prediction. However, calibration often relies on insufficient samples, and so bias correction often mostly depends on extrapolation. For example, bias correction with twelve samples in nine-dimensional box generated by Latin Hypercube Sampling (LHS) has less than 0.1% interpolation domain in the box. Since bias correction is coupled with calibration parameter estimation, calibration with extrapolative bias correction can lead a large error in the calibrated parameters. This paper proposes an idea of calibration with minimum bumpiness correction. The bumpiness of bias correction is a good measure of assessing the potential risk of a large error in the correction. By minimizing bumpiness, the risk of extrapolation can be reduced while the accuracy of parameter estimates can be achieved. It was found that this calibration method gave more accurate results than Bayesian calibration for an analytical example. It was also found that there are common denominators between the proposed method and the Bayesian calibration with bias correction.","PeriodicalId":138856,"journal":{"name":"Volume 2A: 44th Design Automation Conference","volume":"17 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":"132870196","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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