Volume 1: 39th Computers and Information in Engineering Conference最新文献

{"title":"A Physics-Based Formal Vocabulary of Energy Verbs for Function Modeling","authors":"Chiradeep Sen, J. Summers, Xiaoyang Mao","doi":"10.1115/detc2019-98502","DOIUrl":"https://doi.org/10.1115/detc2019-98502","url":null,"abstract":"\u0000 Function modeling of complex systems relies on predefined vocabularies of functions and flows. These vocabularies are usually developed in a top-down approach, i.e., by starting with a survey of existing systems and identifying their functions empirically. These vocabularies, while highly useful in manual modeling due to their expressive power and coverage, can be unsuitable for computerized modeling and reasoning, esp. for physics-based reasoning. To this end, this paper presents a physics-based vocabulary of function verbs developed using the bottom-up approach, where the need for the verbs is identified through a survey of physics phenomena involving operations on various energy forms allowed in physics. This survey results in a minimal set of only six verbs and two logical nodes that are proposed here. Each term is formally defined as object-oriented classes derived from more foundational classes proposed in prior research. The paper shows many applications of these terms, for modeling both simpler devices and more complex engineered systems. Collectively, this new vocabulary provides sufficient coverage over modeling needs and ensures models that are logically consistent and physics-wise valid.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122315321","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":"Machine Learning Assisted Prediction of the Manufacturability of Laser-Based Powder Bed Fusion Process","authors":"Ying Zhang, Guoying Dong, Sheng Yang, Y. Zhao","doi":"10.1115/detc2019-97610","DOIUrl":"https://doi.org/10.1115/detc2019-97610","url":null,"abstract":"\u0000 Laser-based powder bed fusion (LPBF) process is a type of additive manufacturing process which is able to produce complex metal geometries. The fast development of laser-based powder bed fusion process offers new opportunities to the industries. Comparing to the conventional manufacturing process, LPBF offers more freedom on the shape complexity and hierarchical complexity. Even though the LPBF process has many advantages, there are still many constraints on LPBF. At the current stage, LPBF process still has a very high threshold for industrial application. It requires designers to have extensive knowledge of LPBF process to make the design manufacturable. The need for the automatic manufacturability analysis in the early design stage is essential. In this paper, a novel approach on analyzing the manufacturability of LPBF process is introduced. The machine learning model is developed to predict the manufacturability of LPBF. The unique dataset is established as the training examples. The proposed method achieves very competitive accuracy on analyzing the manufacturability of LBPF. The limitation and future work will be discussed in the end.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598772","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 Non-Periodic Lattice Structures by Allocating Pre-Optimized Building Blocks","authors":"Haejoon Choi, A. Baek, N. Kim","doi":"10.1115/detc2019-98204","DOIUrl":"https://doi.org/10.1115/detc2019-98204","url":null,"abstract":"\u0000 The development of lattice design approaches using topology optimization to create complex lattice structures is the focus of ongoing research as the lattice structures show great potential throughout a wider range of research fields. Unfortunately, many of the methods suggested are often very difficult to industrialize due to their complexity and excessive computational costs in the design process. This paper proposes a novel framework of generating non-periodic lattice structures using topologically pre-optimized building blocks to improve the computational efficiency of the optimization process while maintaining the performance of the target structure and to make the process of obtaining optimal design more accessible for people with limited knowledge of topology optimization. The proposed method is comprised of two phases: the generation of a library holding optimized building blocks that can endure various loading conditions and the allocation of these building blocks at the proper location in the design space based on stress information obtained from the finite element analysis. Numerical examples are presented to show that the method is feasible and able to achieve reasonable performances, demonstrating its efficacy.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121927436","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 Deep Learning Based Approach to Predict Sequential Design Decisions","authors":"Molla Hazifur Rahman, Charles Xie, Zhenghui Sha","doi":"10.1115/detc2019-97625","DOIUrl":"https://doi.org/10.1115/detc2019-97625","url":null,"abstract":"\u0000 During a design process, designers iteratively go back and forth between different design stages to explore the design space and search for the best design solution that satisfies all design constraints. For complex design problems, human has shown surprising capability in effectively reducing the dimensionality of design space and quickly converging it to a reasonable range for algorithms to step in and continue the search process. Therefore, modeling how human designers make decisions in such a sequential design process can help discover beneficial design patterns, strategies, and heuristics, which are important to the development of new algorithms embedded with human intelligence to augment computational design. In this paper, we develop a deep learning based approach to model and predict designers’ sequential decisions in a system design context. The core of this approach is an integration of the function-behavior-structure model for design process characterization and the long short term memory unit model for deep leaning. This approach is demonstrated in a solar energy system design case study, and its prediction accuracy is evaluated benchmarked on several commonly used models for sequential design decisions, such as Markov Chain model, Hidden Markov Chain model, and random sequence generation model. The results indicate that the proposed approach outperforms the other traditional models. This implies that during a system design task, designers are very likely to reply on both short-term and long-term memory of past design decisions in guiding their decision making in future design process. Our approach is general to be applied in many other design contexts as long as the sequential design action data is available.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"48 21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126053643","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":"Reverse Engineering and Optimization of a Steering Mechanism Component in a Competition Ground Vehicle","authors":"Matheo López-Pachón, Diego Gonzalez-Cárdenas, Andres Gonzalez-Mancera","doi":"10.1115/detc2019-98075","DOIUrl":"https://doi.org/10.1115/detc2019-98075","url":null,"abstract":"\u0000 In this paper, reverse engineering of a steering system component in a competition vehicle was successfully carried out. A CAD model was generated by means of NURBS curves from 3D scanning. Additionally, two manufacturing methods were compared to obtain the component namely, CNC and Casting. Simultaneously, a topological optimization was carried out from the failure diagnosis of the part; hence, to determine the failure, different loads were simulated in a transient and elastoplastic FEM simulation using an isotropic bilinear hardening model, in order to detect the displacement of a faulty component. The methodology implemented in the study showed satisfactory results for the different stages of the process.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121978263","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":"Using Virtual Reality to Test Human-Robot Interaction During a Collaborative Task","authors":"R. Etzi, Siyuan Huang, G. W. Scurati, Shilei Lyu, F. Ferrise, A. Gallace, A. Gaggioli, A. Chirico, M. Carulli, M. Bordegoni","doi":"10.1115/detc2019-97415","DOIUrl":"https://doi.org/10.1115/detc2019-97415","url":null,"abstract":"\u0000 The use of collaborative robots in the manufacturing industry has widely spread in the last decade. In order to be efficient, the human-robot collaboration needs to be properly designed by also taking into account the operator’s psychophysiological reactions. Virtual Reality can be used as a tool to simulate human-robot collaboration in a safe and cheap way. Here, we present a virtual collaborative platform in which the human operator and a simulated robot coordinate their actions to accomplish a simple assembly task. In this study, the robot moved slowly or more quickly in order to assess the effect of its velocity on the human’s responses. Ten participants tested this application by using an Oculus Rift head-mounted display; ARTracking cameras and a Kinect system were used to track the operator’s right arm movements and hand gestures respectively. Performance, user experience, and physiological responses were recorded. The results showed that while humans’ performances and evaluations varied as a function of the robot’s velocity, no differences were found in the physiological responses. Taken together, these data highlight the relevance of the kinematic aspects of robot’s motion within a human-robot collaboration and provide valuable insights to further develop our virtual human-machine interactive platform.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122428974","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":"An Uncertainty and Sensitivity Analysis Tool for Building Thermal Energy Consumption: A Performance Comprehension of Static and Dynamic Response Analyses","authors":"Rasool Koosha, F. Shahsavari","doi":"10.1115/detc2019-98510","DOIUrl":"https://doi.org/10.1115/detc2019-98510","url":null,"abstract":"\u0000 Recent literature on building energy performance simulation leans toward implementing uncertainty analysis (UA), instead of deterministic solutions, to handle ever-existing and pivotal uncertainties in building design decision-making process. Variations in weather temperature, degradation of building envelope material properties over time, and random behavior of occupants, among all, are the key sources of uncertainty in building energy consumption predictions. The UA couples to the sensitivity analysis (SA) to identify the most influential inputs on the uncertainties of the building energy consumption. This paper describes a newly-developed UA and SA predictive tool for building energy performance simulations. Energy performance simulations are based on a resistance-capacitance thermal model for the building.\u0000 For a hypothetical residential building in College Station, Texas, USA, the present work describes and compares predicted probability distribution and sensitivity indexes produced by the UA-SA tool using a transient (dynamic) response analysis (TRA) and static response analysis (SRA). For brevity, the analysis considers uncertainty only for the exterior walls’ parameters including thickness, thermal conductivity, heat transfer coefficient, density, and heat capacity; i.e., a five-dimensional problem is solved. Compared to the TRA, predictions from the SRA underestimate the annual energy consumption up to 30%; however, SRA is significantly faster. Nonetheless, sensitivity indexes from the SRA and TRA closely match.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123009450","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":"Technology Knowledge Graph for Design Exploration: Application to Designing the Future of Flying Cars","authors":"Serhad Sarica, Binyang Song, Jianxi Luo, K. Wood","doi":"10.1115/detc2019-97605","DOIUrl":"https://doi.org/10.1115/detc2019-97605","url":null,"abstract":"\u0000 To pursue innovation, design engineers need to continuously exploit the knowledge in their design domain and explore other relevant knowledge around the domain. While many methods and tools have been developed to retrieve knowledge within a given design domain, e.g., flying cars, knowledge discovery beyond the domain for innovation remains a challenge, and relevant methods are under-developed. Herein, we introduce a methodology to use a technology knowledge graph (TKG), which covers sematic-level knowledge in all technology fields defined in the international patent classification system, to retrieve the existing engineering knowledge in a domain and discover engineering concepts around the domain for future design considerations and innovation. We demonstrate the TKG-based methodology by applying it to explore the future designs of flying cars, an emerging domain with high uncertainty despite growing prospects.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128355295","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":"Dimensional Prediction for FDM Machines Using Artificial Neural Network and Support Vector Regression","authors":"Jiaqi Lyu, S. Manoochehri","doi":"10.1115/detc2019-97963","DOIUrl":"https://doi.org/10.1115/detc2019-97963","url":null,"abstract":"\u0000 With the development of Fused Deposition Modeling (FDM) technology, the quality of fabricated parts is getting more attention. The present study highlights the predictive model for dimensional accuracy in the FDM process. Three process parameters, namely extruder temperature, layer thickness, and infill density, are considered in the model. To achieve better prediction accuracy, three models are studied, namely multivariate linear regression, Artificial Neural Network (ANN), and Support Vector Regression (SVR). The models are used to characterize the complex relationship between the input variables and dimensions of fabricated parts. Based on the experimental data set, it is found that the ANN model performs better than the multivariate linear regression and SVR models. The ANN model is able to study more quality characteristics of fabricated parts with more process parameters of FDM.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131323876","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":"Toolbox for User-Centered Specification of VR Systems","authors":"Andreas Liebal, Heidi Krömker, Atif Mahboob, C. Weber","doi":"10.1115/detc2019-97340","DOIUrl":"https://doi.org/10.1115/detc2019-97340","url":null,"abstract":"\u0000 Breaking into the consumer market, Virtual Reality (VR) has now penetrated wide areas of the industrial market. Rapidly developing technologies are opening up new application possibilities with low cost and high quality solutions. Although VR has been used exclusively as a marketing tool in companies up to now, it is still being extended to support all areas of the complete product development process.\u0000 However, this is also accompanied by the difficulty to manage the amount of functionalities and design options associated with VR. Developers of such VR systems must nevertheless maintain an overview of the multitude of technological and content-related possibilities and bring them in line with the requirements of VR customers or VR users. At the same time, VR customers must also have an idea of the technological possibilities and formulate their vision of a VR system as precisely, completely and goal-oriented as possible. In the end, both sides must find a common level of communication in order to record the characteristics of the VR system in the form of a requirement specification or an offer. Factors such as different terminologies and different views about the product development process along with the diverse knowledge about VR functionalities on both sides complicate this process.\u0000 This article is intended to show the concept for a toolbox that supports the definition of a customer-specific VR solution. This shall exploit the full potential of VR for the customer’s specific product development process in VR. The generic product development process will be studied and the VR application areas will be located. In this regard, a classification of the main components of VR systems is performed. The goal of such a classification is to establish a relationship between the product model (along with product specific properties and relationships) and the VR system components.\u0000 As the field of VR does not yet have an established set of standardized terms, a definition of all terms in this context can help to improve communication between customer and developer. This article shows how a toolbox for agencies and development departments must be structured so that the VR developer can design, evaluate, calculate and develop a complex VR system in a customer-oriented way. Furthermore, the vision of the toolbox presented in this paper also has the goal that the VR customer can specifically formulate his/her requirements in a customer-specific way that leads to an easier understanding for VR developer and eventually to a VR solution.\u0000 Finally, the possibility of extension of the presented toolbox structure is discussed to automatically generate specification proposals and suitable technology recommendation based on the problem at hand.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128589454","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}