bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of Data-Driven Models in Human-Robot Load-Sharing
Authors: Nguyen Vinh , Marvel Jeremy 
Published: September 2022

Abstract
Human-robot load-sharing is a potential application for human-robot collaborative systems in production environments. However, knowledge of the appropriate data-driven models for this application type is limited due to a lack of physical real-world data and validation metrics. This paper describes and demonstrates a load-sharing testbed for evaluating data-driven models in a human-robot load-sharing application. Specifically, the testbed consists of a single operator and single robot relocating a payload to a desired destination. In this work, the operator initially communicates to the robot using audio feedback to initiate and alter robotic motion commands. During the payload relocation, human, payload, and robot state data are recorded. The measurements are then used to train three data-driven models (neural network, naïve Bayes, and random forest). The data-driven models are then used to transmit movement commands to the robot during human-robot load-sharing without the use of audio feedback, thus improving robustness and eliminating audio signal processing time. Evaluation of the three data-driven models shows that the random forest model was demonstrated to be the most accurate model followed by naïve Bayes and then the neural network. Hence, the results of this study provide novel insight into the types of data-driven models that can be used in load-sharing applications in addition to development of a real-world testbed.
The Effect of Composition, Geometry and a Novel Tool Design on Metal Recovery During Aluminum Process Scrap Remelting
Authors: Liao Jiankan , Sharma Ashvin , Cooper Daniel 
Published: September 2022

Abstract
Aluminum recycling requires less energy and releases fewer greenhouse emissions than primary production from naturally occurring ores; however, a significant fraction of the furnace charge is lost to dross generation during remelting. In this article, we use an electric furnace to remelt clean aluminum sheet and machining chip process scrap of varying thickness, surface roughness, and composition. The metal recovery results show that magnesium-containing alloys (e.g., 2xxx, 5xxx, 6xxx, and 7xxx alloys) accelerate dross generation and lower metal recovery. This is likely due to magnesium having a higher reactivity than aluminum, with the magnesium content detected in the dross (using Energy-dispersive X-ray spectroscopy) greater than the magnesium content in the alloy. Metal recovery decreased when remelting thinner scrap. Metal recovery for clean machining chips was lower than for aluminum sheet scrap of the same thickness and composition. This disparity was likely due to the greater surface roughness of the machining chips, which will increase the surface area for oxidation and potentially the wetting of the oxide by the Wenzel effect. The decreased metal recovery for scratch brushed aluminum sheets confirmed the effect of surface roughness. Subsequently, a “squeeze” cutting tool was designed and manufactured, which smooths the otherwise rough back-side of the machining chips. These smoother machining chips exhibited increased metal recovery during remelting.
Physics-Informed Artificial Intelligence for Temperature Prediction in Metal Additive Manufacturing: A Comparative Study
Authors: Ghungrad Suyog , Gould Benjamin , Wolff Sarah , Haghighi Azadeh 
Published: September 2022

Abstract
Prediction of the temperature history of printed paths in additive manufacturing is crucial towards establishing the process-structure-property relationship. Traditional approaches for predictions such as physics-based simulations are computationally costly and time-consuming, whereas data driven approaches are highly dependent on huge, labeled datasets. Moreover, these labeled datasets are mostly scarce and costly in additive manufacturing owing to its unique application domain (mass customization) and complicated data-gathering stage. Recently, model-based or physics-informed artificial intelligence approaches have shown promising potential in overcoming the existing limitations and challenges faced by purely analytical or data driven approaches. In this work, a novel physics-informed artificial intelligent structure for scenarios with limited data is presented and its performance for temperature prediction in the selective laser melting additive manufacturing process is compared with one of the state-of-the-art data driven approaches, namely long short-term memory (LSTM) neural networks. Temperature data for training and testing was extracted from infrared images of single-track layer-based experiments for Ti64 material with different combinations of process parameters. Compared to LSTM, the proposed approach has higher computational efficiency and achieves better accuracy in limited data scenarios, making it a potential candidate for real-time closed-loop control of the additive manufacturing process under limited and sparse data scenarios. In other words, the proposed model is capable to learn more efficiently under such scenarios in comparison to LSTM model.
Bidirectional Bending of Thin Metals With Femtosecond Lasers
Authors: Tessmann Bryce , Li Kewei , Zhao Xin 
Published: September 2022

Abstract
Lasers have a wide range of manufacturing applications, one of which is the bending of metals. While there are multiple ways to induce bending in metals with lasers, this paper examines laser peen forming with femtosecond lasers on thin metals of 75-micrometer thickness perpendicular to the laser. The effects of multiple parameters, including laser energy, scan speed, scan pitch, and material preparation, on the bend angle of the metal are investigated. The bend angles are generated in both concave and convex directions, represented by positive and negative angles, respectively. While it is possible to create angles ranging from 0 to 90 degrees in the concave direction, the largest average convex angle found was only −26.2 degrees. The positive angles were created by high overlapping ratios and slow speeds. Furthermore, the concave angles were made by a smaller range of values than the convex angles, although this range could be expanded by higher laser energy. The positive angles also had a higher inconsistency than the negative angles, with an average standard deviation of 6.8 degrees versus an average of 2.6 degrees, respectively. The characterization of bending angles will allow for more accurate predictions, which will benefit traditional metal forming applications and more advanced applications such as origami structures with metal.
Comparison of Human Skeleton Trackers Paired With a Novel Skeleton Fusion Algorithm
Authors: Flowers Jared T. , Wiens Gloria J. 
Published: September 2022

Abstract
The onset of Industry 4.0 brings a greater demand for Human-Robot Collaboration (HRC) in manufacturing. This has led to a critical need for bridging the sensing and AI with the mechanical-n-physical necessities to successfully augment the robot’s awareness and intelligence. In a HRC work cell, options for sensors to detect human joint locations vary greatly in complexity, usability, and cost. In this paper, the use of depth cameras is explored, since they are a relatively low-cost option that does not require users to wear extra sensing hardware. Herein, the Google Media Pipe (BlazePose) and OpenPose skeleton tracking software packages are used to estimate the pixel coordinates of each human joint in images from depth cameras. The depth at each pixel is then used with the joint pixel coordinates to generate the 3D joint locations of the skeleton. In comparing these skeleton trackers, this paper also presents a novel method of combining the skeleton that the trackers generate from each camera’s data utilizing a quaternion/link-length representation of the skeleton. Results show that the overall mean and standard deviation in position error between the fused skeleton and target locations was lower compared to the skeletons resulting directly from each camera’s data.
Pass-Wise Tool Wear Prediction in Turning Based on Long-Short Term Memory Algorithm Using Current Signals
Authors: Chinomona Benvolence , Chung Chunhui , Wang Po-Chieh 
Published: September 2022

Abstract
A novel tool wear predictive model was developed based on the current signals in this study. The system adapts to different part geometry with accurate prediction of the tool wear during the operation. The current sensor was utilized presenting a practical and better choice for tool wear monitoring which is inexpensive and no need to be attached to the working table or spindle. To avoid interruptions during the machining process, the tool wear was only measured at the end of the operation. The Long Short-Term Memory model was used to develop the tool wear prediction system. The tool wear prediction results indicate 23.92% and 36.41% average error for all the testing samples after 1/3 of the operations for profiling and straight turning, respectively. When the tool wear prediction was carried out after 2/3 of the operations, excellent results are observed with 6.15% error for profiling and 9.44% error for straight turning. The prediction results at the end of the operation shows 0.18% and 0.68% error for profiling and straight turning. The performance of the model using the current sensor shows that the model can predict the tool wear with less than 10% error after 2/3 of the turning operation without interfering with the turning process.
3D Printing of Eco-Friendly Artificial Martian Clay (JMSS-1) for In-Situ Resource Utilization on Mars
Authors: Ghosh Avishek , Favier Jean-Jacques 
Published: September 2022

Abstract
Permanent settlement on the surface of planets like the Moon and Mars is anticipated to be beneficial for long-duration exploration missions. The space agencies have developed several plans, along with other commercial partners, to build operational stations on such planetary bodies, which will be economical and resourceful to execute further missions into deep space. Therefore, the real integration of an advanced manufacturing technique is essentially a matter of further research to design and deliver critical subsystems utilising in-situ resources available on the surface of Mars. The Additive Manufacturing (AM) technique is becoming increasingly promising for developing complex structures by depositing multiple consecutive layers, unlike specific moulds required in the conventional manufacturing process. Therefore, to assess the feasibility of 3D printing with local resources technically, a recently developed artificial Mars soil simulant known as Jining Martian Soil Simulant (JMSS-1) has been processed to formulate clay useful for the extrusion 3D printing process. The developed Martian clay has been fabricated, characterised, and its dielectric properties measured at high frequencies for the first time. A stable aqueous clay has been developed containing less organics (&lt; 10 wt% versus typically 30–40 wt%), which is amenable to resource-efficient 3D printing. A range of solid and porous structures of various shapes and sizes have been fabricated using a custom-developed material extrusion 3D printing system. The 3D printed artificial Martian clay sintered for 2 hours at 1100°C exhibited relative permittivity (εr) = 4.52, dielectric loss (tanδ) = 0.0015, quality factor (Q × f) = 7039 GHz. TCf = −19; and demonstrated similar properties at higher frequencies. This work demonstrates the progress in clay additive manufacturing and illustrates the potential to deliver components with functional properties through a “Powder to Product” holistic approach that can support long-term space exploration by utilising local resources available on Mars.
Layer-Wise Melt Pool Temperature Evolution in Laser Powder Bed Fusion: An Experimental Study Using a Single Camera Based Two-Wavelength Imaging Pyrometry
Authors: Vallabh Chaitanya Krishna Prasad , Hinnebusch Shawn , To Albert C. , Zhao Xiayun 
Published: September 2022

Abstract
In metal additive manufacturing (AM) the layer-wise thermal history is crucial for its effect on the print part properties, such as, microstructure, porosity, and mechanical strength. Literature studies for evaluating the part thermal history are typically based on in-situ infrared thermography and thermal modeling. However, the effect of melt pool temperature on the part thermal history has not been widely studied. In this preliminary work, for the first time we present a large and comprehensive in-situ monitored, layer-wise melt pool temperature evolution data for laser powder bed fusion (LPBF) AM. The melt pool temperature is evaluated using an in-house Single Camera Two-Wavelength Imaging Pyrometry (STWIP) system. The melt pool temperature evolution was evaluated for three different prints with different inter-layer-times and print heights. The melt pool temperature history trends presented in this work are in agreement with literature studies on part-thermal histories. The LPBF process signatures from our STWIP system can help develop more accurate thermal models with the melt pool temperature as the input and the unique capability of the STWIP system to acquire and analyze large amounts data facilitates the development of machine learning models for estimating part properties based on the process signatures.
Acoustic Process Monitoring of Contact Tip to Work Piece Distance in Wire Arc Additive Manufacturing by Random Forest Algorithms
Authors: Hebert Dylan , Thien Austen , Saldana Christopher 
Published: September 2022

Abstract
Wire arc additive manufacturing (WAAM) is a metal additive process that allows for constructing diverse parts using an electric arc and metal wire feed stock. It provides higher deposition rates and larger build areas that make it an attractive technology for industry. An important element of WAAM process control is maintaining specific contact tip to work-piece distance (CTWD). Cumulative errors caused by modeling inaccuracies and thermal conditions can create an increase in CTWD during processing. In the present study, acoustic signals were investigated as a non-invasive form of monitoring this phenomenon and were compared with traditional current-based sensing approaches. Experiments were conducted across a range of controlled CTWD conditions. From the acoustic signals, 35 different statistical features were extracted and machine learning strategies were utilized to correlate them to the CTWD. Random forests composed of 50 trees each were chosen to classify the CTWD into three different levels of CTWD granularity. The ability of random forest algorithms to detect varying levels of CTWD was investigated and the models showed limited performance especially for high granularity CTWD predictions when compared to current sensing approaches. The implications for these results in rapid implementation for high-level process monitoring of process condition are briefly discussed.
Towards a Digital Twin: Simulation and Residual Stress Analysis in Aerospace Composite Structures Assembly
Authors: Lutz Tim , Yue Xiaowei , Camelio Jaime 
Published: September 2022

Abstract
Aerospace composites assemblies/joining demand ultra-high precision due to critical safety requirements, which necessitate adherence to indicators of risk that are often difficult to quantify. This study examines one important indicator, the residual stress that arises as a result of dimensional mismatch between mating components during the composite structures assembly process. Conventional simulations of large components assemblies investigated the process at a local or global scale, but lacked detailed exploitation of multi-layer stress analysis at integrated scale for composite structures. We develop a novel digital twin simulation for joining large composite structures with mechanical fasteners. The digital twin simulation integrates global features and local features for detailed investigation of stresses. We perform a statistical analysis to better understand the numerical properties of residual stresses after the fastening. Goodness-of-Fit tests and normality tests are used to explore the probabilistic distributions of the stresses exceeding a chosen safety threshold. The case study is conducted based on composite fuselage joining. The results show the stresses in composite structures assembly follow extreme value distributions (such as Weibull, Gumbel) rather than the widely used Gaussian distribution. The stresses in joined composite structures differ across layers, which can be attributed to the anisotropic material behavior.
Visualization Error Analysis for Augmented Reality Stereo Video See-Through Head-Mounted Displays in Industry 4.0 Applications
Authors: Yang Wenhao , Zhang Yunbo 
Published: September 2022

Abstract
Under the fourth industrial revolution (Industry 4.0), Augmented Reality (AR) provides new affordances for a variety of applications, such as AR-based human-robot interaction, virtual assembly assistance, and workforce virtual training. The see-through head-mounted displays (STHMDs), based on either optical see-through or video see-through technologies, are the primary AR device to augment the visual perception of the real environment with computer-generated contents through a hand-free headset. Specifically, the video see-through STHMDs process the superimposing of the real environment and virtual contents based on the digital images and output it to users, while optical see-through STHMDs display virtual contents through the optics-based near-eyes display with users’ normal view of the real scene kept. For both types of AR devices, the accuracy of visualization is essential. For example, in AR-based human-robot interaction, the inaccurate rendering of 3D virtual objects with respect to the real environment, will lead to users’ mistaking operations, and therefore, causes an invalid tool path planning result. In spite of many works related to system calibration and error reduction for optical see-through STHMDs, there are few efforts at figuring out the nature and factors of those errors in video see-through STHMDs. In this paper, taking consumer-available AR video see-through STHMDs as an example, we identify error sources of registration and build a mathematical model of the display progress to describe the error propagation in the stereo video see-through systems. Then, based on the mathematical model of the system, the sensitivity of each error source to the final registration error is analyzed. Finally, possible solutions of error correction are suggested and summarized in the general video see-through STHMDs.
Research on Synchronous Control Strategy of Dual-Drive Feed System Based on Fuzzy PID Control
Authors: Wang Ben , Lu Hong , Liu Qi , Wang Shaojun , Pan Hengchen , Dai Jiashun 
Published: September 2022

Abstract
Dual-drive feed system (DDFS) is widely used in computer numerical control (CNC) machine tools. In the process of machining, it is necessary to ensure the complete synchronization of the two axes of the feed system, otherwise it will affect the machining accuracy and shorten the life of the machine tool. Due to the structure error of the DDFS and the uneven distribution of the load on the two axes in the process of machining irregular workpiece, there are synchronization errors between the two axes. Therefore, it is of great significance to reduce the synchronization errors by studying the dual-drive synchronous control strategy. In this paper, fuzzy control is introduced into traditional PID synchronous control strategy. Compared with traditional PID control, fuzzy control has the characteristics of high robustness and high control performance. Firstly, the PID model of single-axis servo feed system is established. Then, the master-slave control strategy is selected as the dual-drive synchronous control strategy and the model of master-slave control strategy based on conventional PID (MSCS-CPID) is established. Next, the fuzzy PID control is introduced into the current loop of the servo feed system and the model of master-slave control strategy based on fuzzy PID (MSCS-FPID) is established. The simulation results of the MSCS-CPID and the MSCS-FPID show that the DDFS under the MSCS-FPID has faster response speed and smaller synchronization errors. Moreover, the DDFS under the MSCS-FPID has better synchronization performance after external interference. Experiment confirmed that the synchronization performance of the MSCS-FPID is better than that of the MSCS-CPID.
Shape Error Analysis in Ultra-Precision Grinding of Optical Glass by Using Motor-Current-Based Grinding Force Monitoring
Authors: Iinuma Naoki , Chen Boshi , Kawasato Tappei , Kakinuma Yasuhiro 
Published: September 2022

Abstract
4K and 8K technologies are attracting attention in optical industries. The most important mechanical element to enhance the imaging performance is the aspherical lens requiring higher surface quality and higher form accuracy. Currently, the production process of optical lenses consists of brittle-mode grinding and pro-longed polishing process, which play a role of shaping the form and producing the fine surface, respectively. However, this process is not considered to be suitable for manufacturing such higher-quality lenses for 4K and 8K imaging devices because a required form accuracy could not be ensured, and the polishing time gets longer. To enhance the form accuracy and production efficiency, application of ductile-mode grinding is expected to reduce polishing amount. However, the shape error generated by the ductile mode grinding is not clear. Therefore, the purpose of this research is to analyze the relation between the shape error and the grinding force estimated from motor-current in the grinding machine. The motor-current acquisition system in all translational axes and the work spindle is constructed and implemented into a 4-axis ultra-precision aspherical machine. The grinding force in each axis is derived by subtracting the motor current during non-grinding previously obtained in air-grinding test from the current during grinding. Firstly, the behavior of the motor current in each axis is investigated from the viewpoint of repeatability and position dependency. While the periodic fluctuation of the motor current affected by the influence of permanent magnet in the linear motor is confirmed, it shows high repeatability at each position. This result indicated that grinding force is easily calculated from the motor current with less uncertainty. Then, influence of grinding condition in the range of ductile mode grinding on the shape error is analyzed by monitoring the motor current. Toward the outside of the workpiece, the shape error gradually increases with the increase of motor current, which means larger grinding force at the outer side causes the deformation of the resin grinding wheel.