A. Iliopoulos, J. Steuben, N. Apetre, J. Michopoulos
{"title":"Surrogate Models for the Efficient Estimation of Residual Fields Associated With Additively Manufactured Parts","authors":"A. Iliopoulos, J. Steuben, N. Apetre, J. Michopoulos","doi":"10.1115/detc2019-98332","DOIUrl":"https://doi.org/10.1115/detc2019-98332","url":null,"abstract":"\u0000 Computing residual field distributions resulting from the thermomechanical history and interactions experienced by materials build by additive manufacturing (AM) methods, can be a very inefficient and computationally expensive process. To address this issue, the present work proposes and demonstrates a data-driven surrogate modeling approach that does not require solving the thermal-structural simulation of the AM process explicitly. Instead, it introduces the employment of various types of physics-agnostic surrogate models that are trained to data produced by full order physics-informed models. This enables to efficiently predict the resulting residual fields (e.g. distortions and residual stress) throughout the entire component. More specifically, two types of surrogate models for two different requirements scenarios are selected for the proposed work: Non-Uniform Rational B-Splines (NURBs) for a regularly sampled parametric space and k-simplex interpolants approach based on a two-step 3 + 1 dimensional interpolation that can operate on irregularly sampled spaces and grids. It is demonstrated that both methodologies can operate with low error and high performance (solution can be obtained within a few seconds on a desktop computer) on additively manufactured components of complex geometries.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"32 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":"129329165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Iliopoulos, J. Michopoulos, J. Steuben, A. Birnbaum, B. Graber, B. Rock, L. Johnson, E. Gorzkowski
{"title":"Towards Selective Volumetric Additive Manufacturing and Processing of Ceramics","authors":"A. Iliopoulos, J. Michopoulos, J. Steuben, A. Birnbaum, B. Graber, B. Rock, L. Johnson, E. Gorzkowski","doi":"10.1115/detc2019-98195","DOIUrl":"https://doi.org/10.1115/detc2019-98195","url":null,"abstract":"\u0000 The development of advanced additive manufacturing (AM) and material processing techniques is currently a topic of great interest to broad communities of scientists and engineers. In particular, there is a need for AM processes capable of producing functional and high-quality components at a faster rate than is currently achievable. In response to this demand, the present work introduces the initial steps of a novel spatially-resolved and selective approach for processing volumetric regions of ceramic materials. The proposed method utilizes microwave radiation to heat material at desired locations within a domain filled with ceramic powder. Using this principle of operation, a number of methods for implementation of this process are proposed. As a first step, a multiphysics computational methodology and an associated model that allows for the analysis and design of relevant processing systems is introduced. Additionally, a number of simulations demonstrating the feasibility of the proposed methodology are presented. Based on these preliminary results, we conclude with a discussion of ongoing and future efforts to fully realize this technology.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"60 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":"126692425","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 Augmented Reality-Based Training System for Manual Milling Operations","authors":"Tung-Jui Chuang, Chih-Kai Yang, Shana Smith","doi":"10.1115/detc2019-97844","DOIUrl":"https://doi.org/10.1115/detc2019-97844","url":null,"abstract":"\u0000 This study created an AR-based training system for manual milling machine operation. Users can operate a full-size virtual milling machine with their natural operating behavior, without additional worn or handheld devices. An Intel RealSense R200 camera was used to get the images and the depth information of the real world scenes. A Leap Motion controller was used to track user’s hand motion. Both Intel RealSense R200 and Leap Motion were mounted on an Oculus Rift head-mounted display so that users can freely walk around in the augmented environment to operate the virtual milling machine. A calibration method was developed to solve the dynamic occlusion problem in real time to increase the realism and immersiveness of the system. The system provided a safe learning-by-doing training environment, which was expected to enhance users’ learning effect and reduce accidents. User test results showed that the system was robust and helpful in improving user learning experience in manual milling machining operation.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"56 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":"124213043","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}
Saadia A. Razvi, S. Feng, A. Narayanan, Y. T. Lee, P. Witherell
{"title":"A Review of Machine Learning Applications in Additive Manufacturing","authors":"Saadia A. Razvi, S. Feng, A. Narayanan, Y. T. Lee, P. Witherell","doi":"10.1115/DETC2019-98415","DOIUrl":"https://doi.org/10.1115/DETC2019-98415","url":null,"abstract":"\u0000 Variability in product quality continues to pose a major barrier to the widespread application of additive manufacturing (AM) processes in production environment. Towards addressing this barrier, monitoring AM processes and measuring AM materials and parts has become increasingly commonplace, and increasingly precise, making a new wave of AM-related data available. This newfound data provides a valuable resource for gaining new insight to AM processes and decision making. Machine Learning (ML) provides an avenue to gain this insight by 1) learning fundamental knowledge about AM processes and 2) identifying predictive and actionable recommendations to optimize part quality and process design. This report presents a literature review of ML applications in AM. The review identifies areas in the AM lifecycle, including design, process plan, build, post process, and test and validation, that have been researched using ML. Furthermore, this report discusses the benefits of ML for AM, as well as existing hurdles currently limiting applications.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"32 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":"128097832","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":"Effects of Head Supported Mass on Predicted Neck Musculoskeletal Loadings During Walking and Running","authors":"Xianlian Zhou, Xinyu Chen, P. Roos, P. Whitley","doi":"10.1115/detc2019-97389","DOIUrl":"https://doi.org/10.1115/detc2019-97389","url":null,"abstract":"\u0000 This study aimed to investigate how muscle activation and intervertebral compressive forces during walking and running are altered with different head supported mass (HSM) types. A detailed neck musculoskeletal model was adapted and simulations were performed using existing motion data. It was found HSM wear required increased muscle activations, with the highest increase in running. Extensor activation increased particularly for the HSM with its center of mass (COM) in front of the head’s COM and flexor activation was significantly higher in running than in walking. Intervertebral compressive forces increased with HSM wear and the heaviest HSM caused the highest increase. During walking, the computed maximum compressive force at C7 was 129.7N without an HSM, and 163.6N and 208.5N with HSMs with a mass of 1.43 kg and 3.12 kg respectively. For running, it was 275.7N without an HSM, and 349.1N and 451.2N with the two HSM types. Overall muscle contributions to the compressive force varied over the gait cycle and were higher in running (26–110%) than in walking (18–58%). It was concluded that neck musculoskeletal loading increases with HSM wear, which is affected by HSM mass and mass distribution.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"28 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":"125768493","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":"Computational Design of Scheduling Strategies for Multi-Robot Cooperative 3D Printing","authors":"Laxmi Poudel, Wenchao Zhou, Zhenghui Sha","doi":"10.1115/detc2019-97640","DOIUrl":"https://doi.org/10.1115/detc2019-97640","url":null,"abstract":"\u0000 Cooperative 3D printing (C3DP) is a novel approach to additive manufacturing, where multiple printhead-carrying mobile robots work together cooperatively to print a desired part. The core of C3DP is the chunk-based printing strategy in which the desired part is first split into smaller chunks, and then the chunks are assigned to individual printing robots. These robots will work on the chunks simultaneously and in a scheduled sequence until the entire part is complete. Though promising, C3DP lacks proper framework that enables automatic chunking and scheduling given the available number of robots. In this study, we develop a computational framework that can automatically generate print schedule for specified number of chunks. The framework contains 1) a random generator that creates random print schedule using adjacency matrix which represents directed dependency tree (DDT) structure of chunks; 2) a set of geometric constraints against which the randomly generated schedules will be checked for validation; and 3) a printing time evaluation metric for comparing the performance of all valid schedules. With the developed framework, we present a case study by printing a large rectangular plate which has dimensions beyond what traditional desktop printers can print. The study showcases that our computation framework can successfully generate a variety of scheduling strategies for collision-free C3DP without any human interventions.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"134 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":"131763510","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}
Satchit Ramnath, Payam Haghighi, Ji Hoon Kim, D. Detwiler, M. Berry, J. Shah, Nikola Aulig, Patricia Wollstadt, S. Menzel
{"title":"Automatically Generating 60,000 CAD Variants for Big Data Applications","authors":"Satchit Ramnath, Payam Haghighi, Ji Hoon Kim, D. Detwiler, M. Berry, J. Shah, Nikola Aulig, Patricia Wollstadt, S. Menzel","doi":"10.1115/detc2019-97378","DOIUrl":"https://doi.org/10.1115/detc2019-97378","url":null,"abstract":"\u0000 Machine learning is opening up new ways of optimizing designs but it requires large data sets for training and verification. While such data sets already exist for financial, sales and business applications, this is not the case for engineering product design data. This paper discusses our efforts in curating a large Computer Aided Design (CAD) data set with desired variety and validity for automotive body structural compositions. Manual creation of 60,000 CAD variants is obviously not viable so we examine several approaches that can be automated with commercial CAD systems such as Parametric Design, Feature Based Design, Design Tables/Catalogs of Variants and Macros. We discuss pros and cons of each method and how we devised a combination of these approaches. This hybrid approach was used in association with DOE tables. Since the geometric configurations and characteristics need to be correlated to performance (structural integrity), the paper also demonstrates automated workflows to perform FEA on CAD models generated. Key simulation results can then be associated with CAD geometry and, for example, processes using machine learning algorithms for both supervised and unsupervised learning. The information obtained from the application of such methods to historical CAD models may help to understand the reasoning behind experiential design decisions. With the increase in computing power and network speed, such datasets together with novel machine learning methods, could assist in generating better designs, which could potentially be obtained by a combination of existing ones, or might provide insights into completely new design concepts meeting or exceeding the performance requirements.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"34 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":"132868983","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":"Direct Solid Element Slicing in Topology Optimization for Additive Manufacturing","authors":"D. Bender, A. Barari","doi":"10.1115/detc2019-98452","DOIUrl":"https://doi.org/10.1115/detc2019-98452","url":null,"abstract":"\u0000 The traditional input to almost all commercially available Additive Manufacturing (AM) systems is in STL (Standard Tessellation Language) format, which represents a solid model by its tessellated surfaces. This does not allow transferring the entire information of a solid model to the additive manufacturing preprocessing system. However, in some recent applications such as additive manufacturing preprocessing simulation, closed-loop of topology optimization and additive manufacturing process planning, and AM-based design optimization the full access to the solid model information is necessary. Slicing of the finite element model directly is introduced in this paper. The presented approach enables access to the entire solid model information during the AM preprocessing tasks with a focus on coupling the topology optimization in the design process with the actual manufacturing constraints.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"72 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":"133324362","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 Customer-to-Manufacturer Design Model for Custom Compression Casts","authors":"Yunbo Zhang, Tsz-Ho Kwok","doi":"10.1115/detc2019-98043","DOIUrl":"https://doi.org/10.1115/detc2019-98043","url":null,"abstract":"\u0000 This paper presents a computational framework for designing and optimizing custom compression casts/braces. Different from the conventional cast/brace design, our framework generates custom casts/braces with fitness, lightweight, and good ventilation. The computational pipeline is an end-to-end solution, directly from customer to the manufacturer, which starts from a 3D scanned human model represented by mesh and ends with the 3D printed cast/brace. Our interactive tools allows users to define and edit the 3D curves on the mesh surface, and trim the mesh surface to form the cast/brace shape using the curves. These tools are efficient and simple to use, and also they enable designing the custom casts/braces fitting to the given human body. In order to reduce the weight and improve the ventilation, we adopt the topology optimization (TO) method to optimize the cast/brace design. We extend the existing three-dimensional (3D) TO method to the mesh surface by simplifying the optimization problem to a 2D problem. Therefore, the efficiency of the TO computation is improved significantly. After the optimized cast/brace design is obtained on the mesh surface, a solid model is generated by our design interface and then sent to a 3D printer for fabrication. Simulation results show that our method can better re-disturb the stresses compared with the conventional 3D TO.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"14 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":"133844282","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 Automated Use Case Generation for Early Design Stage Functional Failure and Human Error Analysis","authors":"Lukman Irshad, H. Demirel, I. Tumer","doi":"10.1115/detc2019-98466","DOIUrl":"https://doi.org/10.1115/detc2019-98466","url":null,"abstract":"\u0000 Human errors and poor ergonomics are attributed to a majority of large-scale accidents and malfunctions in complex engineered systems. Human Error and Functional Failure Reasoning (HEFFR) is a framework developed to assess potential functional failures, human errors, and their propagation paths during early design stages so that more reliable systems with improved performance and safety can be designed. In order to perform a comprehensive analysis using this framework, a wide array of potential failure scenarios need to be tested. Coming up with such use cases that can cover a majority of faults can be challenging or even impossible for a single engineer or a team of engineers. In the field of software engineering, automated test case generation techniques have been widely used for software testing. This research explores these methods to create a use case generation technique that covers both component-related and human-related fault scenarios. The proposed technique is a time based simulation that employs a modified Depth First Search (DFS) algorithm to simulate events as the event propagation is analyzed using HEFFR at each timestep. This approach is applied to a hold-up tank design problem and the results are analyzed to explore the capabilities and limitations.","PeriodicalId":352702,"journal":{"name":"Volume 1: 39th Computers and Information in Engineering Conference","volume":"450 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":"115729666","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}