bluuebunny/crossref_metadata_2025_split huggingface dataset:
Manufacturing Process Classification Based on Distance Rotationally Invariant Convolutions
Authors: Wang Zhichao , Rosen David 
Published: November 2022

Abstract
Given a design part, the task of manufacturing process classification identifies an appropriate manufacturing process to fabricate it. Our previous research proposed a large dataset for manufacturing process classification and achieved accurate results based on a combination of a convolutional neural network (CNN) and the heat kernel signature (HKS) for triangle mesh. In this paper, we constructed a classification method based on rotation-invariant shape descriptors and a neural network for point clouds, and it achieved better accuracy than all previous methods. This method uses a point cloud part representation, in contrast to the triangle mesh representation used in our previous work. The first step extracted rotation-invariant features consisting of a set of distances between points in the point cloud. Then, the extracted shape descriptors were fed into a CNN for the classification of manufacturing processes. In addition, we provided two visualization methods for interpreting the intermediate layers of the neural network. Last, the performance of the method was tested on some ambiguous examples and their performance was consistent with expectations. In this paper, we have considered only shape information, while non-shape information like materials and tolerances were ignored. Additionally, only parts that required one manufacturing process were considered in this research. Our work demonstrates that part shape attributes alone are adequate for discriminating between different manufacturing processes considered.
Digital Twin Approach to Build Predictive Maintenance Model and Its Case Study
Authors: Yang Wenqiang , Bao Xiangyu , Zheng Yu 
Published: November 2022

Abstract
Predictive maintenance is considered to be an effective strategy to optimize system operation. In the execution of increasingly complex tasks, efficient and intelligent management becomes crucial. As the basis of Digital twin (DT), predictive capabilities contribute to the value of systems and help describe their complex behavior. But the challenge in Digital twin model building is still exist, it cannot accurately reproduce the physical resources, and the introduction of error will lead to the differential extension of virtual system from physical space. The challenge is how to build Digital twin capabilities and reduce accumulative error at the same time. Based on the current research progress, this paper analyzed the existing challenges in realizing predictive maintenance capability driven by Digital twin, and then, it described the predictive control process with flow path and layer framework, In addition, the way of inserting the optimization algorithm for Digital twin was explored. Finally, a practical trajectory prediction problem was taken as a case study to effectively utilize the cyclic interaction mechanism and data fusion method of Digital twin, which can consider the offset cumulative signal, and correct the prediction state in real time. This research may provide the reference for Digital twin configuration and further study.
GraMMaCAD: Interactively Defining Spatially Varying FGMs on BRep CAD Models
Authors: Luu Thu Huong , Altenhofen Christian , Stork André , Fellner Dieter 
Published: November 2022

Abstract
Additive manufacturing (AM) has enabled control over heterogeneous materials and structures in ways that were not previously possible, including functionally graded materials (FGM). However, typical computer aided design (CAD) systems — mainly based on boundary representations (B-Rep) — are not built for modeling and representing spatially varying material information, which are the key features for designing FGM parts. While volumetric representations (V-Rep) such as tetrahedral or hexahedral meshes, trivariate splines or subdivision solids can hold the necessary FGM information, they are by default not compatible with existing CAD models. Thus, the capabilities of AM motivate enhancing CAD systems to support volumetric material allocation and distribution. We present an interactive, intuitive and efficient interaction concept based on grading features and transition functions for augmenting CAD models with smooth volumetric material gradients for multi-material fabrication using our system called GraMMaCAD (Graded Multi-Material CAD). This concept has the advantage of being extremely versatile, since the material property functions are left to the arbitrariness of the user, who is free to modify their definition as needed. By providing interactive preview modes, the system allows for efficient exploration of the volumetric gradation of the designed FGM part. We fabricated two functional examples that demonstrate the usability of the system and show the ability to quickly augment models with functionally graded material transitions.
Semantic Search With Sentence-BERT for Design Information Retrieval
Authors: Walsh Hannah S. , Andrade Sequoia R. 
Published: November 2022

Abstract
Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval (IR): semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pre-trained sBERT model performs well out-of-the-box, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over a keyword search. Our use case throughout the paper is to use queries related to specific requirements from a NASA project. Fine tuning the sBERT model on LLIS data yields a mean average precision (MAP) of 0.807 on queries based on information needs from a real NASA project. Results indicate that applying state-of-the-art natural language processing techniques, especially when fine-tuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.
Holistic Optimal Design of Face-Milled Hypoid Gearsets
Authors: Grabovic Eugeniu , Artoni Alessio , Gabiccini Marco 
Published: November 2022

Abstract
The aim of this paper is to showcase a holistic approach to design optimized spiral bevel and hypoid gearsets. The first step is the definition of the gear and pinion blanks starting from the basic transmission data. The second step is to synthesize the basic machine-tool settings required to cut the two toothed members. The gear machine-tool settings are obtained first, whereas the basic pinion settings are identified by minimizing the deviations of the pinion tooth surface from the gear conjugate member accounting for the presence of misalignments. The proposed novel identification strategy can handle all the higher-order motions while offering a remarkable speedup with respect to existing techniques. The result of the macro-geometry design phase is a conjugate spiral bevel or hypoid gearset.
As a last step, the design of the pinion micro-geometry is formulated as a multi-objective optimization problem where the obtained optimal ease-off is guaranteed to be manufacturable. To this end, an original strategy is devised where the search for the optimal tooth surface is driven by the coefficients of a free-form polynomial representation of its micro-topography. However, the tooth geometry evaluated by the loaded tooth contact solver is actually its closest manufacturable analogue. A fully worked out numerical test case substantiates the whole method.
Aspect-Sentiment-Guided Opinion Summarization for User Need Elicitation From Online Reviews
Authors: Han Yi , Moghaddam Mohsen , Suthar Meet Tusharbhai , Nanda Gaurav 
Published: November 2022

Abstract
Extracting and analyzing informative user opinion from large-scale online reviews is a key success factor in product design processes. However, user reviews are naturally unstructured, noisy, and verbose. Recent advances in abstractive text summrization provide an unprecedented opportunity to systematically generate summaries of user opinions to facilitate need finding for designers. Yet, two main gaps in the state-of-the-art opinion summarization methods limit their applicability to the product design domain. First is the lack of capabilities to guide the generative process with respect to various product aspects and user sentiments (e.g., polarity, subjectivity), and the second gap is the lack of annotated training datasets for supervised learning. This paper tackles these gaps by (1) devising an efficient and scalable methodology for abstractive opinion summarization from online reviews guided by aspects terms and sentiment polarities, and (2) automatically generating a reusable synthetic training dataset that captures various degrees of granularity and polarity. The methodology contributes a multi-instance pooling model with aspect and sentiment information integrated (MAS), a synthetic data assembled using the results of the MAS model, and a fine-tuned pretrained sequence-to-sequence model “T5” for summary generation. Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for sneakers to demonstrate the performance, feasibility, and potentials of the developed methodology. Several directions are provided for future exploration in the area of automated opinion summarization for user-centered product design.
Two-Dimensional Lineup Design of Intermediate Functional Products With Enumerative Optimization of Combinatorial Mini-Max Optimality
Authors: Fujita Kikuo , Ono Naoki , Nomaguchi Yutaka 
Published: November 2022

Abstract
As mechanical products have become further complicated and diversified, integrative design of their components, such as motors, fans, and pumps, through commonalization has become essential for shorter lead time and more cost-saving behind the mainstream. This paper names those products as intermediate functional products and proposes their lineup design method. Scale and coefficient identify their performance. Thus, they are arranged over a two-dimensional space of design requirements to minimize the lead time and production cost with maintaining performance optimality. The former cannot be measured quantitatively before actual production. Thus, minimizing the process complexity is demanded. The latter criterion translates into optimizing the worst-case performance across the requirement space. Those understanding leads to a design method by enumerating possible commonalization patterns, exclusively arranging the lineup for each pattern optimally, and investigating the best lineup through trade-off analysis. While the lineup arrangement under a pattern takes a mini-max type formulation, its optimization is performed based on monotonicity analysis efficiently. This paper demonstrates the formulation and procedures through an application to universal motors.
Human-Centric Facility Layout and Production Planning in Mixed Reality
Authors: Baroroh Dawi Karomati , Chu Chih-Hsing 
Published: November 2022

Abstract
A production system with manual operations tends to have variations in system performance induced by individual differences of human workers. Adjusting the production parameters to meet a specific target under such uncertainty can be time-consuming without the support of advanced simulation tools. Therefore, this research proposes a novel idea of facility layout and system simulation in mixed reality (MR) that facilitates production planning involving human operators. A production planning tool is implemented as an MR application to verify the feasibility of this idea using a test case of logistic facility design. In this case, a manager adjusts the current facility and production parameters designed for one product in order to operate the logistics of two different products that must achieve a given production target. An operator is responsible for manually sorting, transporting, and placing the products for storage. Both are participating in the system simulation as different roles via HoloLens. The MR planning tool provides decision-making support for the manager to quickly complete the adjustment for the target. The planning results show that the tool outperforms traditional simulation software in terms of planning quality and flexibility. Thus, this work verifies the feasibility of MR as a new approach to realizing human-centric production system design.
Towards a Porosity Aware Stochastic Framework for Computing Apparent Mechanical Properties of Additively Manufactured Parts
Authors: Iliopoulos Athanasios , Stewart Colin A. , Birnbaum Andrew J. , Steuben John C. , Rowenhorst David J. , Michopoulos John G. 
Published: November 2022

Abstract
The presence of pores in parts generated via metal Additive Manufacturing (AM) may substantially impact their mechanical performance. To understand the resulting performance, it is essential to identify the quantitative relationship between the size, shape, and location of the pores and the mechanical properties of the manufactured part. To obtain insight into this relationship, we have initiated the development of a stochastic framework that takes as input digital microscope images of AM part sections and provides as output the distribution of mechanical properties of interest such as the apparent (in the global sense) yield stress and the stress-strain response. The distribution of these pores has a semi-stochastic nature, which depends on the process type, process parameters, material type, and AM path. Firstly, we calculate various pore metrics using digital image processing techniques. The metrics are related to geometric characteristics, such as the distance of the pore from the specimen surface. Subsequently, we generate a two-dimensional distribution based on non-parametric principles. We use this distribution to sample exemplified geometries and develop multiple Finite Element Models (FEM). Then we perform virtual experiments to calculate the non-linear stress-strain response for each FEM. The results are then distributed to bins in order to generate distributions and histograms of mechanical properties of interest. We demonstrate the framework by applying it on an AM-produced conformal pressure vessel to show its capacity in computing the distribution of relevant quantities.
Spatial-Temporal Modeling Using Deep Learning for Real-Time Monitoring of Additive Manufacturing
Authors: Ko Hyunwoong , Kim Jaehyuk , Lu Yan , Shin Dongmin , Yang Zhuo , Oh Yosep 
Published: November 2022

Abstract
Real-time monitoring for Additive Manufacturing (AM) processes can greatly benefit from spatial-temporal modeling using deep learning. However, existing, deep-learning approaches in AM are case-dependent, and therefore not robust to changes of control inputs and data types. As AM is dynamic and complex, this limitation leads to a lack of systematic, DL approaches for real-time monitoring of AM, which involves a large number of varying control parameters and monitoring data. To address the challenge, this paper introduces a novel approach for developing spatial-temporal models to monitor Laser Powder Bed Fusion (LPBF) processes using deep learning on real-time, monitoring data. First, we present a novel model for representing in-situ-monitoring and control data of LPBF at multiple scales. Second, from the model, we extract spatial-temporal relationships for in-situ monitoring of LPBF processes. Third, we present a spatial-temporal, modeling approach using the architecture of convolutional long short-term memory (LSTM) to monitor the spatial-temporal relationships and detect anomalies. A case study used convolutional LSTM Autoencoder on optical, melt-pool-monitoring data, one of the most widely adopted data types in in-situ monitoring of LPBF. The data was generated from an LPBF testbed called the Additive Manufacturing Metrology Testbed. The novel, learning approach enables spatial-temporal modeling of AM dynamics directly from real-time data for the monitoring of varying AM environments. This methodical approach provides the potential to fuse realtime data at multiple, spatial-temporal scales.
Work Process Transfer Reinforcement Learning: Feature Extraction and Finetuning in Ship Collision Avoidance
Authors: Wang Xinrui , Jin Yan 
Published: November 2022

Abstract
The advancement of artificial intelligence and machine learning technologies has led to significant changes in work processes. The computer agents are applied to perform not only routine and repetitive jobs but also highly complex tasks such as driving a car and steering a ship. Given the sensory information of the environment, a reinforcement learning method has been applied for agents to learn how to perform complex tasks by trial and error through interactions with the environment. To overcome the issues such as limited and sparse training data, researchers are attempting to reuse the previously learned knowledge in new task situations. In this paper, we investigate how feature extraction and finetuning methods can be combined to allow computer agents to perform transfer reinforcement learning more effectively and efficiently in the context of ship collision avoidance. Taking a computer simulation-based empirical approach, we first develop a ship collision avoidance gameplay environment by introducing the own ship, target ships, and the base case and target cases. A deep neural network including four convolutional layers and three fully connected layers is devised for work process feature capturing through deep reinforcement learning. The case study results have shown that features do exist in work processes, and they can be captured and reused. The similarity between the source case and the target case is a key factor that determines how the feature extract and finetuning methods should be combined for effective task results and efficient learning processes.
Requirements Mapping of a High-Powered Rocket System to Explain Solution Similarities Across Generations
Authors: Jacobson Lindsey , Ferguson Scott 
Published: November 2022

Abstract
Designers have historically used existing solutions as a baseline for modifying and improving the next generation solution. However, it is also possible that certain design solutions are pursued by designers irrelevant of previous design history, suggesting that there is some dominant system architecture for a given set of requirements. While the reason for system architecture similarities can never be determined with certainty without consulting designers, it is possible to speculate about why design decisions might have been made, especially when certain system architectures and design elements have dominated a space for so long. The NASA SL Challenge serves as a compelling area for study, given that NASA SL vehicle and recovery performance requirements are similar each year, while payload integration and payload function requirements differ each year. Student design teams are tasked with designing a new launch vehicle and payload each year to meet all of the requirements. Despite yearly changes in payload requirements, some teams’ vehicle architectures remain largely unchanged, prompting a question of whether these design solutions are inherently used because of the nature of the design problem or if it is a reuse of previous design solutions. To investigate this question, we relate system requirements to vehicle elements using a DSM-style requirements mapping process. These relationships are then translated into tree structures that are centered around a single element/requirement. Finally, the tree structures are analyzed to determine why certain design solutions may have been selected based on entanglement between requirements. These mappings are used to identify areas of tension between requirements and independent variables within system architectures for a NASA SL team. Our analysis of the mappings allows us to suggest that certain elements of system architectures or mission profile may be more dominant, that is more favorable, based solely on the design problem definition. In NASA SL for example, teams could target a lower apogee because it aids in meeting entangled recovery requirements. In future work we will explore how requirements mapping can be used to identify dominant architectures during the conceptual phase of design. Finally, based on observations made in this case study, there is evidence that requirements mappings could help guide the strategic placement of excess in a system. Future work is needed to determine how requirements mapping could support the placement of excess and to explore how to conduct requirements mapping in a streamlined and efficient way.
Gerotor Pump Simulation Modules for Enhancing Fluid Power Education
Authors: Azzam Israa , Dhar Sujan , Patil Veeranagouda , Maiti Dipak , Asunda Paul , Garcia Bravo Jose , Breidi Farid 
Published: November 2022

Abstract
Fluid power is one of the courses required for most engineering technology programs and thus enrolls more than 200 students annually. Studies show that fluid power modules related to hydraulic and pneumatic systems result in a relatively high W/D/F rate (Withdrawals, D and F grades), where many students struggle to connect the theory with practice. From a pedagogical perspective, this rate is due to the traditional laboratory syllabus that is insufficient to address the complexity of hydraulic and pneumatic systems. This work aims to enhance fluid power education by conducting a research study for testing and examining the efficacy of integrating state-of-the-art simulation courseware into the laboratory work of fluid power courses. For conducting the study, interfaceable Computational Fluid Dynamics (CFD) modules of a hydraulic Gerotor pump are developed and incorporated into the laboratory syllabus of the MET:23000 Fluid Power course at Purdue University. The CFD simulation modules were designed and implemented in the labs in collaboration with Simerics. A survey was designed and conducted with 93 students to measure their perceived learning of fluid power concepts and examine their attitudes toward the CFD simulation used for instruction. The survey outcomes revealed that the executed CFD simulation assisted the students in understanding the overall operation of the Gerotor pump. The results show that the simulation courseware supported students in acquiring the fundamentals of fluid power, which could assist them in conducting future engineering decisions. Besides the conceptual understanding, students were engaged in the lab modules. They enjoyed visualizing the variation in pressure contours, velocity vectors, and cavitation bubbles.