bluuebunny/crossref_metadata_2025_split huggingface dataset:
Material Efficiency and Economics of Hybrid Additive Manufacturing
Authors: Reginald Elvis Peter Francis , Kumaraguru Senthilkumaran 
Published: August 2021

Abstract
In the past few years, Hybrid Additive Manufacturing has emerged to take advantage of both Additive Manufacturing and Subtractive Manufacturing processes and also to overcome the limitation of one process with the other. In aerospace applications, material wastage has become an issue in conventional machining process which reflects in total production cost and time. Especially, when dealing with expensive materials, conventional processes lack material efficiency with high buy-to-fly ratio which results in increased material cost. This paper deals with Hybrid Additive Manufacturing involving two different volume partitioning strategies — (i) Feature-based volume partitioning method (ii) Stock-based near net-shaping volume partitioning method to discuss the economics and material efficiency of Hybrid Additive Manufacturing process via simple cost estimator (formulated from the existing literature) by comparing these two volume partitioning strategies through suitable case studies — (i) Turbine blade and (ii) Impeller. From the results, it was found that the feature-based volume partitioning method was found to be material efficient and cost effective than the stock based near net shaping volume partitioning method in both the case studies.
New Perspectives Regarding the Chip Formation Process of Ti-6Al-4V
Authors: Lortz Wolfgang , Pavel Radu 
Published: August 2021

Abstract
The mathematical, physical and morphological characteristics of the chip formation process during cutting of Ti-6Al-4V will be analyzed and presented in this paper. In recent years titanium has received more attention due to their unique material properties, such as light weight by height strength, small deformation at high temperatures, low brittleness at low temperatures, and nearly no oxidation at high temperatures, but with the disadvantage that it is difficult to machine. A lot of investigations have been conducted to solve the complex process of machining. But the real complex phenomena at the cutting edge can’t be explained with the help of simplified models. This paper presents a new mathematical-physical model describing the process mechanics leveraging two kinds of friction to explain the metal behavior to strain and stress with self-hardening or softening effects, and the dynamic chip formation behavior due to strain rate discontinuity. All these influencing parameters have an interdependent relationship; thus, they cannot be analyzed separately. The resultant deformation process leads to a grid deformation pattern in the relevant region of the transversal section of a chip that can be used for comparing the theoretical solution with the experimental result. This deformation pattern is the only characteristic that will not disappear after machining. As long as the theoretical results are found to be in agreement with the experimental data of the produced segmented chip, we can be sure, that the models integrating the friction conditions, strain-stress, and metallurgical conditions are correctly developed. In approaching these problems, it is difficult to choose the relevant machining conditions, because a “quick-stop” test is difficult to produce. The reason might be the existing contact conditions at the tool-chip interface, which has an intensive connection due to the diffusion process. Therefore, two different cutting velocities were chosen with the hope that the diffusion is not too intensive; (one slow velocity with vc = 12.5 m/min and a higher velocity with vc = 100 m/min). In addition, a photomicrograph of a chip was taken for the validation process between theoretical and experimental results. Furthermore, the existing temperatures in the contacting zone as well as in the chip formation area could be developed and are discussed and presented in this paper.
A Digital Twin for Automated Layup of Prepreg Composite Sheets
Authors: Chen Yi-Wei , Joseph Rex Jomy , Kanyuck Alec , Khan Shahwaz , Malhan Rishi K. , Manyar Omey M. , McNulty Zachary , Wang Bohan , Barbič Jernej , Gupta Satyandra K. 
Published: August 2021

Abstract
The composite sheet layup process involves stacking several layers of a viscoelastic prepreg sheet and curing the laminate to manufacture the component. Demands for automating functional tasks in the composite manufacturing processes have dramatically increased in the past decade. A simulation system representing a digital twin of the composite sheet can aid in the development of such an autonomous system for prepreg sheet layup. While Finite Element Analysis (FEA) is a popular approach for simulating flexible materials, material properties need to be encoded to produce high-fidelity mechanical simulations. We present a methodology to predict material parameters of a thin-shell FEA model based on real-world observations of the deformations of the object. We utilize the model to develop a digital twin of a composite sheet. The method is tested on viscoelastic composite prepreg sheets and fabric materials such as cotton cloth, felt and canvas. We discuss the implementation and development of a high-speed FEA simulator based on the VegaFEM library [29]. By using our method to identify sheet material parameters, the sheet simulation system is able to predict sheet behavior within 5 cm of average error and have proven its capability for 10 fps real-time sheet simulation.
Creation of Functionally Graded Glass Channels by Electrochemical Discharge Machining Process: A Feasibility Study
Authors: Grisell Andrea , Sundaram Murali 
Published: August 2021

Abstract
Functionally graded surfaces — surfaces with properties that are engineered to have spatial variations — have numerous applications such as micropumps, auto-mixers, and flow control for lab-on-chip devices. Manufacturing of functionally graded surfaces is an increasingly important topic of research. This study investigates the feasibility of creating a functionally graded surface during channeling of borosilicate glass by the electrochemical discharge machining (ECDM) process. The ability to create surface roughness gradients in microchannels during the machining process was demonstrated by modifying the input voltage, tool feed rate, and tool rotation speed. Microchannels with graded surface roughness having Ra values ranging from 0.35 to 4.07 μm were successfully machined on borosilicate glass by ECDM. Surface profiles were obtained via a stylus profilometer, and roughness values were calculated after detrending and applying a Gaussian filter. To demonstrate the process versatility, micro channels with increasing and decreasing Ra values were machined, one increasing from 1.43 μm to 4.07 μm, another decreasing from 3.29 μm to 1.10 μm. To demonstrate the process repeatability, a micro channel with similar surface roughness on both ends and a lower Ra value in the center was created. In this channel, the Ra value at the start is 0.35 μm, reducing to 0.24 μm, then rising again to 0.38 μm in the final section.
Adhesive Deposition Process Characterization for Microstructure Assembly
Authors: Sherehiy Andriy , Montenegro Andres , Wei Danming , Popa Dan O. 
Published: August 2021

Abstract
Recent advancements in additive manufacturing such as Direct Write Inkjet printing introduced novel tools that allow controlled and precise deposition of fluid in nano-liter volumes, enabling fabrication of multiscale structures with submillimeter dimensions. Applications include fabrication of flexible electronics, sensors, and assembly of Micro-Electro-Mechanical Systems (MEMS). Critical challenges remain in the control of fluid deposition parameters during Inkjet printing to meet specific dimensional footprints at the microscale necessary for the assembly process of microscale structures. In this paper we characterize an adhesive deposition printing process with a piezo-electric dispenser of nano-liter volumes. Applications include the controlled delivery of high viscosity Ultraviolet (UV) and thermal curable adhesives for the assembly of the MEMS structures. We applied the Taguchi Design of Experiment (DOE) method to determine an optimal set of process parameters required to minimize the size of adhesive printed features on a silicon substrate with good reliability and repeatability of the deposition process. Experimental results demonstrate repeatable deposition of UV adhesive features with 150 μm diameter on the silicon substrate. Based on the observed wettability effect of adhesive printed onto different substrates we propose a solution for further reduction of the deposit-substrate contact area for microassembly optimization.
Organic Piezoresistive Robotic Skin Sensor Fabrication, Integration and Characterization
Authors: Olowo Olalekan O. , Zhang Ruoshi , Yang Zhong , Goulet Brian , Popa Dan O. 
Published: August 2021

Abstract
Advanced applications for human-robot interaction require perception of physical touch in a manner that imitates the human tactile perception. Feedback generated from tactile sensor arrays can be used to control the interaction of a robot with their environment and other humans. In this paper, we present our efforts to fabricate piezoresistive organic polymer sensor arrays using PEDOT: PSS or poly (3,4-ethylenedioxythiophene)-poly(styrenesulfonate). Sensors are realized as strain-gauges on Kapton substrates with thermal and electrical response characteristics to human touch. In this paper, we detail fabrication processes associated with a Gold etching technique combined with a wet lift-off photolithographic process to implement a circular tree designed sensor microstructure in our cleanroom. The testing of this microstructure is done on a load testing apparatus facilitated by an integrated circuit design. Furthermore, a lamination process is employed to compensate for temperature drift while measuring pressure for double-sided sensor substrates. Experiments carried out to evaluate the performance of the fabricated structure, indicates 100% sensor yields with the updated technique implemented.
Robotic Grasping Training Using Deep Reinforcement Learning With Policy Guidance Mechanism
Authors: Yao Junying , Liu Yongkui , Lin Tingyu , Ping Xubin , Xu He , Wang Wenxiao , Xiao Yingying , Zhang Lin , Wang Lihui 
Published: August 2021

Abstract
For the past few years, training robots to enable them to learn various manipulative skills using deep reinforcement learning (DRL) has arisen wide attention. However, large search space, low sample quality, and difficulties in network convergence pose great challenges to robot training. This paper deals with assembly-oriented robot grasping training and proposes a DRL algorithm with a new mechanism, namely, policy guidance mechanism (PGM). PGM can effectively transform useless or low-quality samples to useful or high-quality ones. Based on the improved Deep Q Network algorithm, an end-to-end policy model that takes images as input and outputs actions is established. Through continuous interactions with the environment, robots are able to learn how to optimally grasp objects according to the location of maximum Q value. A number of experiments for different scenarios using simulations and physical robots are conducted. Results indicate that the proposed DRL algorithm with PGM is effective in increasing the success rate of robot grasping, and moreover, is robust to changes of environment and objects.
Multiple Sound Sensors And Fusion In Modern CNN-Based Machine State Prediction
Authors: Kim Eunseob , Yun Huitaek , Jun Martin Byung-Guk , Kim Kyunghyun , Cha Suk Won 
Published: August 2021

Abstract
In the new era of manufacturing with Industry 4.0, Smart Manufacturing (SM) is growing in popularity as a potential for the factory of the future. A critical component of SM is effective machine monitoring. Legacy machines indirect monitoring using Internet of Things (IoT) sensors are preferred instead of modifying hardware directly. Machine tools are composed of rotary components, resulting in machine tools emitting acoustic and vibratory signals. However, sound data cannot easily function as a direct representation for machine status due to its noise, variable time course, and irregular sampling. In this paper, we attempt to bridge this gap through machine learning techniques and auditory monitoring of auxiliary components (i.e., coolant, chip conveyor, and mist collector) as well as the main spindle running state of machine tools. Multi-label classification and Convolutional Neural Network (CNN) were utilized to train models for monitoring machine tools from the sound features. An external microphone and three internal sound sensors were attached to both mill and lathe machines. As a sound feature, Mel-frequency cepstrum (MFCC) features were extracted. The classification task performance was compared between each sensor location and early sensor fusion. The results showed that the sensor fusion approach resulted in the highest F1 score on both machine system.
Fabrication of Multilayered Polymer Composite Fibers for Enhanced Functionalities
Authors: Xu Weiheng , Ravichandran Dharneedar , Jambhulkar Sayli , Zhu Yuxiang , Song Kenan 
Published: August 2021

Abstract
Carbon nanoparticles-based polymer composites have wide applications across different fields for their unique functional properties, durability, and chemical stability. When combining nanoparticle morphologies with micro- or macro-scale morphologies, the hierarchal structure often would greatly enhance the composites’ functionalities. Here in this work, a thermoplastic polyurethane (TPU) and graphene nanoplatelets (GnPs) based multilayered fiber is fabricated through the combination of dry-jet-wet spinning, based on an in-house designed spinneret which accommodates three layers spinning solution, and hot isostatic pressing (HIP), at 220 °C. The multilayered spinneret enables the spinnability of a high GnPs loaded spinning dope, highly elastic, with great mechanical strength, elongation, and flexibility. The HIP process resulted in superior electrical properties as well as a newly emerged fourth hollow layer. Together, such a scalable fabrication method promotes a piezoresistive sensor that is sensitive to uniaxial strain and radial air pressure. The hollow fiber is characterized based on surface morphologies, layer formation, percolation threshold, piezoresistive gauge factor, mechanical stability and reversibility, and air-pressure sensitivity and reversibility. Such facile fabrication methods and unique structures have combined the mechanically robust outer shell with a highly conductive middle sensing layer for a new sensor with great potentials in wearable, robotics, biomedical, and other areas.
Analysis of Vibration Signals in Monitoring Titanium End Milling Process Using Triaxial Accelerometer
Authors: Navarro-Devia John Henry , Dao Dzung Viet , Chen Yun , Li Huaizhong 
Published: August 2021

Abstract
Vibrations during milling of hard-to-cut materials can cause low productivity, inferior quality and short tool life. It is one of the common issues in the machining of hard-to-cut materials employed in aerospace applications, such as titanium alloys. This paper presents an analysis of the vibration signals in the 3 axes of movement during titanium end milling, under diverse cutting parameters, manipulating spindle speed and feed rate. Signals were obtained using a triaxial accelerometer and processed in MATLAB. The analysis was conducted in the frequency-domain and the time-frequency domain. The results show that high-frequency vibration could occur in any direction with different amplitudes. Response on each axis depends on spindle speed, feed, and type of milling. A frequency component continually appeared in each axis regardless of cutting conditions and is located near the natural frequencies. Finally, the triaxial accelerations were compared for the milling cases with a new and a worn tool. Results highlight the importance and need for continuous monitoring of vibration in the 3 axes, instead of only using a single-channel signal, providing experimental data which could expand knowledge relating to the milling of titanium alloys.
Psychological Effects of Whole-Body Vibration Training in Frail Older Adults: An Open, Randomized Control Trial
Authors: Wadsworth Daniel , Turnbull Janet , Lark Sally 
Published: August 2021

The aim of this study was to identify the psychological effects of whole-body vibration (WBV) exercise in frail older adults. About 117 male and female volunteers (82.5 ± 7.9 years) from residential care facilities were randomized and assigned to control, simulated exercise (SIM), or WBV exercise (WBV) groups. All received regular care, while exercise groups also underwent 16 weeks of training (3 × 20 min/week). WBV exercise began with 5 × 1 min bouts (6 Hz/2 mm, 1:1 min exercise:rest), self-progressing to 10 × 1 min (up to 26 Hz/4 mm), and maintaining knee flexion. SIM training mimicked exercise stance and duration. Pre- and post-measures of falls–confidence, quality of life, and functional independence were completed using validated questionnaires. Functional independence and falls–confidence scores increased by 5.8% and 17.4% respectively with WBV exercise, compared with declines in SIM (p = .074/p = .035, respectively) and control (p = .000/p = .000, respectively) participants. Beneficial effects remained for at least 6-month post-intervention. Further WBV benefits were observed in activity, mobility, and self-care elements of quality of life. Sixteen weeks of low-level WBV exercise is sufficient to enhance frail older adults’ falls-related confidence, quality of life, and functional independence.
Estimated total cardiovascular risk in a rural area of Bangladesh: a household level cross-sectional survey done by local community health workers
Authors: Zaman Mohammad Mostafa , Moniruzzaman Mohammad , Chowdhury Kamrun Nahar , Zareen Salma , Hossain AHM Enayet 
Published: August 2021

ObjectiveThe aim of this study was to estimate 10-year cardiovascular disease (CVD) risk among Bangladeshi rural community residents, using the 2014 WHO/International Society of Hypertension (WHO/ISH) risk prediction charts.Study designCross-sectional population-based study done by local community healthcare workers engaging the lowest level facilities of the primary healthcare system.Setting and participantsA total of 1545 rural adults aged ≥40 years of Debhata upazila of Satkhira district of Bangladesh participated in this survey done in 2015. The community health workers collected data on age, smoking, blood pressure, blood glucose and treatment history of diabetes and hypertension.Primary outcome measuresWe estimated total 10-year CVD risk using the WHO/ISH South East Asia Region-D charts without cholesterol and categorised the risk into low (<10%), moderate (10%–19.9%), high (20%–29.9%) and very high (≥30%).ResultsThe participants’ mean age (±SD) was 53.9±11.6 years. Overall, the 10-year CVD risks (%, 95% CI) were as follows: low risk (81.6%, 95% CI 78.4% to 84.6%), moderate risk (9.9%, 95% CI 7.4% to 12.1%), high risk (5.8%, 95% CI 4.4% to 7.2%) and very high risk (2.8%, 95% CI 1.5% to 4.1%). In women, moderate to very high risks were higher (moderate 12.1%, high 6.1% and very high 3.7%) compared with men (moderate 7.5%, high 5.5% and very high 1.9%) but none of these were statistically significant. The age-standardised prevalence of very high risk increased from 2.9% (0.7%–5.2%) to 8.5% (5%–12%) when those with anti-hypertensive medication having controlled blood pressure (<140/90 mm Hg) added.ConclusionThe very high-risk estimates could be used for planning resource for CVD prevention programme at upazila level. There is a need for a national level study, covering diversities of rural areas, to contribute to national planning of CVD prevention.
Development of entrustable professional activities for residents rotating nephrology department in a Japanese university hospital: a Delphi study
Authors: Tanaka Akihito , Kondo Takeshi , Urushibara-Miyachi Yuka , Maruyama Shoichi , Nishigori Hiroshi 
Published: August 2021

ObjectivesTraining strategies regarding entrustable professional activities (EPAs) vary from country to country; one such strategy is for residents. However, there are no reports of EPAs developed for residents who rotate to the nephrology departments. We aimed to construct such EPAs, which could be generalised to other institutions.DesignPurposive design and a modified Delphi method to build consensus.SettingThe department of nephrology in a university hospital in Aichi Prefecture, Japan.ParticipantsBased on the attainment goals used in our department, an initial list was developed within the research group. The expert panel included 25 nephrologists from our affiliate hospital. Responses were based on a 5-point method and agreement was reached if both (A) and (B) were met: (A) mean≥4 with a SD <1; (B) more than 75% of respondents rated the item 4 or more. With agreement, the item was left for the next round. This round was repeated.ResultsAn initial list of 11 items was developed; after three Delphi rounds and revisions, eight items remained that were then established as the final EPAs. These items can serve as a list of goals to be reached by residents who rotate to the department of nephrology. The results indicated that most of the experts believed residents should be able to perform tasks deemed necessary or urgent for all physicians, such as those that deal with hyperkalaemia and heart failure.ConclusionsThe concept of EPAs enabled us to develop goals and evaluation criteria for residents’ training in nephrology. This study can serve as a springboard for future discussions and contribute to the development of resident education in nephrology.