bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Robot Path Planning Method Based on LSTM Neural Network and Rapidly-Exploring Random Tree Algorithm
Authors: Hu Weifei , Tang Feng , Liu Zhenyu , Tan Jianrong 
Published: November 2021

Abstract
As an important field of robot research, robot path planning has been studied extensively in the past decades. A series of path planning methods have been proposed, such as A* algorithm, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmaps (PRM). Although various robot path planning algorithms have been proposed, the existing ones are suffering the high computational cost and low path quality, due to numerous collision detection and exhausting exploration of the free space. In addition, few robot path planning methods can automatically and efficiently generate path for a new environment. In order to address these challenges, this paper presents a new path planning algorithm based on the long-short term memory (LSTM) neural network and traditional RRT. The LSTM-RRT algorithm first creates 2D and 3D environments and uses the traditional RRT algorithm to generate the robot path information, then uses the path information and environmental information to train the LSTM neural network. The trained network is able to promptly generate new path for randomly generated new environment. In addition, the length of the generated path is further reduced by geometric relationships. Hence, the proposed LSTM-RRT algorithm overcomes the shortcomings of the slow path generation and the low path quality using the traditional RRT method.
Capability Language Processing (CLP): Classification and Ranking of Manufacturing Suppliers Based on Unstructured Capability Data
Authors: Zandbiglari Kimia , Ameri Farhad , Javadi Mohammad 
Published: November 2021

Abstract
The unstructured data available on the websites of manufacturing suppliers can provide useful insights into the technological and organizational capabilities of manufacturers. However, since the data is often represented in an unstructured form using natural language text, it is difficult to efficiently search and analyze the capability data and learn from it. The objective of this work is to propose a set of text analytics techniques to enable automated classification and ranking of suppliers based on their capability narratives. The supervised classification and semantic similarity measurement methods used in this research are supported by a formal thesaurus that uses SKOS (Simple Knowledge Organization System) for its syntax and semantics. Normalized Google Distance (NGD) was used as a metric for measuring the relatedness of terms. The proposed framework was validated experimentally using a hypothetical search scenario. The results indicate that the generated ranked list shows a high correlation with human judgment specially if the query concept vector and supplier concept vector belong to the same class. However, the correlation decreases when multiple overlapping classes of suppliers are mixed together. The findings of this research can be used to improve the precision and reliability of Capability Language Processing (CLP) tools and methods.
Self-Optimizing Loop Sifting and Majorization for 3D Reconstruction
Authors: Zhang Guoxiang , Chen YangQuan 
Published: November 2021

Abstract
Visual simultaneous localization and mapping (vSLAM) and 3D reconstruction methods have gone through impressive progress. These methods are very promising for autonomous vehicle and consumer robot applications because they can map large-scale environments such as cities and indoor environments without the need for much human effort. However, when it comes to loop detection and optimization, there is still room for improvement. vSLAM systems tend to add the loops very conservatively to reduce the severe influence of the false loops. These conservative checks usually lead to correct loops rejected, thus decrease performance. In this paper, an algorithm that can sift and majorize loop detections is proposed. Our proposed algorithm can compare the usefulness and effectiveness of different loops with the dense map posterior (DMP) metric. The algorithm tests and decides the acceptance of each loop without a single user-defined threshold. Thus it is adaptive to different data conditions. The proposed method is general and agnostic to sensor type (as long as depth or LiDAR reading presents), loop detection, and optimization methods. Neither does it require a specific type of SLAM system. Thus it has great potential to be applied to various application scenarios. Experiments are conducted on public datasets. Results show that the proposed method outperforms state-of-the-art methods.
Corrugated Diaphragm Actuator for Soft Robotic and Exoskeleton Applications
Authors: Erel Veysel , Lindsay Alexandra R. , Singh Inderjeet , Wijesundara Muthu B. J. 
Published: November 2021

Abstract
Soft robotics is projected to have a significant impact on healthcare, industry, and the military to deliver assistance in rehabilitation, daily living activities, repetitive motion tasks, and human performance augmentation. Many attempts have been made for application-specific robotic joints, robots, and exoskeletons using various actuator types, materials, and designs. The progress of creating soft robotic systems can be accelerated if a set of actuators with defined characteristics were developed, similar to conventional robotic actuators, which can be assembled to create desired systems including exoskeletons and end effectors. This work presents such an attempt by designing a modular corrugated diaphragm actuator that can apply linear displacement, force, and bending motion. This modular actuator approach allows for creating various robotic joints by arranging them into different configurations. Numerical simulation, fabrication, and testing were carried out to evaluate the displacement, force, and bending characteristics of the corrugated diaphragm actuator as a single unit and in multi-unit arrays to understand their applicability for different scenarios. Actuator arrays that are configured in a serial and parallel manner were investigated. The results will be presented in terms of using this modular actuator concept to create single and multi-DOF joints, which will demonstrate the versatility of this modular actuator approach.
Online Product Review Analysis to Automate the Extraction of Customer Requirements
Authors: Mokadam Aashay , Shivakumar Shrikrishna , Viswanathan Vimal , Agumbe Suresh Mahima 
Published: November 2021

Abstract
The increasing use of online retail platforms has generated an enormous amount of textual data on the user experiences with these products in online reviews. These reviews provide a rich resource to elicit customer requirements for a category of products. The recent research has explored this possibility to some extent. The study reported here investigates the coding of publically available user reviews to understand customer sentiments on environmentally-friendly products. The manual review typically consists of a qualitative analysis of textual content, which is a resource-intensive process. An automated procedure based on Aspect-Based Sentiment Analysis (ABSA) is proposed and explored in this study. This procedure can be beneficial in analyzing reviews of products that belong to a specific category. As a case study, environmentally-friendly products are used. Manual content analysis and automated ABSA-based analysis are performed on the same review data to extract customer sentiments. The results show that we obtain over a 50% classification accuracy for a multiclass classification NLP task with a very elementary word vector-based model. The drop in accuracy (compared to human annotation) can be offset because an automated system is thousands of times faster than a human. Given enough data, it will perform better than its human counterpart in tasks on customer requirement modeling. We also discuss the future routes that can be taken to extend our system by leveraging more sophisticated paradigms and substantially improving our system’s performance.
Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors
Authors: Wang Liwei , Yerramilli Suraj , Iyer Akshay , Apley Daniel , Zhu Ping , Chen Wei 
Published: November 2021

Abstract
Scientific and engineering problems often require an inexpensive surrogate model to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners in surrogate modeling, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for a growing number of data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent variable Gaussian process (LVGP) model where qualitative factors are mapped into a continuous latent space to enable GP modeling of mixed-variable datasets. By extending variational inference to LVGP models, the large training dataset is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multi-type responses. Comparative studies demonstrate that the proposed method scales well for large datasets with over 104 data points, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of qualitative factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.
Feedback Control of the Locomotion of a Tailed Quadruped Robot
Authors: Liu Yujiong , Ben-Tzvi Pinhas 
Published: November 2021

Abstract
The traditional locomotion paradigm of quadruped robots is to use dexterous (multi degrees of freedom) legs and dynamically optimized footholds to balance the body and achieve stable locomotion. With the introduction of a robotic tail, a new locomotion paradigm becomes possible as the balancing is achieved by the tail and the legs are only responsible for propulsion. Since the burden on the leg is reduced, leg complexity can be also reduced. This paper explores this new paradigm by tackling the dynamic locomotion control problem of a reduced complexity quadruped (RCQ) with a pendulum tail. For this specific control task, a new control strategy is proposed in a manner that the legs are planned to execute the open-loop gait motion in advance, while the tail is controlled in a closed-loop to prepare the quadruped body in the desired orientation. With these two parts working cooperatively, the quadruped achieves dynamic locomotion. Partial feedback linearization (PFL) controller is used for the closed-loop tail control. Pronking, bounding, and maneuvering are tested to evaluate the controller’s performance. The results validate the proposed controller and demonstrate the feasibility and potential of the new locomotion paradigm.
Analysis of the Knowledge Gain and Cognitive Load Experienced Due to the Computer-Aided Instruction of Additive Manufacturing Processes
Authors: Mathur Jayant , Miller Scarlett R. , Simpson Timothy W. , Meisel Nicholas A. 
Published: November 2021

Abstract
Although there is a substantial growth in the Additive Manufacturing (AM) market commensurate with the demand for products produced by AM methods, there is a shortage of skilled designers in the workforce that can apply AM effectively to meet this demand. This is due to the innate complications with cost and infrastructure for high-barrier-to-entry AM processes such as powder bed fusion when attempting to educate designers about these processes through in-person learning. To meet the demands for a skilled AM workforce while also accounting for the limited access to the range of AM processes, it is important to explore other mediums of AM education such as computer-aided instruction (CAI) which can increase access to hands-on learning experiences. Therefore, the purpose of this paper is to analyze the use of CAI in AM process education and focus on its effects on knowledge gain and cognitive load. Our findings show that when designers are educated about material extrusion and powder bed fusion through CAI, the knowledge gain for powder bed fusion is significantly different than knowledge gain for material extrusion, with no significant difference in cognitive load between these two AM processes. These findings imply that there is potential in virtual mediums to improve a designer’s process-centric knowledge for the full range of AM processes including those that are usually inaccessible. We take these findings to begin developing recommendations and guidelines for the use of virtual mediums in AM education and future research that investigates implications for virtual AM education.
BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design
Authors: Regenwetter Lyle , Curry Brent , Ahmed Faez 
Published: November 2021

Abstract
In this paper, we present “BIKED,” a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset, then highlight some prominent research questions that BIKED can help address. Of these questions, we further explore the following in detail: 1) Are there prominent gaps in the current bicycle market and design space? We explore the design space using unsupervised dimensionality reduction methods. 2) How does one identify the class of a bicycle and what factors play a key role in defining it? We address the bicycle classification task by training a multitude of classifiers using different forms of design data and identifying parameters of particular significance through permutation-based interpretability analysis. 3) How does one synthesize new bicycles using different representation methods? We consider numerous machine learning methods to generate new bicycle models as well as interpolate between and extrapolate from existing models using Variational Autoencoders. The dataset is available at http://decode.mit.edu/projects/biked/ along with referenced code.
Investigating Mind-Mapping as a Tool for Problem Exploration in Early Design
Authors: Chen Ting-Ju , Vyas Shantanu , Krishnamurthy Vinayak R. 
Published: November 2021

Abstract
We present an experiment to study the role of mind-mapping as a tool for design opportunity identification and problem understanding. Our goal is to investigate how the quality of design opportunity statements change with two different techniques, namely, mind-mapping and free writing. Identifying design opportunities is an important step in new product development and little is currently understood in terms of what tools can provide cognitive support for problem clarification. In this work, we focus on mind-mapping as one example of a potential tool for providing such support. Mind-maps are well-known for their ability to enable the exploration of ideas in an unconstrained and structured way. To study their role in helping problem exploration, we conducted a between-subject user study with 28 participants to investigate how information structure and organization affect the exploration of ideas in a given design context. Further, we propose new evaluation metrics to quantitatively assess key elements presented in the design opportunity statements generated after exploring the problem domain. We report on the quantitative results, the exploration behaviors, and the general user feedback about the experience. Finally, we discuss the implications of these findings on design problem identification and future digital mind-mapping tools for exploratory tasks.
A Framework for Interactive Structural Design Exploration
Authors: Valdez Sofia , Seepersad Carolyn , Kambampati Sandilya 
Published: November 2021

Abstract
Rapid advances in additive manufacturing and topology optimization enable unprecedented levels of design freedom for realizing complex structures. The challenge is that the increasing design freedom is accompanied by increasing complexity, such that it can become difficult for either computational algorithms or human designers alone to search these expansive design spaces effectively. Our goal is to establish an interactive design framework that is both data-driven and designer-guided so that human designers can work together with computational algorithms to search structural design spaces more effectively. The framework builds upon classical topology optimization techniques to build a library of designs for a class of problems. A conditional generative adversarial network (cGAN) is trained to establish a latent representation of the library and to support rapid exploration of candidate designs. The library of designs is clustered based on visual similarity. The user selects clusters with desirable features, and the underlying latent representation is manipulated to generate visually similar candidate designs with adjustable levels of diversity or similarity to the selected clusters. The framework enables designers to use their expertise and intuition to guide the algorithm towards promising solutions by screening designs quickly and eliminating clusters of designs that may not be desirable for reasons that are difficult to embed within the optimization itself but are recognizable and significant to a human designer (e.g., secondary functionality, aesthetics).
A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems
Authors: Shi Zhangyue , Liu Chenang , Kan Chen , Tian Wenmeng , Chen Yang 
Published: November 2021

Abstract
With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality monitoring and control. However, the cyber-enabled environment may pose the collected online stream sensor data under high risks of cyber-physical attacks as well. Specifically, cyber-physical attacks could occur during the manufacturing process to maliciously tamper the sensor data, which could result in false alarms or failures of anomaly detection. In addition, the cyber-physical attacks may also illegally access the collected data without authorization and cause leakage of key information. Therefore, it becomes critical to develop an effective approach to protect online stream data from these attacks so that the cyber-physical security of the manufacturing systems could be assured. To achieve this goal, an integrative blockchain-enabled method, is proposed by leveraging both asymmetry encryption and camouflage techniques. A real-world case study that protects cyber-physical security of collected stream data in additive manufacturing is provided to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well.
Identifying Computer-Aided Design Action Types From Professional User Analytics Data
Authors: Leonardo Kevin , Olechowski Alison 
Published: November 2021

Abstract
Inspired by popular personality type indicators, we develop a framework for classifying individuals by their computer-aided design (CAD) behaviours. We are motivated by the trend of modern CAD software towards cloud platforms and expanded collaborative features. Cloud-CAD platforms enable collaboration by increasing access, and reducing conflicts and barriers to file-sharing.
In order to generate insight to support CAD collaboration, we analyze the real-world data from an industry partner’s product development project, consisting of eight professional designers working on a cloud-CAD platform. This data corresponds to more than 1,420,000 actions over a span of eight months. Via hierarchical clustering, we group 79 unique CAD activities into 14 categories of CAD action groups, such as Part Studio, Assembly, Comment, View/Scan and Export. Next, we identify the degree to which each of the eight designers performs activity in these CAD action groups. We demonstrate the usefulness of this framework by highlighting insights revealed by the CAD action group mapping, confirmed via discussion with the industry partner. This CAD-type behaviour framework provides a tool for assessment and reflection on the types of behavioural tendencies present or missing on a team of designers. It can assist CAD educators and trainees in understanding their comprehensive CAD learning trajectory. Future extensions of the framework could leverage artificial intelligence techniques to provide real-time feedback on designer roles.
Development of a Design for Additive Manufacturing Worksheet for Medical Casts
Authors: Noh Heena , Park Kijung , Park Kiwon , Okudan Kremer Gül E. 
Published: November 2021

Abstract
Traditional plaster casts often cause dermatitis due to disadvantages in usability and wearability. Additive manufacturing (AM) can fabricate customized casts to have light-weight, high strength, and better air permeability. Although existing studies have provided design for additive manufacturing (DfAM) guidelines to facilitate design applications for AM, most relevant studies focused on the mechanical properties of outputs and too general/specific design guidelines; novice designers may still have difficulty understanding trade-offs between functional and operational performance of various DfAM aspects for medical casts. As a response, this study proposes a DfAM worksheet for medical casts to effectively guide novice designers. First, important DfAM criteria and their possible solutions for medical casts are examined through a literature review to construct a basic DfAM framework for medical casts. Next, a scoring system that considers relative criteria importance and criteria evaluation from both functional and operational perspectives is developed to identify the overall suitability of a medical cast design for AM. A case study of finger cast designs was performed to identify the DfAM performance of the sample designs along with redesign requirements suggested by the worksheet. The proposed worksheet would be used to achieve rapid medical cast design by objectively assessing its suitability for AM.