bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast Two-Scale Analysis via Clustering
Authors: Yuan Chongxi , Liu Xingchen
Published: November 2021

Both man-made and natural materials exhibit heterogeneous properties at smaller observation scales. The multiscale analysis allows the inclusion of fine-scale information in coarse-scale simulations. One of the commonly used methods is homogenization, replacing the detailed fine-scale structures with their locally homogeneous effective material properties. When fine-scale material structures are stationary, representative volume elements (RVE) are often identified for their effective material properties to be applied over the entire structure. However, in non-stationary material structures, it is inappropriate to assume a single representative material. In this case, homogenization is often required for every individual cell, resulting in significant increases in computational cost.

We propose a stiffness-based clustering algorithm that reduces the total number of homogenization computations needed for multiscale analysis. Cells with similar effective stiffness tensors are clustered together such that only a single homogenization is required for each cluster. Specifically, the clustering algorithm is based on the novel concept of Eigenstiffness, which represents the relative directional stiffness of a given material structure. The rotation invariant property of Eigenstiffness allows material structure with similar intrinsic stiffness but different orientations to be clustered together, further decreasing the number of clusters required for the multiscale analysis. Without a priori knowledge of the accurate homogenized material properties, approximated elasticity tensors and Eigenstiffness estimated through FFT-based homogenization methods are used for rapid clustering. The effectiveness of the method is verified by numerical simulations on various multiscale structures, including Voronoi foams and fiber-reinforced composites.
Towards a Synthesis Method of Kresling Tower Used as a Compliant Building Block
Authors: Berre John , Geiskopf Francois , Rubbert Lennart , Renaud Pierre
Published: November 2021

In this paper, the use of the Kresling tower origami as a building block for compliant mechanism design is considered. Two contributions are introduced to develop a synthesis method of such a building block. First, models to link the origami pattern geometry to the Kresling tower kinematics are derived. The position of stable configurations, the lead angle of its helical motion are expressed as functions of the pattern parameters. Experimental validation of the models is performed. Second, a modification of pattern by local adjustment of fold geometry is introduced. This aims at modifying the origami stiffness without affecting the kinematics. The use of modified fold geometries is experimentally investigated. The capacity to strongly modify the stiffness level is observed, which is encouraging to go towards a synthesis method with decoupling of kinematics and stiffness selection.
On the Mechanical Behaviour in Stiffness Compensated Piezoelectric Beams - An Experimental Investigation Towards Energy Harvesting
Authors: van de Wetering E. , Blad T. W. A. , van Ostayen R. A. J.
Published: November 2021

In this work, a piezoelectric beam is stiffness compensated through adding a negative stiffness formed by attracting magnets. The mechanism’s purpose is low-frequency energy harvesting. The effect of deformation speed on the beam’s stiffness is investigated by force-displacement measurements taken at different speeds and with different load resistors connected. The effect of the load resistance on the beam’s stiffness has been found to be strongly dependent on the deformation speed. A load that results in the same stiffness as in a closed circuit at low deformation speed results in a stiffer response at a faster deformation speed. Also, when the beam is brought close to static balance with a certain load resistance connected, alteration of the load resistance has a great influence on the attained stiffness level. Furthermore, memory effects in the hysteresis found in piezoelectric actuators, related between input voltage and displacement, were also confirmed between displacement and force in sensor application.
A Mirror-Symmetrical XY Compliant Parallel Manipulator With Improved Performances Without Increasing the Footprint
Authors: Zhu Jiaxiang , Hao Guangbo , Li Shiyao , Yu Shuwen , Kong Xianwen
Published: November 2021

The design of XY compliant parallel manipulators (CPMs) remains challenging considering the tradeoff between the mirror-symmetry for better constrained undesired rotations and the small footprint, although a significant number of XY CPMs have been reported in extensive applications. This paper presents a new XY CPM using mirror-symmetry without increasing its footprint, mainly aiming to reduce the undesired parasitic rotations of input and output motion stages. The concept of higher degree of stiffness centre symmetry is deployed to tackle the parasitic rotations, leading to a multi-layer compact XY CPM design with each layer being a rotation-symmetrical design. A nonlinear and analytical model of the proposed XY CPM is derived using free body diagrams and the Beam Constrained Model (BCM) to accurately analyse its performance characteristics over a large range of motion. The designed XY CPM is then verified by the nonlinear finite element analysis (FEA) method. Finally, the proposed multi-layer design is comprehensively compared with the well-received single-layer rotation-symmetrical design. It is shown in the new design that the parasitic rotations of input and output stages along with the actuator isolation are significantly reduced and that the out-of-plane-stiffness is also significantly increased.
Automatic Differentiation in Automatic Generation of the Linearized Equations of Motion
Authors: Minaker Bruce , González Francisco
Published: November 2021

In the ongoing search for mathematically efficient methods of predicting the motion of vehicle and other multibody systems, and presenting the associated results, one of the avenues of continued interest is the linearization of the equations of motion. While linearization can potentially result in reduced fidelity in the model, the benefits in computational speed often make it the pragmatic choice. Linearization techniques are also useful in modal and stability analysis, model order reduction, and state and input estimation. This paper explores the application of automatic differentiation to the generation of the linearized equations of motion. Automatic differentiation allows one to numerically evaluate the derivative of any function, with no prior knowledge of the differential relationship to other functions. It exploits the fact that every computer program must evaluate every function using only elementary arithmetic operations. Using automatic differentiation, derivatives of arbitrary order can be computed, accurately to working precision, with minimal additional computational cost over the evaluation of the base function. There are several freely available software libraries that implement automatic differentiation in modern computing languages. In the paper, several example multibody systems are analyzed, and the computation times of the stiffness matrix are compared using direct evaluation and automatic differentiation. The results show that automatic differentiation can be surprisingly competitive in terms of computational efficiency.
Design of Path Tracking Controller for Autonomous Vehicles Through Bias Learning of Vehicle Dynamic Models Under Environmental Uncertainty
Authors: Ren Lichuan , Xi Zhimin
Published: November 2021

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.
Design and Actuation of a Skeleton for a Robotic Fish
Authors: Abulon Dina Joy K. , Li Jiaji , McCarthy J. Michael
Published: November 2021

In this paper, we present the design of a pneumatically actuated skeleton for a robotic fish. The tail is designed as a one degree of freedom coiling truss that is actuated by air pressure supplied to pouch actuators along the truss. We present that kinematic synthesis procedure, the fabrication and testing of the fish tail system. Our goal is an efficient and effective fish-like locomotion system.
Research on the Potential of Front Wheel Steering Control for Vehicle Dynamics Control
Authors: Zheng Sheng , Cheng Yiming , Yu Liangyao
Published: November 2021

The development of active steering control technology not only provides key actuators for intelligent vehicle motion control, but also expands vehicle stability and safety. This paper studies the potential control ability of the front-wheel steering control to the vehicle plane dynamics, and the controllable area boundary is designed on the phase plane of side slip angle and yaw rate. Previous studies have defined a dynamics stable area on the vehicle states phase plane, in which the vehicle state can autonomously return to a stable equilibrium point. The area outside the stable area are divided into the controllable area and the uncontrollable area in this paper. In the controllable area, the front-wheel steering control has the ability to pull the vehicle states back towards the stable area. Considering actuator constraints and model errors, based on the principle of safety design, a band-shaped critical area is designed to separate the controllable area from the uncontrollable area, and the linear mathematical model of the controllable area boundary is designed. In order to verify the rationality of the controllable area definition, nonlinear model predictive controller is designed to control the vehicle outside the dynamics stable area. The controller uses the high-fidelity nonlinear vehicle model and the magic formula tire model as the state equation constraints, and the practical steering actuator constraints are used as the control input constraints, and the nonlinear numerical optimization solver is used to solve the optimal steering input sequence. The phase plane analysis of the controlled vehicle verifies the rationality of the controllable area defined in this paper.
Thermal Modeling Using Two-Port Network Impedance Fractional-Order Approximations
Authors: Duhé Jean-François , Victor Stéphane , Melchior Pierre , Abdelmounen Youssef , Roubertie François
Published: November 2021

Sufficiently accurate thermal modeling is necessary for many applications such as heat dissipation, melting processes, building design or even bio-heat transfers in surgery. Circuit models help modeling heat transfer dynamics: this method is simple and is often used to model thermal phenomena. However, such models well approximates low and high frequency behavior but they are not accurate enough in the middle band of interest, thus lacking of precision in dynamical terms. A more complete and accurate description of conductive heat transfer can be obtained by using a two-port network. The resulting analytical expressions are complex and nonlinear in the frequency ω. This complexity in the frequency domain is difficult to handle when it comes to control applications and more specifically in real-time applications such as surgery. Consequently, an analysis of this thermal two-port network in the frequency domain directly leads to fractional-order systems. A frequency domain analysis of the series and shunt impedances will be presented and different approximations will be explored in order to obtain simple but sufficiently precise linear fractional transfer function models. The series impedances are approximated by using asymptotic and pole-zero approximations and the shunt impedance is approximated by using a capacitance approximation and two fractional model approximations.
If a Picture is Worth 1000 Words, Is a Word Worth 1000 Features for Design Metric Estimation?
Authors: Edwards Kristen M. , Peng Aoran , Miller Scarlett R. , Ahmed Faez
Published: November 2021

A picture is worth a thousand words, and in design metric estimation, a word may be worth a thousand features. Pictures are awarded this worth because of their ability to encode a plethora of information. When evaluating designs, we aim to capture a range of information as well, information including usefulness, uniqueness, and novelty of a design. The subjective nature of these concepts makes their evaluation difficult. Despite this, many attempts have been made and metrics developed to do so, because design evaluation is integral to innovation and the creation of novel solutions. The most common metrics used are the consensual assessment technique (CAT) and the Shah, Vargas-Hernandez, and Smith (SVS) method. While CAT is accurate and often regarded as the “gold standard,” it heavily relies on using expert ratings as a basis for judgement, making CAT expensive and time consuming. Comparatively, SVS is less resource-demanding, but it is often criticized as lacking sensitivity and accuracy. We aim to take advantage of the distinct strengths of both methods through machine learning. More specifically, this study seeks to investigate the possibility of using machine learning to facilitate automated creativity assessment. The SVS method results in a text-rich dataset about a design. In this paper we utilize these textual design representations and the deep semantic relationships that words and sentences encode, to predict more desirable design metrics, including CAT metrics. We demonstrate the ability of machine learning models to predict design metrics from the design itself and SVS Survey information. We demonstrate that incorporating natural language processing (NLP) improves prediction results across all of our design metrics, and that clear distinctions in the predictability of certain metrics exist. Our code and additional information about our work are available at http://decode.mit.edu/projects/nlp-design-eval/.
On the Structural Constraint and Motion of 3-PRS Parallel Kinematic Machines
Authors: Nigatu Hassen , Ho Choi Yun , Kim Doik
Published: November 2021

This paper presents a consistent analytic kinematic formulation of the 3-PRS parallel manipulator (PM) with a parasitic motion by embedding the velocity level structural constraint equation into the motion expression. Inverse rate kinematics (IRK) is solved with a simple constraint compatible velocity profile, which is obtained by projecting the instantaneous restriction space onto the motion space. Moreover, the systematic method to reveal the parasitic motion is introduced. Thus, the parasitic terms are automatically identified from the main motions. Unlike the usual approach, this study does not consider any explicit parasitic motion expression. Consequently, the derivation of constraint compatible input velocity, which comprises the parasitic term, is simplified. To incorporate the parasitic motion into the task velocity, constraint Jacobian of the manipulator is analytically obtained first. The manipulator Jacobian is extended to incorporate the passive joint’s information apart from the active joints and structural constraint. Hence, the dimension of the Jacobian matrix used to solve IRK is 9 × 6. The validity of the IRK is proved by the Bordered Gramian based forward rate kinematics (FRK). Then, an accurate numerical integration, RK4, is applied to the joint velocity of IRK to obtain the manipulator’s joint values. Consequently, the moving plate’s pose is obtained via forward position kinematics computed using integrated active and passive joint values for validation. The projection matrix used to get compatible constraint motion adjusts our input velocity and makes it compatible with the structural constraint policy, and the parasitic motion is embedded easily. Thus, an explicit formulation of the parasitic motion equation is not required, as the usual practice. Finally, the study presented numerical simulations to show the validity of the outlined resolutions. This paper’s result and analysis can be uniformly applied to other parallel manipulators with less than 6 DoFs.
Virtual Reality (VR) for the Support of the Analysis and Operation of a Solar Thermal Tower Power Plant
Authors: Mahboob Kamran , Mahboob Atif , Husung Stephan
Published: November 2021

A substantial part of the global energy mix depends upon fossil fuels that needed to be reduced to overcome the pollution and environment-related challenges. This has directed the world to shift the energy mix towards renewable energy technologies. Among the development in renewable energy technologies, the development of solar tower power plant is an active research topic. Over the past decade, advances in computers and simulation software systems have greatly expanded their use in design and development, which can facilitate the engineering activities of solar tower power plants. However, an important limitation is the visualization of three-dimensional geometrical design data onto two-dimensional computer screens. VR technologies are a great means in the visualization of 3D data. Therefore, this article attempts to illustrate a concept for the application of VR technologies in the development of solar tower power plant and lists down relevant support scenarios. The main focus of the paper is on analyzing the efficiency of the VR technology used in the design of solar tower power plants and learning from the experience gained in this process. A discussion about further scenarios ranging from on-site visualization of solar tower power plant infrastructure, installation and repair, cleaning and maintenance, etc. is included as well as future directions are pointed out. The demonstrator part consists of an Android Smartphone-based VR application and an HMD based VR application. Furthermore, a brief comparison of both the applications as well as of HMD and sVR is also provided.
Integrating User Preference Into Improved Home Appliance Scheduling
Authors: Starks Jacob , Song Li , Allen Janet K. , Mistree Farrokh
Published: November 2021

Primary Question – How can smart appliance networks integrate human preference to enhance appliance scheduling?

To deal with user preference variability, where the physical network interacts with human behavior, the most effective method is a flexible Graphical User Interface (GUI), or dashboard. In this work a dashboard is developed to make a more flexible model, this dashboard can account for variability in load preference, goal preference and appliance specifications, allowing consumers to plan loads on their specific network of household appliances in order to schedule a preferred time and evaluate the costs of certain load timing, given the desire to minimize the cost of electricity, avoid exceeding a peak load with minimal deviations from the user preferred schedule. As a result, uncertainty due to users is mitigated, such that only uncertainty in the load cycles themselves had to be managed, and that management could be done with greater robustness and computational efficiency. Consequently, this provides a model for developing more computationally efficient and robust scheduling patterns for household appliances. In this paper, household appliances are treated as an interdependent network to find satisficing solutions for timing loads to minimize electric cost, peak load, and deviation from the preferred time of scheduling.
Influence of Realistic Virtual Environments and Humanlike Avatars on Patients With Social Phobia
Authors: Stefanova Milena , Pillan Margherita , Gallace Alberto
Published: November 2021

The practice of treating phobias with Virtual Reality-based therapies is a well-established field. Understanding the level of realism required by the therapy to be most effective is an essential matter of study.

This research aims to explore the effects of visual realism on the emotional response in subjects with social phobia when exposed to VR-based applications. Social phobias are triggered by the presence of other people, which translated into virtual environments, refers to avatars. Our hypothesis is that patients with social phobia experience different emotional response to humanlike avatars compared to people without social phobia.

To try the hypothesis, a prototype-based survey is conducted. Three types of avatars are implemented with different levels of human likeness: low, medium, and high. The analysis of the collected data suggests that for people with social phobias the anxiety is lowest for avatars with high levels of human likeness. This result is in direct contrast with the uncanny valley effect theory.

The research explores how we should design virtual environments to make them more effective in the treatment of phobias. Moreover, the research produces new knowledge about the perception of humanlike avatars in virtual reality.
Design and Kinematic Analysis of a Novel 2-DoF Closed-Loop Mechanism for the Actuation of Machining Robots
Authors: Ginnante Angelica , Leborne François , Caro Stéphane , Simetti Enrico , Casalino Giuseppe
Published: November 2021

The essential characteristics of machining robots are their stiffness and their accuracy. For machining tasks, serial robots have many advantages such as large workspace to footprint ratio, but they often lack the stiffness required for accurately milling hard materials. One way to increase the stiffness of serial manipulators is to make their joints using closed-loop or parallel mechanisms instead of using classical prismatic and revolute joints. This increases the accuracy of a manipulator without reducing its workspace. This paper introduces an innovative two degrees of freedom closed-loop mechanism and shows how it can be used to build serial robots featuring both high stiffness and large workspace. The design of this mechanism is described through its geometric and kinematic models. Then, the kinematic performance of the mechanism is analyzed, and a serial arrangement of several such mechanisms is proposed to obtain a potential design of a machining robot.
Design of Light Weight-Low Cost Remotely Operated Underwater Vehicle
Authors: Salah Salah , El Telbany Kareem , Samy Bavly , Khalil Ahmed , El-Ghanzory Karim , Hussein Wessam , Yacoub Mostafa
Published: November 2021

Remotely Operated Underwater Vehicles (ROV) currently have been utilized for scientific and commercial applications. Many industries are involved in developing robots in order to reduce human effort as well as increase productivity, efficiency, and monitoring. That said, the need to optimize the cost for design of ROVs became popular. In the present work, a simplified design procedure of a low cost-light-weight ROV is proposed. The design overview includes a description of the three thrusters operated by bilge pump electric motors. The ROV contains only a camera, an IMU and a GPS sensor. The ROV is wired to a control station that is equipped with a screen with a GUI, a joystick and a keyboard to monitor and control the ROV. The design is optimized to achieve a neutral buoyancy force in order to reduce the stabilizing effort during operation, hence reducing energy consumption. The drag force, lift force, drag center, lift center and fluid velocity contours are predicted using a CFD tool. Dynamic analysis including thrust and drag forces in longitudinal, vertical and yaw directions of motion are investigated to facilitate selection of the prime movers. Finally, the control strategy and electrical system description is presented. The proposed methodology proved that a satisfactory performance with an optimized design is achievable. The ROV design presented could be used in quick and low cost inspection of ships.
Health Monitoring Using Acoustic Emission Technique During Fused Filament Fabrication Printing Process
Authors: Xu Ke , Manoochehri Souran
Published: November 2021

Fused Filament Fabrication (FFF) is one of the most popular additive manufacturing technologies for manufacturing prototypes with various complex geometries. However, current commercial FFF machines have limitations in terms of process reliability and product quality. In order to overcome these limitations and improve the accuracy and reliability of these machines, a real-time monitoring system is needed to make sure that any part defects can be detected during the printing process and printing parameters can be identified that can be modified to resolve the printing anomalies resulting in minimization of waste and improvement of efficiency. In this study, a method for in-situ monitoring of FFF machine conditions is proposed utilizing an acoustic emission (AE) technology. The AE sensor is used to monitor the vibration signals generated during the whole printing process in real-time. The AE signal is then analyzed and processed, and categorized according to the selected objective characteristics. The proposed method can be utilized to identify the abnormal states of machine conditions. The time-domain features of AE hits after post-processing are used as key indicators. Experimental results show that this method has the potential to be used as a non-invasive diagnostic and prognostic tool for FFF machine maintenance and process control.
Orthopedic Bone-Drilling Assessment Through Laplacian-Based Trajectory Noise Characterization
Authors: Mohanty Ronak R. , Vyas Shantanu , Nigam Aman , Tai Bruce L. , Krishnamurthy Vinayak R.
Published: November 2021

Assessment techniques for orthopedics training are primarily subjective, and often based on qualitative metrics. In this paper, we propose an analytical approach for the quantitative assessment of orthopedic surgery training, specifically, bone drilling. Our goal in this paper is to help improve orthopedics training by providing a means to assess the resident training progress. To this end, we introduce a novel metric that assigns a unique signature to an individual’s drilling activity based on their drilling trajectory, and we compare it with the signatures of expert surgeons. We conduct a simple bone-drilling experiment with surgeons (experts) and novice users on a hybrid (physical - digital) setup consisting of 3D printed bone surrogates that emulate physical and perceptual properties of a human bone across the young and old age groups. We create expert models using our drilling signature metric to evaluate drilling performance for novice users with respect to expert orthopedic surgeons. Our preliminary analysis of drilling signatures across expert and novice users showcases a perceivable distinction across two different bone types highlighting some fundamental insights on the drilling setup, bone material, and user response to each bone type. Our results indicate that the drilling signature helps capture not only a novice user’s drilling behavior, but also their relative expertise as they progress with training.
Sustainable Creativity: Overcoming the Challenge of Scale When Repurposing Wind-Turbine Blades
Authors: Arabian K. , Shu L. H.
Published: November 2021

Increased adoption of wind-energy technology helps address climate change, but also requires disposition of retired wind-turbine blades that are not easily recycled. This pressing environmental problem is used as the prompt in a creativity study, where participants are asked to identify potential reuses in a Wind-turbine-blade Repurposing Task (WRT). In past iterations of this study, participants consistently struggled with correctly incorporating the large physical size of wind-turbine blades in their reuse concepts. The Alternate Uses Task (AUT) is an established measure of creativity and asks participants to identify uses for much smaller objects like bricks and paper clips. The current work explored whether an AUT can be adapted as an intervention to help overcome the scale challenge in the WRT.

Students in a fourth-year undergraduate engineering design course (N = 28) underwent both of two conditions, a scaled-AUT intervention and a control, typical AUT before the WRT. AUT fluency and flexibility (number and categories of ideas) were significantly lower in the scaled AUT than the typical AUT. This result supports that object scale more than unfamiliarity is the main WRT challenge, since the AUT objects were relatively common. Notably, correctly scaled WRT concepts significantly increased after the scaled AUT, supporting the intervention’s effectiveness. Finally, the WRT is proposed as a standard design-study task whose solutions help address a real-world problem.
Influence of Different Representation of Requirements on Idea Generation: An Experimental Study
Authors: Patel Akash , Summers Joshua D. , Karmakar Sourabh
Published: November 2021

The objective of this research is to understand how different representations of requirements influence idea generation in terms of quantity, addressment, sketch detail, novelty, and variety of conceptual sketches. Requirements are statements of need, desires, and wishes of the stakeholders that are used by engineers to frame the problem. Essentially, requirements are the raison d’etre for any engineering project. As the requirements document provides constraints and criteria for a design, it defines and determines the success of a project. While there is research studying the effect of requirements on the conceptual sketch, little study has focused one the impact of different requirement representations on solution development. An experimental study was conducted with 52 fourth year mechanical engineering undergraduate students. Two design problems were formulated with three different representations: a problem statement with embedded requirements, a problem statement and a traditional requirement list, and a problem statement with contextualized scrum stories. Each student was provided each design problems with two different representations of requirements. It was found that the use of contextualized scrum story representations significantly affected the conceptual sketch in the novelty of solution fragments and addressment of requirements, while no significant change in variety, sketch detail, and quantity was seen. Also, the contextualized representation positively affected all metrics but the sketch quantity. Finally, it was found that quantity is not directly related to the number of requirements addressed in the sketches.
Engineering Document Summarization Using Sentence Representations Generated by Bidirectional Language Model
Authors: Qiu Yunjian , Jin Yan
Published: November 2021

In this study, the extractive summarization using sentence embeddings generated by the finetuned BERT (Bidirectional Encoder Representations from Transformers) models and the K-Means clustering method has been investigated. To show how the BERT model can capture the knowledge in specific domains like engineering design and what it can produce after being finetuned based on domain-specific datasets, several BERT models are trained, and the sentence embeddings extracted from the finetuned models are used to generate summaries of a set of papers. Different evaluation methods are then applied to measure the quality of summarization results. Both the automatic evaluation method like Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and the statistical evaluation method are used for the comparison study. The results indicate that the BERT model finetuned with a larger dataset can generate summaries with more domain terminologies than the pretrained BERT model. Moreover, the summaries generated by BERT models have more contents overlapping with original documents than those obtained through other popular non-BERT-based models. It can be concluded that the contextualized representations generated by BERT-based models can capture information in text and have better performance in applications like text summarizations after being trained by domain-specific datasets.
Exploring How Lean Product and Process Development Can Promote Industrial Sustainability
Authors: Cooper Daniel R. , Appell Katrina
Published: November 2021

Approximately one-third of anthropogenic greenhouse gas emissions from energy and processing are from making products and chemicals. These emissions can be lowered if they are deliberately considered during the design of products. How can lean product and process development (LPPD) be used to build environmental considerations into the product development system? We explore if LPPD is an effective system in delivering sustainable manufacturing. We conducted case studies in the automotive and oil & gas industries in which LPPD was used to deliver significant physical changes to the product and product manufacturing. We completed semi-structured interviews with stakeholders and focused on the motivation for the change, how LPPD was either a help or hindrance, and the delivered cost savings and environmental benefits. Findings from the case studies were used to structure a workshop on LPPD and sustainability held at a leading manufacturing (industry focused) conference. In this workshop, we focused on first identifying the main environmental impacts in each industry, the physical opportunities to reduce those impacts, and how LPPD might help or hinder delivering that change. This article structures the findings from the case studies and workshop. We present a guide (with examples) for how various LPPD methods (e.g., concept papers, value stream mapping, and design guides) might be used to meet sustainability challenges (e.g., reducing the generation of manufacturing scrap). While LPPD is itself agnostic to sustainability, we suggest that it is an effective method of creating an organizational system for promoting sustainable manufacturing particularly in complex environments.
Designing a Stand for Machine Tool Energy Efficiency, With Pump Embodied Energy Tradeoff Case Study
Authors: Garretson Ian C. , Guo Qiuhao , Linke Barbara S.
Published: November 2021

Energy consumption of machine tools needs to be investigated and decomposed to determine areas for improvement. Motors are one key area of energy consumption, and the analysis of motors, including pump motors is useful for long term energy saving. The following paper describes the static and dynamic analysis of a machine tool stand and an energy analysis to compare manufacturing the stand against using the pump motor. The stand needed to be designed that would both withstand the static load of the machine and minimize vibrational excitation caused during grinding experiments. A review of life cycle inventories for induction motors was then undertaken to evaluate the manufacturing energy consumption of pump motors. A breakeven time was determined for the energy investment of replacing a coolant pump with a stand to conserve energy via gravity-fed coolant recirculation. The simulations were performed as worst-case scenarios and are not necessarily indicative of regular use. Results indicate that the stand as designed will not transmit any significant harmonic vibrations. Results of the energy comparison identify that fractional horsepower coolant pumps operating continuously can be replaced with a stand and the energy to produce the stand will break even within two to three years depending on operating schedule.
Robotic Manipulation System for Multi-Layer Fabric Stitching
Authors: Lahoud Marcel , Marchello Gabriele , Abidi Haider , D’Imperio Mariapaola , Cannella Ferdinando
Published: November 2021

Designing gripping and manipulation systems for soft materials is an interesting but challenging task that has been widely investigated in the robotic field recently. Soft materials require a departure from traditional methodologies proposed for grasping rigid objects. The presented work is step towards manipulating planar soft materials, namely garments. A work-cell to automate the stitching, involving the manipulation of a fabric cloth and a foam pad is described and simulated. The results of the simulation show the feasibility, and speeds comparable to humans. This work is part of SOFTMANBOT project, a cross-sectoral project funded in the EU Horizon 2020 framework.
Efficient Response Amplification of Electrothermally Driven Resonators Using Magnets
Authors: Abuhaimed Ghanimah , Jaber Nizar , Alcheikh Nouha , Younis Mohammad I.
Published: November 2021

Micro/Nano-electromechanical systems, MEMS/NEMS-based resonators are presently an important part of a wide range of applications. However, many of these devices suffer from the low signal-to-noise ratio and the need for a large driving force. Different principles were proposed to enhance the sensitivity and improve their signal-to-noise ratios (SNR), such as bifurcations, jumps and higher-order excitation. However, these methods require special designs and high actuation voltages, which are not always available in the standard function generators and power supplies. Also, it increases the devices’ overall cost and power requirements. Furthermore, parametric excitation is explored as an option to amplify the signal at a lower cost and energy demand. However, this type of excitation requires specific geometrical settings, in addition to very low damping conditions. Electrothermal actuation is investigated to achieve excitation of primary resonance, which can be used for parametric excitation. This type of excitation is desirable due to its simplicity, robustness and ability to create large internal forces at low voltages. However, the time response is limited by the thermal relaxation time. In this work, we demonstrate the use of electromagnetic actuation to significantly amplify the response of electrothermally actuated clamped-clamped resonators at first mode (primary) resonance. At ambient pressure, experimental data show 18 times amplification of the response amplitude compared with electrothermal actuation only. The method is based on introducing a permanent magnetic field to induce an out-of-plane Lorentz-force. The results show the great potential of this technique being used for a variety of sensing and signal processing applications, especially, where a large signal-to-noise ratio is required while using low operational voltages.
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

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

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

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

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

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.