bluuebunny/crossref_metadata_2025 huggingface dataset:
Reaction-Diffusion Modeling of Photopolymerization During Femtosecond Projection Two-Photon Lithography
Authors: Pingali Rushil , Saha Sourabh K.
Published: August 2021

Two-photon lithography (TPL) is a polymerization-based direct laser writing process that is capable of fabricating arbitrarily complex three-dimensional (3D) structures with submicron features. Traditional TPL techniques have limited scalability due to the slow point-by-point serial writing scheme. The femtosecond projection TPL (FP-TPL) technique increases printing rate by a thousand times by enabling layer-by-layer parallelization. However, parallelization alters the time and the length scales of the underlying polymerization process. It is therefore challenging to apply the models of serial TPL to accurately predict process outcome during FP-TPL. To solve this problem, we have generated a finite element model of the polymerization process on the time and length scales relevant to FP-TPL. The model is based on the reaction-diffusion mechanism that underlies polymerization. We have applied this model to predict the geometry of nanowires printed under a variety of conditions and compared these predictions against empirical data. Our model accurately predicts the nanowire widths. However, accuracy of aspect ratio prediction is hindered by uncertain values of the chemical properties of the photopolymer. Nevertheless, our results demonstrate that the reaction-diffusion model can accurately capture the effect of controllable parameters on FP-TPL process outcome and can therefore be used for process control and optimization.
A Dynamic Programming Approach to Solve the Facility Layout Problem for Reconfigurable Manufacturing
Authors: Salimpour Saeideh , Azab Ahmed
Published: August 2021

Preparing manufacturing systems to deal with disruptions caused by unexpected factors such as COVID-19 is critical to remain in today’s competitive market. Reconfigurable manufacturing systems (RMS) which are characterized by being rapid and cost-effective in response to market changes, are a good alternative to cope with such unexpected events. From the layout point of view, in an RMS, the layout of facilities needs to be changeable and able to be redesigned easily. Dynamic facility layout problem (DFLP) is a good approach to develop layouts that are capable to be changed and redesigned. Dynamic programming (DP) has been known as one of the effective methods to deal with DFLP. To optimize DFLP by DP, the set of possible layouts for every single period which is called the state-space is given to DP and the best multi-period layout is found. Since the number of possible layouts increases rapidly with the increase in the number of facilities, considering all these layouts encounters two major difficulties, memory requirements and computer time requirements. This paper proposes a method that has two main phases. In the first phase, the set of layouts to be considered in each period are determined using a heuristic approach. These layouts are the states in the DP approach where the periods constituted the decomposition stages. The recursive formulation of DP is solved in the second phase using a hybridized metaheuristic approach. The proposed approach restricts the DP to a good subset of the state-space. A genetic algorithm is applied to search for the best subset of layouts where each chromosome represents one subset of layouts. This subset is given to DP to be solved and the result is considered as the fitness of the chromosome. By the evolution of the chromosomes, the best subset of layouts that leads to the best multi-period layout plan is found. The proposed approach is evaluated against DP benchmarks in the literature. Computational results show that the proposed approach is able to provide more efficient solutions, especially for large-sized problems.
Influence of Machine Type and Powder Batch During Laser-Based Powder Bed Fusion (L-PBF) of AISI 316L
Authors: Greco Sebastian , Gutzeit Kevin , Hotz Hendrik , Schmidt Marc , Zimmermann Marco , Kirsch Benjamin , Aurich Jan C.
Published: August 2021

The use of additive manufacturing (AM) in industrial applications is steadily increasing due to its near net shape production and high design-freedom. For metallic components, laser-based powder bed fusion (L-PBF) is currently one of the most widely used AM processes. During L-PBF, a component is manufactured layer by layer from a powdery raw material. The process is controlled by a multitude of parameters like the laser power, scanning speed and layer thickness, whose combination significantly influences the properties of the components.

In this study, the influence of the L-PBF machine type and the influence of the powder batch are investigated by means of relative density, microhardness and microstructure of the components. For this purpose, three setups are defined, differing in the powder batch and machine type used. By comparing the process results of the additive manufacturing of different setups, the influence of the machine type and powder batch are determined. The considered material is stainless steel AISI 316L. The results revealed significant differences between all investigated properties of the additively manufactured components. Consequently, process parameter combinations cannot be transferred between different machine types and powder batches without verification of the component properties and, if necessary, special adaption of the process.
Assessing the Mechanical Properties of 3D Printed Bio-Inspired Structures and Integrating Them Into a Product
Authors: Perelman Binjamin , Sharma Vishal S.
Published: August 2021

The Honeycomb structure is one of the most common natural structures used in sandwich panel cores. The Enamel structure’s mechanical properties were compared to the Honeycomb structure’s mechanical properties to investigate if the Enamel structure can improve the compressive strength, stiffness and energy absorption capabilities of sandwich panel cores and potentially replace the common Honeycomb structure. Also, the optimal cellular configurations for the Honeycomb and Enamel structures were explored. Indeed, it was found the Enamel structure can potentially replace the Honeycomb structure and a wall thickness of 1.2 mm and a wall length/cell radius of 8.14 mm will maximize the natural structures mechanical properties.

Furthermore, it was found that both the natural structures have good compressive strength. Therefore, the natural structures with their optimal cellular configurations were integrated into a novel automobile floor mat to ensure the mat possesses good compressive strength to resist failure or permanent deformation. Moreover, the novel automobile floor mat has a design feature that offers an efficient debris capturing and removal system that adds value to the automobile floor mat.
Influence of Clamping for Out-of-Plane Welding Distortion Mitigation During Thin Steel Plates Welding
Authors: Wang Jiangchao , Yi Bin , Zhou Xiaoli
Published: August 2021

Thin steel plates is usually employed for lightweight application, while welding distortion in particular welding induced buckling, will significantly influence dimensional accuracy of thin-walled structure. As elementary investigation of out-of-plane welding distortion mitigation, butt and fillet welded joints with thin plates were experimentally conducted with considering clamping influence. Effective thermal elastic plastic FE computation was proposed to examine mechanical response caused by welding, and large deformation theory was applied to represent welding induced buckling. Good agreement between measured data and computed results can be observed. Furthermore, FE computation without clamping was also carried out to clarify the difference of plastic strains and residual stress comparing with experimental case. Influence of clamping on out-of-plane welding distortion mitigation was eventually proposed for mechanism understanding.
Joining of Metal-Plastic Composites With Advanced Welding Processes
Authors: Faes Koen , Vermeersch Jens , Silva Rafael Gomes Nunes
Published: August 2021

Due to the growing environmental concern and the increasing demand for more ecological methods of transportation, composite materials that combine low weight and high resistance have raised the interest of several industrial sectors. Among these materials, the metal-plastic composites (MPCs) can be highlighted, who combine a low weight and a relatively high mechanical resistance. Such materials are used in advertising signs, building panels, motor shielding, etc. Mechanical joining of MPC materials is to be avoided when protrusions are not allowed or because of aesthetical reasons. Welding is therefore considered as an interesting alternative. However, qualitative joining using welding of the MPC materials to other MPC materials or to metallic plates is a real challenge. Due to their special features (layer structure, material mix, etc.), conventional manufacturing processes are therefore only of limited or no use at all. In particular, the polymer core layer is a barrier for the use of conventional joining methods. This contribution presents a novel joining approach for MPCs. The basic approach is the local melting of the polymer layer by ultrasonic waves and displacement of the molten plastic material by pressure on the cover sheets. This work proposes the investigation of the use of non-conventional solid-state welding processes, such as ultrasonic welding (USW) and refill friction stir spot welding (RFSSW), for joining MPCs with aluminum sheets. Prior to the application of the joining processes, the intermediate plastic core of the MPC materials is displaced using ultrasonic vibrations, so that the materials can be joined as monolithical materials. These joining concepts are validated experimentally. The obtained weld quality is assessed based on destructive and non-destructive testing methods.
Multistream Sensor Fusion-Based Prognostics Model for Systems Under Multiple Operational Conditions
Authors: Li Xin , Fang Xiaolei
Published: August 2021

The residual useful lifetime prediction of complex engineering systems operating under dynamic multi-operational conditions is crucial yet challenging. One of the key challenges is that sensing signals consist of both degradation information that resulted from the physical deterioration of the system and amplitude jumps due to the switch of operational conditions. To accurately predict the failure times, we need to separate the degradation information from sensing signals and exclude jumps. Another challenge stems from the fact that complex engineering systems are typically monitored by multiple sensors that generate multi-stream sensing signals but not all the signals are informative for failure time prediction. Therefore, a systematical sensor selection method needs to be developed to identify informative sensors. The third challenge is that signals from the informative sensors are high-dimensional and may provide redundant information. As a result, a data fusion method is needed to fuse the multi-stream signals from the informative sensors. To address the aforementioned challenges, this paper focuses on developing a prognostic framework for systems that operate under dynamic multiple operational conditions. The framework will first extract the degradation signal from each sensor by removing the jump information resulted from the change of operational conditions. Next, informative sensors are selected using a sensor selection method. Finally, the degradation signals from the selected sensors are fused to predict the failure time of a partially degraded system. The effectiveness of the proposed prognostic framework is validated using a degradation data set of aircraft turbofan engines from NASA repository.
Additive Manufacturing of Porous Ceramics With Foaming Agent
Authors: Guo Zipeng , An Lu , Lakshmanan Sushil , Armstrong Jason N. , Ren Shenqiang , Zhou Chi
Published: August 2021

The macro-porous ceramics has promising durability and thermal insulation performances. A cost-effective and scalable additive manufacturing technique for the fabrication of macro-porous ceramics, with a facile approach to control the printed porosity is reported in the paper. Several ceramic inks were prepared, the foaming agent was used to generate gaseous bubbles in the ink, followed by the direct ink writing and the ambient-pressure and room-temperature drying to create the three-dimensional geometries. The experimental studies were performed to optimize the printing quality. A set of studies revealed the optimal printing process parameters for printing the foamed ceramic ink with a high spatial resolution and fine surface quality. Varying the concentration of the foaming agent enabled the controllability of the structural porosity. The maximum porosity can reach 85%, with a crack-free internal porous structure. The tensile tests showed that the printed macro-porous ceramics have enhanced durability with the addition of fiber. With a high-fidelity 3D printing process and precise control of the porosity, the printed samples exhibited a low thermal conductivity and high mechanical strength.
High-Speed EDM Milling Using Rotating Short Arcs Under Composite Field
Authors: Zhang Jin , Han Fuzhu
Published: August 2021

This paper proposed a new method of high-speed electrical discharge machining (EDM) using rotating short arcs under composite field. By the Lorentz force, the electric force and the high-speed rotation of the tool electrode, rotating short arcs are generated between the tool electrode and the workpiece, which can greatly improve the material removal rate of difficult-to-cut materials such as titanium alloys and superalloys. Firstly, the machining equipment used to generate rotating short arcs was constructed. Secondly, single arc discharge experiment was carried out to investigate the motion characteristics of rotating short arcs. The result shows that the arcs can rotate between the tool electrode and workpiece under composite field. Then, the experiment of processing GH4169 was conducted to explore the machining characteristics of rotating short arcs milling, which indicated that rotating short arcs can achieve a much higher material removal rate (MRR). Additionally, it’s found that the magnetic field also has influence on debris, which is beneficial to debris removal. Finally, a comparative experiment was carried out. The MRR of rotating short arcs milling was three times than that of traditional EDM, and the tool electrode wear rate (TEWR) is only one-fifth of that of traditional EDM. The comparative experiment further verified that rotating short arcs milling can achieve higher MRR and lower TEWR.
Evolution of White Layer and Residual Stress in Electrical Discharge Machining
Authors: Wang Guisen , Han Fuzhu , Zhu Liang
Published: August 2021

White layer and residual stress are the main reasons for the decrease in fatigue life of electrical discharge machined samples. Therefore, it is important to research the evolution of the white layer and residual stress in electrical discharge machining and explain the influence mechanism of machining parameters on them. In this study, the surface topography, white layer thickness, and residual stress of electrical discharge machined samples under different processing parameters were evaluated. The results indicated that surface roughness, white layer thickness, and residual stress increased as the discharge current (I) and pulse-on time (ton) increased. However, when the ton was short, the effect of I (≤ 9.8 A) on surface roughness is not very obvious. When the discharge energy is similar, surface roughness is high under high I conditions. When the discharge energy is similar and low, the average thickness of the white layer is thin under the low I. The effect of I on surface residual stress was greater than that of the ton. The I and ton affect the white layer and residual stress by affecting the amount of melting and removal of the materials. These results were demonstrated that the input process of discharge energy has an important influence on residual stress and the white layer. Therefore, under the premise of ensuring the processing requirements, they can be controlled by selecting the appropriate combination of the ton and I to improve the fatigue life of the workpiece.
Tool Condition Monitoring Method Based on Generative Adversarial Network for Data Augmentation
Authors: Wang Yongqing , Niu Mengmeng , Liu Kuo , Wang Honghui , Shen Mingrui , Qin Bo
Published: August 2021

In the process of parts processing, due to the real working conditions and data acquisition equipment, the collected working data of tools are actually limited. Meanwhile, the tool usually works in the normal state, so it is prone to cause the problem of unbalanced data set, which restricts the accuracy of tool condition monitoring. Aiming at this problem, this paper proposes a tool condition monitoring method based on generative adversarial network (GAN) for data augmentation. Specifically, first collect original samples data during processing in different tool conditions, then the collected sample data is input into GAN, and the generator of GAN can generate new samples which has similar distribution with original samples from tool condition signals data, finally the real samples and generated samples are combined to train deep learning network to predict tool conditions. Experimental results show that the proposed method can significantly improve the accuracy of tool condition monitoring. This paper compares and visualizes the impact of the training data set on the classification ability of the deep learning network model. In addition, some traditional methods are used for comparison, and F1 measure is introduced to evaluate the quality of the results. The results show that this method is better than the Adaptive Synthetic Sampling (Adasyn), add-noise, and resampling.
Development of Magnetic-Field Assisted Finishing (MAF) Process for Chromium-Alloyed Low Carbon Steel Sheet Metal
Authors: Song Guangchao , Poudel Bibek , Kwon Patrick , Chung Haseung , Detweiler Zachary , Quan Guangchun
Published: August 2021

Magnetic-Field Assisted Finishing (MAF) is a polishing process that utilizes a slurry mixture made of ferrous and abrasive particles in a liquid medium, known as a brush. The brush attached to a magnetic tool directly interacts with the surface of a workpiece and removes any imperfections and defects in the surface giving a smooth and nice surface finish. In this study, two distinct MAF setups were applied to the surface of chromium alloyed low carbon steel sheets to achieve the surface finish. The preliminary studies were conducted on one setup to understand the polishing behavior of the sheets and the other setup was designed to polish larger areas of the sheets to mimic the practical sheet producing environment. The effect of processing conditions such as types and sizes of abrasives, brush composition, and finishing time to attain the final surface roughness of the sheets was studied. The brush with the weight composition of 4:1:1.5 (iron: 3 μm black ceramic: silicone) was found to be the optimal condition for polishing the sheet metal samples. The optimal conditions obtained were applied to the larger scaled experimental setup. The final surface roughness of 38 nm and 220 nm were achieved in these experimental setups, respectively.
Material Properties of Discarded Textiles for Manufacturing Feedstocks
Authors: Bashir Asad , Clarke-Sather Abigail R. , Poggogiale Tyler M. , Meehan Christopher L.
Published: August 2021

Presently, many textiles are discarded, in a condition that would allow a significant percentage of them to be able to be completely reused or recycled. Recent consumption practices embodied by “fast fashion”, fast purchasing, and fast disposal of out of style clothing has increased the volume of discarded clothing, as the repurposing and/or recycling of discarded textile materials has not increased at a proportional rate. Consequently, discarded clothing may have nearly no wear and tear or extensive use before consumers choose to dispose of these textiles. Increasing the recovery of textiles from municipal solid waste streams involves understanding the material properties that discarded textiles possess. Measuring the material properties available from discarded textiles will allow for understanding whether these textiles can be reused. At the same time as disposal of textiles has increased, geotextile purchase and use has been increasing rapidly. In the current study, tensile strength (break force) and permittivity of discarded clothing samples made of cotton, polyester, and cotton-polyester blends were measured and compared with material properties that are commonly specified for geotextile applications. Average break force values measured for discarded cotton and polyester and average permittivity values measured for 50%/50% cotton-polyester blends and polyester are higher than what is commonly recommended for common geotextile applications. Polyester materials showed promise for drainage and erosion control applications that would be commonly serviced by geotextiles, as polyester samples yielded average break force and permittivity values are above typically recommended geotextile minimum values for these applications.
Rheological Analysis of Low Viscosity Hydrogels for 3D Bio-Printing Processes
Authors: Tuladhar Slesha , Nelson Cartwright , Habib Md Ahasan
Published: August 2021

Following the success of 3D printing with synthetic polymers like ABS, FLA, Nylon, etc., scientists and researchers have been putting efforts into fabricating bio-compatible materials. It has not only broadened the field of bioengineering and manufacturing but also regenerative medicine. Unlike the traditional 3D printing process, additive bio-manufacturing, also known as 3D bio-printing has a lot of challenges like cell survivability and proliferation, and the mechanical properties of the biomaterials which involve printability and the ability to hold its structural integrity. Proper design of experiments with extensive rheological investigation can help identify useful mechanical property ranges which are directly related to the geometric fidelity of 3D bio-printed scaffolds. Therefore, to investigate the printability of a low viscosity Alginate-Carboxymethyl Cellulose (CMC), multiple concentrations of the mixture were tested maintaining a 8% (w/v) solid content. A set of rheological tests such as the Steady Rate Sweep Test, Three Point Thixotropic Test (3ITT), and Amplitude test were performed. The outcome of those tests showed that the rheological properties can be controlled with the percentage of CMC in the mixtures. The fabricated filaments and scaffolds in the 5 combinations of CMC percentages were analyzed for flowability and shape fidelity. The rheological results and the printability and shape fidelity results were analyzed.
Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification
Authors: Wang Hao , Qamsane Yassine , Moyne James , Barton Kira
Published: August 2021

Machine-part interaction classification is a key capability required by Cyber-Physical Systems (CPS), a pivotal enabler of Smart Manufacturing (SM). While previous relevant studies on the subject have primarily focused on time series classification, change point detection is equally important because it provides temporal information on changes in behavior of the machine. In this work, we address point detection and time series classification for machine-part interactions with a deep Convolutional Neural Network (CNN) based framework. The CNN in this framework utilizes a two-stage encoder-classifier structure for efficient feature representation and convenient deployment customization for CPS. Though data-driven, the design and optimization of the framework are Subject Matter Expertise (SME) guided. An SME defined Finite State Machine (FSM) is incorporated into the framework to prohibit intermittent misclassifications. In the case study, we implement the framework to perform machine-part interaction classification on a milling machine, and the performance is evaluated using a testing dataset and deployment simulations. The implementation achieved an average F1-Score of 0.946 across classes on the testing dataset and an average delay of 0.24 seconds on the deployment simulations.
Platform Development of Tick Collection Robot
Authors: Qiu Yesiliang , Dong Janet , Harrison Moretto Caroline “Niki”
Published: August 2021

This paper discusses the design, building, and testing of a novel robot that is used to collect ticks in a variety of rugged terrains in remote areas. The robot provides a new approach for US Army Medical Researchers or other biologists to collect the insects remotely by using a robot equipped with a vision system and GPS. The approach will change the way that insects are being collected with automation instead of human manual work. The robot is designed to operate in different terrains, including soft grass, dirt, muddy soil, and rocky surface. The robot is weighted within 30 lbs., so the army staff or soldiers can easily carry it to the field.

The tick collection robot consists of a robust platform, a driving system, an assembled flag section for high-performance tick selection, and GPS and vision system for autonomous control. There are two modes for tick collection: Continuous mode and stationary mode. In the continuous mode, the robot can run 3 hours continuously, and in the stationary mode, the robot can operate 6 hours at one location to collect ticks. The robot platform is about 29.64 lbs. without stationary components, which makes it easy to carry. The driving system enables the robot to run through various types of the above-mentioned locations and obstacles. The assembly flag component will make it easy to collect and clean the tick attached to its porous surface.
Enhancement of Force Control Performance of Macro-Micro System Based Polishing Robot With Gravity Compensation
Authors: Ogawa Shotaro , Koto Katsuki , Tsukada Takuhiro , Kakinuma Yasuhiro
Published: August 2021

In a fine mold manufacturing process, the polishing process plays an important role in enhancing the surface quality and is performed manually by skilled workers. However, there are many problems such as decrease in skilled workers, health hazards due to scattering of abrasives, and difference in surface quality due to difference in the proficiency. Hence, there is a strong demand for automation of the polishing process at present.

In this research, a robot polishing system that applies macro-micro mechanism is proposed. The functional polishing module of the end effector is developed and attached to the hand of the serial link robot. Tool path and posture are controlled in a serial link robot as a macro mechanism, and polishing force and tool rotation speed are controlled in the developed polishing module as micro mechanism. This mechanism ideally controls position, force, and rotation speed at the same time. An interlocking control system for position and force has already been constructed. In this paper, we constructed gravity compensation and evaluated the force control performance of the constructed system. Through the evaluation, the followability of the estimated reaction force to the command force and the validity of the actual force behavior measured by the force sensor were evaluated.
Material Efficiency and Economics of Hybrid Additive Manufacturing
Authors: Reginald Elvis Peter Francis , Kumaraguru Senthilkumaran
Published: August 2021

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

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

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

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

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

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

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

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

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

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.
Deep learning-based stereophonic acoustic echo suppression without decorrelation
Authors: Cheng Linjuan , Peng Renhua , Li Andong , Zheng Chengshi , Li Xiaodong
Published: August 2021

Traditional stereophonic acoustic echo cancellation algorithms need to estimate acoustic echo paths from stereo loudspeakers to a microphone, which often suffers from the nonuniqueness problem caused by a high correlation between the two far-end signals of these stereo loudspeakers. Many decorrelation methods have already been proposed to mitigate this problem. However, these methods may reduce the audio quality and/or stereophonic spatial perception. This paper proposes to use a convolutional recurrent network (CRN) to suppress the stereophonic echo components by estimating a nonlinear gain, which is then multiplied by the complex spectrum of the microphone signal to obtain the estimated near-end speech without a decorrelation procedure. The CRN includes an encoder-decoder module and two-layer gated recurrent network module, which can take advantage of the feature extraction capability of the convolutional neural networks and temporal modeling capability of recurrent neural networks simultaneously. The magnitude spectra of the two far-end signals are used as input features directly without any decorrelation preprocessing and, thus, both the audio quality and stereophonic spatial perception can be maintained. The experimental results in both the simulated and real acoustic environments show that the proposed algorithm outperforms traditional algorithms such as the normalized least-mean square and Wiener algorithms, especially in situations of low signal-to-echo ratio and high reverberation time RT60.
Three-Day Remote Monitoring of Gait Among Young and Older Adults Using Participants’ Personal Smartphones
Authors: Lugade Vipul , Kuntapun Janeesata , Prupetkaew Paphawee , Boripuntakul Sirinun , Verner Eric , Silsupadol Patima
Published: August 2021

Conventional one-time gait analyses do not evaluate walking across more than a few steps, cannot monitor changes longitudinally, and do not reflect performance in real-life environments. To successfully quantify age-related gait decrement, technology that can continuously monitor gait is vital. This study examined the feasibility and validity for participant smartphones to remotely assess gait. In addition, the authors investigated whether smartphone-derived measures could differentiate between young and older adults (fallers and nonfallers). A total of 63 adults completed clinical and gait assessment in the laboratory and donned their smartphones for 3 days in the real-life environment. A custom-built Android application collected triaxial accelerations with spatiotemporal gait measures computed and compared between groups. Across 11 brands and 10 Android versions, smartphone-derived gait parameters were valid. Furthermore, results indicated age-related differences in walking during the 3-day assessment. However, no disparities were found between older adult groups. Smartphone-based evaluations may improve real-life screening of adults with gait deficits.
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.