Volume 3B: 47th Design Automation Conference (DAC)最新文献

{"title":"A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys","authors":"M. Allen, T. Kirk, R. Malak, R. Arróyave","doi":"10.1115/detc2021-68836","DOIUrl":"https://doi.org/10.1115/detc2021-68836","url":null,"abstract":"\u0000 Compositionally graded alloys, a special class of functionally graded materials (FGMs), utilize localized variations in composition within a single metal part to achieve higher performance than traditional single-material parts. In previous work [1], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s usefulness or feasibility, such as deleterious phases or properties, and also optimizes gradients for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space at every step in the gradient. Because the addition of even a small amount of an alloying element can introduce a new deleterious phase, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new sampling method samples within an artificial expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the distribution of samples in each subspace of a 2-D, 3-D, and 4-D state space. Next, a parametric study in a synthetic 2-D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127737045","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}

{"title":"Topology Optimization Taking Into Account Geometrical Constraint of No-Closed Hole for Additive Manufacturing Based on Fictitious Physical Model Concept","authors":"T. Yamada, Y. Noguchi","doi":"10.1115/detc2021-66717","DOIUrl":"https://doi.org/10.1115/detc2021-66717","url":null,"abstract":"\u0000 This paper proposes a topology optimization method that considers the geometrical constraint of a non-closed hole for additive manufacturing based on the fictitious physical model concept. First, the basic topology optimization concept and level set-based method are introduced. Second, the concept of a fictitious physical model for geometrical constraint in the topology optimization framework is discussed. Then, the model for the geometrical constraint of a non-closed hole for additive manufacturing is proposed. Numerical examples are provided to validate the proposed model. In addition, topology optimization considering this geometrical constraint is formulated, and topology optimization algorithms are constructed using the finite element method. Finally, optimization examples are provided to validate the proposed topology optimization method.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130108472","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}

{"title":"A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data","authors":"Huiru Li, Xiaoping Du","doi":"10.1115/detc2021-67958","DOIUrl":"https://doi.org/10.1115/detc2021-67958","url":null,"abstract":"\u0000 Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and the component states are therefore assumed independent by the traditional method, which can result in a large error. This work proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the proposed method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"338 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134206025","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}

{"title":"Automating Design Requirement Extraction From Text With Deep Learning","authors":"H. Akay, Maria C. Yang, Sang-Gook Kim","doi":"10.1115/detc2021-66898","DOIUrl":"https://doi.org/10.1115/detc2021-66898","url":null,"abstract":"\u0000 Nearly every artifact of the modern engineering design process is digitally recorded and stored, resulting in an overwhelming amount of raw data detailing past designs. Analyzing this design knowledge and extracting functional information from sets of digital documents is a difficult and time-consuming task for human designers. For the case of textual documentation, poorly written superfluous descriptions filled with jargon are especially challenging for junior designers with less domain expertise to read. If the task of reading documents to extract functional requirements could be automated, designers could actually benefit from the distillation of massive digital repositories of design documentation into valuable information that can inform engineering design. This paper presents a system for automating the extraction of structured functional requirements from textual design documents by applying state of the art Natural Language Processing (NLP) models. A recursive method utilizing Machine Learning-based question-answering is developed to process design texts by initially identifying the highest-level functional requirement, and subsequently extracting additional requirements contained in the text passage. The efficacy of this system is evaluated by comparing the Machine Learning-based results with a study of 75 human designers performing the same design document analysis task on technical texts from the field of Microelectromechanical Systems (MEMS). The prospect of deploying such a system on the sum of all digital engineering documents suggests a future where design failures are less likely to be repeated and past successes may be consistently used to forward innovation.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115807201","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}

{"title":"Topology Optimization Design of Structures Based on Eigenfrequency Matching","authors":"Daniel Giraldo-Guzmán, C. Lissenden, P. Shokouhi, M. Frecker","doi":"10.1115/detc2021-69498","DOIUrl":"https://doi.org/10.1115/detc2021-69498","url":null,"abstract":"\u0000 We demonstrate the design of resonating structures using a density-based topology optimization approach, which requires the eigenfrequencies to match a set of target values. To develop a solution, several optimization modules are implemented, including material interpolation models, penalization schemes, filters, analytical sensitivities, and a solver. Moreover, common challenges in topology optimization for dynamic systems and their solutions are discussed. In this study, the objective function is to minimize the error between the target and actual eigenfrequency values. The finite element method is used to compute the eigenfrequencies at each iteration. To solve the optimization problem, we use the sequential linear programming algorithm with move limits, enhanced by a filtering technique. Finally, we present a resonator design as a case study and analyze the design process with different optimization parameters.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128940782","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}

{"title":"Deep Reinforcement Learning Enhanced Convolutional Neural Networks for Robotic Grasping","authors":"Jianhao Fang, Weifei Hu, Chuxuan Wang, Zhen-yu Liu, Jianrong Tan","doi":"10.1115/detc2021-67225","DOIUrl":"https://doi.org/10.1115/detc2021-67225","url":null,"abstract":"\u0000 Robotic grasping is an important task for various industrial applications. However, combining detecting and grasping to perform a dynamic and efficient object moving is still a challenge for robotic grasping. Meanwhile, it is time consuming for robotic algorithm training and testing in realistic. Here we present a framework for dynamic robotic grasping based on deep Q-network (DQN) in a virtual grasping space. The proposed dynamic robotic grasping framework mainly consists of the DQN, the convolutional neural network (CNN), and the virtual model of robotic grasping. After observing the result generated by applying the generative grasping convolutional neural network (GG-CNN), a robotic manipulation conducts actions according to Q-network. Different actions generate different rewards, which are implemented to update the neural network through loss function. The goal of this method is to find a reasonable strategy to optimize the total reward and finally accomplish a dynamic grasping process. In the test of virtual space, we achieve an 85.5% grasp success rate on a set of previously unseen objects, which demonstrates the accuracy of DQN enhanced GG-CNN model. The experimental results show that the DQN can efficiently enhance the GG-CNN by considering the grasping procedure (i.e. the grasping time and the gripper’s posture), which makes the grasping procedure stable and increases the success rate of robotic grasping.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129010499","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}

{"title":"Global Sensitivity Analysis for Field Response Based on the Manifold of Feature Covariance Matrix","authors":"Zhouzhou Song, Zhao Liu, Can Xu, P. Zhu","doi":"10.1115/detc2021-69086","DOIUrl":"https://doi.org/10.1115/detc2021-69086","url":null,"abstract":"\u0000 In real-world applications, it is commonplace that the computational models have field responses, i.e., the temporal or spatial fields. It has become a critical task to develop global sensitivity analysis (GSA) methods to measure the effect of each input variable on the full-field. In this paper, a new sensitivity analysis method based on the manifold of feature covariance matrix (FCM) is developed for quantifying the impact of input variables on the field response. The method firstly performs feature extraction on the field response to obtain a low-dimensional FCM. An adaptive feature selection method is proposed to avoid the FCM from singularity. Thereby, the field response is represented by a FCM, which lies on a symmetric positive-definite matrix manifold. Then, the GSA technique based on the Cramér-von Mises distance for output valued on the Riemannian manifold is introduced for estimating the sensitivity indices for field response. An example of a temporal field and an example of a 2-D displacement field are introduced to demonstrate the applicability of the proposed method in estimating global sensitivity indices for field solution. Results show that the proposed method can distinguish the important input variables correctly and can yield robust index values. Besides, the proposed method can be implemented for GSA for field responses of different dimensionalities.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123301445","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}

{"title":"The Development and Testing of Pour-Flush Toilet Sensors for Understanding User Interaction in Peri-Urban Households","authors":"Pablo Cotera Rivera, A. Bilton","doi":"10.1115/detc2021-67697","DOIUrl":"https://doi.org/10.1115/detc2021-67697","url":null,"abstract":"\u0000 Rapid worldwide urbanization has created peri-urban environments that often lack services and infrastructure for water and sanitation. Globally, around 4.5 billion people do not have access to safely managed sanitation, as is often the case in such environments. Efforts to develop appropriate sanitation alternatives in these contexts recognize the value of understanding users’ preferences and interaction with their sanitation systems, however, the traditional tools for assessing technology usage and adoption are based on physical observation, which presents limitations. In this work, we developed a toilet sensor to identify usage patterns of pour-flush toilets by quantifying flushing and defecation events. The device has a methane gas sensor, IR distance sensor and a motion sensor connected to a microcontroller. Its small footprint allows for unobtrusive installation inside a toilet bowl and operates battery-powered for about 5 days depending on usage patterns. To evaluate the sensor performance, units were installed for a field trial in nine participants’ households in a Mexican peri-urban community and an algorithm for automated data analysis was developed. Surveys were also conducted to benchmark the sensor performance and determine the potential value of the approach. Results showed that on average people underreported their daily toilet usage by two events compared to the measurements and they flushed only 75% of the time after defecation. By monitoring the usage of the current pour-flush toilets lacking piped water and sewerage and complementing the data with users’ feedback, we can gain an understanding of the existing limitations so more suitable sanitation alternatives can be proposed.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116690546","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}

{"title":"A Computational Framework for Social Entrepreneurs to Determine Policies for Sustainable Development","authors":"V. Karkaria, A. Das, A. Yadav, Ayushi Sharma, J. Allen, F. Mistree","doi":"10.1115/detc2021-70827","DOIUrl":"https://doi.org/10.1115/detc2021-70827","url":null,"abstract":"\u0000 Many villagers in India suffer from multiple socio-economic challenges, such as, low-income, unemployment, and lack of access to clean water and energy that hinder their overall development. Social entrepreneurs can assist with the design and implementation of policies that help villagers achieve overall sustainability. Previously, a framework to support social entrepreneurs and stakeholders in identifying potential challenges and evaluating the impact of solutions concerning sustainable development was proposed. The framework was anchored in a single perspective (thematic area) Dilemma Triangle Method for identification of challenges and System Dynamics Models for evaluation of impacts. Policies for sustainable development, however, require an understanding of the interactions among multiple possibilities and their associated challenges to be viable, feasible, and equitable. Additionally, one key missing feature in the past framework is the evaluation of the economic impact of the solutions or policies.\u0000 In this paper, we add new value to the past framework using the Dilemma Triangle Method to integrate more than one perspective, and the System Dynamics Model integrating the economic indicator to get a holistic view of sustainable development. By the addition of more than one thematic area in the Dilemma Triangle Method, the inter-dependency among thematic areas and their associated parameters is understood, which is necessary for identifying problems in complex systems. We include Gross Value Added (GVA) as an economic indicator for evaluating the economic feasibility of the policies identified by using the framework. To illustrate the efficacy of the framework, we implement it for the Kantashol village (panchayat), Jharkhand, India. Based on the Dilemma Triangle Method, multiple policies are proposed, out of which four policies are evaluated in the Systems Dynamic Model for the sustainable development of the village. The policies are currently being applied in the village and the outcome of this framework will be validated in real-time over the years.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122048415","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}

{"title":"Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty","authors":"Lichuan Ren, Zhimin Xi","doi":"10.1115/detc2021-69284","DOIUrl":"https://doi.org/10.1115/detc2021-69284","url":null,"abstract":"\u0000 Path tracking error control is an important functionality in the development of autonomous vehicles when a collision-free path has been planned. Large path tracking errors could lead to collision or even out of the control of the vehicle. Vehicle dynamic models are used to minimize the vehicle path tracking error so that control strategies can be designed under different scenarios. However, the vehicle dynamic model may not truly represent the actual vehicle dynamics. Furthermore, the nominal parameter employed in the vehicle dynamic model cannot represent actual operating conditions of the vehicle under environmental uncertainty. This paper presents a learning-based bias modeling method to improve the fidelity of any baseline vehicle dynamics model so that effective path tracking controller design can be achieved through a low fidelity but high-efficiency vehicle dynamic model with the aid of a few experiments or high fidelity simulations. The state-of-the-art of machine learning models, such as Gaussian process (GP) regression, recurrent neural network (RNN), and long short-term memory (LSTM) network, are employed for bias learning and comparison. A high-fidelity vehicle simulator, CARLA, is employed to collect virtual test data and demonstrate the effectiveness of the proposed bias-learning based control strategies under environmental uncertainty.","PeriodicalId":299235,"journal":{"name":"Volume 3B: 47th Design Automation Conference (DAC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126647431","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}