bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of Dynamic Parameter for Gantry Mobile-Type Dual-Drive Machine
Authors: Hu Min , Lu Hong , Liu Qi , Dai Jiashun , Wang Ben , Wang Shaojun 
Published: September 2022

Abstract
Due to the principle of driving at gravity center (DGC), Gantry mobile-type dual-drive machines (GMDMs) are widely used in automatic equipment. The dynamic characteristics of GMDMs have a large influence on the machining quality, efficiency and processing performance of the machine tool. According to the existing research on the dynamic characteristics of GMDMs, it can be found that the dynamic characteristics of the machine tool can be influenced by the feed parameters. So, combined with the research content of the subject, the influence of feed speed on the dynamic characteristics of machine tools under different accelerations is studied. In this paper, based on the law of conservation energy, the dynamic model of GMDMs is established. Then, the accuracy and correctness of the dynamic model are verified through theoretical calculations combined with static hammering experiments, this can provide strong theoretical support for subsequent experiments. And the influence of feed speed under different motor acceleration on the dynamic characteristics of the dual-drive system was studied by two experiments. This study provides a method for analyzing the effect of the feed parameters on the dynamic characteristics of the machine tool under different accelerations.
Towards Directed Energy Deposition of Metals Using Polymer-Based Supports: Hardness of 316L Stainless Steel Deposited on Carbon-Fiber-Reinforced ABS
Authors: Kurfess Rebecca , Kannan Rangasayee , Feldhausen Thomas , Saleeby Kyle , Hart A. John , Hardt David 
Published: September 2022

Abstract
Directed energy deposition (DED) is increasingly considered for manufacturing aerospace components and mold tooling with internal cooling channels, and for repair applications, but the design space of DED is limited: steep overhangs and bridge geometries are difficult or impossible to manufacture because support structures must be rigid and monolithic. Dissimilar metals may be used as supports, but these have proven difficult to manufacture and remove. Polymer supports in DED could provide a lower-cost, easily removable alternative, but the suitability of polymer substrates for DED components has not been explored. Crucial to the viability of this concept is understanding the thermal and mechanical stability of metal deposition onto polymers, and the properties of the solidified metal. Here, the deposition of 316L stainless steel onto carbon-fiber-reinforced ABS is investigated. Solid, box-shaped structures were manufactured with different inter-layer cooling times to study the interface between the metal and polymer composite and to determine the effect on the metal of the formation of carbonaceous polymer char generated during the DED process. Micro-hardness measurements across components with varying inter-layer cooling times were critically analyzed and correlated to the underlying structural changes in 316L at the interface. Due to the infiltration of char, the hardness of the metal directly adjacent to the polymer composite substrate was over 60% greater than the expected hardness value of deposited 316L stainless steel in the component with no interlayer cooling time.
Prediction of Weld Defects in Underwater Friction Stir Welding of Dissimilar Materials
Authors: Mahto Raju Prasad , Dutta Ananta , Mishra Debasish 
Published: September 2022

Abstract
Friction Stir Welding (FSW) of aluminum and steel is often encountered with the formation of weld defects due to the improper material flow in the process. This also leads to the formation of inhomogeneous microstructures and non-uniform thickness of inter-metallic layers at the weld interface. The defects, heterogeneous size and orientations of grains, and thickness of intermetallics can be reduced in underwater friction stir welding but cannot be avoided. The destructive tests involved for the identification of weld defects is expensive and time consuming. The prediction of weld defects can also be carried out by the application of signal processing approach on the welding signals such as axial force and spindle torque. In the present work, the discrete wavelet transformation, a signal processing approach has been applied on the axial force and torque which decompose signals into detail and approximate coefficients through filter banks in time-frequency domain. Later different frequency components have been calculated to predict the weld defects. The results have been verified with optical micrographs and X-ray tomography results. Tensile shear strength and hardness of FSWed have been investigated. In addition, microstructures of the welded samples have been studied to understand the variations in the hardness of weld regions.
Machine Learning-Based Cutting Constant Estimation for Mechanistic Force Models of End Milling Operation
Authors: Vaishnav Shubham , Desai K. A. 
Published: September 2022

Abstract
A predictive cutting force model is essential for power requirement estimation, cutting tool design, surface error estimation and stability analysis during the end milling operation. Mechanistic model estimates cutting forces by correlating analytically computed chip geometry with lumped coefficients combining tool-work material properties through empirical relationships. Establishing reliable relationships through the statistical curve-fitting is demanding due to the need for several experiments, anomaly or noise in the experimental data, and process disturbances that deteriorate the goodness of fit. Machine learning models can effectively deal with such inherent uncertainties and serve as an alternative to the statistical curve-fitting. The present work proposes to improve the empirical relationship between instantaneous uncut chip thickness and cutting coefficients by employing a deep learning algorithm, namely Adaptive Moment Estimation (ADAM). The ADAM algorithm is augmented with decoupled weight decay and warm restart features for the improved performance. The decoupled weight decay assigns dynamic sensitivity values to the data points for outlier removal resulting in better model generalization, while warm restart allows better guesses through adaptive learning rates. The proposed approach has been implemented as a computational tool to determine improved coefficients values and empirical relationships. The cutting forces predicted using coefficient values determined using statistical curve fitting and ADAM-based machine learning are compared with experimentally measured data over an extensive range of cutting conditions. It is concluded that the augmentation of the ADAM approach enables the Mechanistic force model to effectively capture end milling process physics by estimating better coefficients values resulting in enhanced prediction abilities.
Controlled Wetting of Spread Powder and its Impact on Line Formation in Binder Jetting
Authors: Inkley Colton , Martin David , Clark Brennen , Crane Nathan 
Published: September 2022

Abstract
Binder Jetting (BJ) has increased in popularity and capability since its development at MIT as it offers advantages such as fast build rates, integrated overhang support, low-power requirements, and versatility in materials. However, defects arise during layer spreading and printing that are difficult to remove during post-processing. Many of these defects are caused by particle rearrangement/ejection during binder deposition. This study explores methods of reducing particle rearrangement and ejection by applying small amounts of moisture to increase the cohesive forces between powder particles. A moisture application system was built using a piezo-electric disk to atomize water to apply a desired liquid to the BJ powder bed without disruption. The moisture is applied after spreading a new layer. Lines of binder were printed using varying droplet spacings and moisture levels. Results show that the moisture delivery system applied moisture levels across the entire application area with a standard deviation under 23%. The moisture levels delivered also had a single position test-to-test uniformity standard deviation under 21%. All tested levels of moisture addition showed mitigation of the balling defects observed in lines printed using dry powder under the same parameters. Moisture addition decreased effective saturation and increased line dimensions (height and width), but lines printed using the smallest amount of moisture tested, showed similar saturation levels and line widths to lines printed in dry powder while still partially mitigating balling.
A Machinability Study of Coated Inserts for Turning Ti-6Al-4V
Authors: Khawarizmi Ryan M. , Guo Yang , Bieler Thomas R. , Kwon Patrick 
Published: September 2022

Abstract
Titanium (Ti) alloys are classified as difficult-to-machine materials due to low thermal conductivity, low modulus, and high chemical reactivity. In this paper, a Ti-6Al-4V alloy bar in a solution treated and aged (STA) microstructure is turned using uncoated carbide, two commercial multilayered coated carbides (Sandvik® MM1115 and MM11125), and several other advanced coated carbide tools with AlTiN, TiAlN, ZrN, BAM, and (AlCrSi/Ti)N coatings that were fabricated for this study. While coatings with increased hardness and chemical stability are expected to provide better protection against tool wear, coated inserts have not been successfully implemented in machining Ti alloys. A series of turning experiments was carried out while measuring the cutting forces using a dynamometer at three cutting speeds (61, 91, and 122 m/min), and the extent of tool wear on the inserts was assessed using Confocal Laser Scanning Microscopy (CLSM). Among the inserts tested, the (AlCrSi/Ti)N coated insert with a 7 μm coating thickness provided the best performance compared to other inserts, but only at the cutting speed of 61 m/min.
Characterizing the Conductivity of Aerosol Jet Printed Silver Traces on Glass Using Intense Pulsed Light (IPL)
Authors: Ferris Connor , Ratnayake Dilan , Curry Alex , Wei Danming , Gerber Erin , Druffel Thad , Walsh Kevin 
Published: September 2022

Abstract
Aerosol Jet Printing is a novel micron-scale printing technology capable of handling a variety of materials due to a large print material viscosity range and high substrate standoff distance of 3–5 mm. To finalize the properties of printed materials, a form of post-processing is often required. A current widely applicable post-processing technique exists in traditional oven curing. However, oven curing greatly restricts the viable substrates as well as curing time. Intense Pulsed Light (IPL) offers the chance to greatly expand this substrate variety and decrease curing time. However, limited models currently exist to relate the finished material properties to the unique settings of current IPL technology. In this paper, an experiment is developed through a General Full Factorial Design of Experiments (DOE) model to characterize conductivity of Ag ink using IPL as a post processing technique. This is conducted through Novacentrix Ag ink (JSA426) by 3 × 3 mm Van der Pauw sensor pads cured using IPL. Sample pads were generated in triplicate over a range of Energy Levels, Counts and Durations for IPL and the resulting conductivity measured. The collected conductivity data was then analyzed using ANOVA to determine the significant interactions. From this, a regression model is developed to predict the conductivity for any Energy-Count-Duration value. The methods employed are applicable to any post-processing technique, and further optimization of the model is proposed for future work.
A Novel Quality Clustering Methodology on Fab-Wide Wafer Map Images in Semiconductor Manufacturing
Authors: Hsu Yuan-Ming , Jia Xiaodong , Li Wenzhe , Lee Jay 
Published: September 2022

Abstract
In semiconductor manufacturing, clustering the fab-wide wafer map images is of critical importance for practitioners to understand the subclusters of wafer defects, recognize novel clusters or anomalies, and develop fast reactions to quality issues. However, due to the high-mix manufacturing of diversified wafer products of different sizes and technologies, it is difficult to cluster the wafer map images across the fab. This paper addresses this challenge by proposing a novel methodology for fab-wide wafer map data clustering. In the proposed methodology, a well-known deep learning technique, vision transformer with multi-head attention is first trained to convert binary wafer images of different sizes into condensed feature vectors for efficient clustering. Then, the Topological Data Analysis (TDA), which is widely used in biomedical applications, is employed to visualize the data clusters and identify the anomalies. The TDA yields a topological representation of high-dimensional big data as well as its local clusters by creating a graph that shows nodes corresponding to the clusters within the data. The effectiveness of the proposed methodology is demonstrated by clustering the public wafer map dataset WM-811k from the real application which has a total of 811,457 wafer map images. We further demonstrate the potential applicability of topology data analytics in the semiconductor area by visualization.
Ultrafast Laser Direct Writing of Conductive Patterns on Polyimide Substrate
Authors: Biswas Ishrat Jahan , Contreras Lopez Enrique , Ahmed Farid , Li Jianzhi 
Published: September 2022

Abstract
Laser direct writing (LDW) is a fast and cost-effective method for printing conductive patterns in flexible polymer substrates. The electrical, chemical, and mechanical properties of polyimide (PI) make it an attractive material choice for laser writing of conductive circuits in such polymer. Electrically insulating PI has shown great potential for flexible printed electronics as LDW enables selective carbonization in the bulk of such material leading to the formation of conductive lines. However, existing studies in this area reveal a few key limitations of this approach including limited conductivity of written structures and fragility of carbonized PI. Therefore, more research is required to overcome those limitations and reap the benefits of the LDW approach in writing flexible electronic circuits in PI. The proposed study investigates potential approaches to enhance the electrical conductivity of femtosecond laser written bulk carbon structures in PI films. Deposition of laser energy was varied by changing key process parameters such as pulse energy, pulse picker divider, and hatch distance of laser scan to maximize the conductively of the carbon structure. The experimental findings show a strong dependency of laser energy deposition on the conductivity carbon structures in PI films. To further enhance the electrical conductivity of laser written structures, the feasibility of adding copper microparticles to the PI solution and subsequent laser carbonization was studied. The proposed LDW of conductive lines has potential in flexible electronic circuits and sensing applications.
A Neural Network-Based Framework of Real-Time Heat Map Generation for Surgical Hand-Held Drilling
Authors: Kung Pei-Ching , Heydari Mahtab , Tai Bruce L. 
Published: September 2022

Abstract
Understanding heat generation can help improve one’s surgical drilling skill to avoid thermal injury. Surgical drilling is mostly done manually, so it can be time-consuming to create personalized thermal models to assess each drilling. For this reason, this paper presents a framework for 2D real-time heat map generation for a moving, varying heat source problem based on neural networks (NN) and linear time-invariant system (LTI). In this framework, several location-specific heat maps and their temporal responses are calculated by finite element analysis (FEA) and trained through NN to build a surrogate model. The total heat map of any given moving heat source can be generated by the superposition of a series of location-specific heat maps along the moving path. The NN training shows a correlation over 99%, indicating a highly representative surrogate model. The validation study of comparing two FEA-based moving heat source problems with the framework predicted results show overall good agreement. Error sources and improvement methods are discussed in this paper.
Curating Datasets of Flexible Assemblies to Predict Spring-Back Behavior for Machine Learning Purposes
Authors: Adrian Alex , Ramnath Satchit , Sunkara Sai Chandu , Korkolis Yannis , Davidson Joseph K. , Shah Jami J. 
Published: September 2022

Abstract
There are many sources of manufacturing variations in sheet metal assemblies, such as automotive bodies. These include non-isotropic material properties from cold rolling, springback in stamping, and distortion from residual stresses when components are clamped and spot welded. FE simulations have been used to predict these variations in order to better design tooling and processes. Such simulations require expertise in complex, multi-stage nonlinear analysis. We are investigating the feasibility of training machine learning algorithms in order to democratize these types of analyses. This requires the curation of large, validated, and balanced data sets. To this end, we have developed a multi-stage finite element simulation workflow encompassing component stamping and joining with a focus on examining deformations due to springback in two-part assemblies. Three connected simulations comprise the workflow: (1) component stamping with capture of springback, (2) assembly clamping, and (3) assembly joining, then release. The workflow utilizes explicit dynamic finite element analysis (FEA) and includes the transfer of intermediate solutions (geometries/stresses), as well as extraction of key geometric parameters of springback from both component- and assembly-level simulations. The NUMISHEET 1993 U-draw/bending benchmark was referenced for its tooling geometry and utilized for verification of the forming process simulation; variations of material and geometry were also simulated. In summary, this work provides a means of generating a design space of flexible two-part assemblies for applications such as dataset generation, design optimization, and machine learning.
Investigating the Heterogeneity in Microstructure Evolution During Selective Laser Melting of Titanium Aluminides: An Integrated Experimental and Modeling Study
Authors: Zhang Xing , Mushongera Leslie , Liao Yiliang 
Published: September 2022

Abstract
Additive manufacturing (AM) has shown great potentials in fabricating titanium aluminides (TiAl-based alloys) toward high-temperature components in aerospace and automotive applications. However, due to the complex thermal conditions during AM, the as-printed components typically contain heterogeneous microstructure, leading to nonuniform mechanical properties. A thorough understanding of microstructure evolution during AM is necessary to fabricate high-performance TiAl-based components. In this work, the mechanism for the formation of heterogeneous microstructure during selective laser melting (SLM), particularly the spatial variations in sub-grain cellular structure, was revealed by a computational framework. Specifically, a binary Ti-45Al (at.%) alloy was used for the SLM experimental observation and model development to investigate the process-microstructure relationship. The computational framework integrates a finite element thermal model and a phase-field microstructural model. A particular focus was put on the local sub-grain cellular structure evolution within the melt pool. The microstructural sensitivity to spatial variations and individual processing parameters were investigated to better understand the non-equilibrium solidification during SLM. Good agreements in the sub-grain size were achieved between experimental measurements and modeling predictions. This work presents valuable insights and guidance toward the process optimization and alloy design for fabricating high-performance TiAl-based alloys.
Characterization of Stress Shielding in Pressure-Assisted Ceramic Binder Jetting
Authors: Kirby Levi , Fei Fan , Song Xuan 
Published: September 2022

Abstract
Ceramic binder jetting processes have inherent limitations in achieving high density due to the low packing density of the powder bed. An emerging route to mitigate the low packing density in ceramic binder jetting entails uniaxial compaction of newly spread powder layers prior to ink deposition. The introduction of layerwise pressure induced a stress shielding effect, i.e., unbalanced stresses between the printed region saturated with ink and the surrounding loose powder, which generates heterogeneous stress in the powder bed and ultimately influences the density of the final part. In this paper, we attempt to better understand the stress shielding effect during the compaction of a selectively ink-jetted powder bed as a function of the printing pattern, i.e., ratio of printed to unprinted sector. Our findings reveal a decreased print area increased the resulting stress shielding effect. Additionally, when pressed without neighboring dry powder, a printed region experienced a much higher stress than the hybrid composition. The dry powder experienced the opposite effect, where when pressed alone, the dry powder had a much lower stress than when pressed with saturated powder. Findings will assist in density prediction and print pattern determination of compacted binder jetted ceramics.
In-Process Machining Process Monitoring Method Based on Impedance Model of Dielectric Coating Layer at Tool-Chip Interface
Authors: Chun Heebum , Kim Jungsub , Nam Jungsoo , Ju Songhyun , Lee ChaBum 
Published: September 2022

Abstract
In this study, we investigated a novel approach that enables the in-process machining process monitoring at the tool-chip interface (TCI) by utilizing the impedance characteristics of the dielectric coating layer of the cutting tool. This study first analyzes the Nyquist diagram that characterizes the impedance response of a few micrometer-thick dielectric layers coated on the surface of the cutting tool by using an impedance analyzer under various temperature conditions for establishing the relationship between the relative permittivity of the dielectric layer and temperature. Consequently, the impedance of the dielectric layer was subject to change according to given temperature conditions. Thus, under its temperature-dependent impedance characteristics, the machining processes could be in-situ tracked and analyzed by directly probing the localized TCI, the so-called cutting hot spot, during the machining. The current source was implemented with the machining system and the variations of impedance at TCI were monitored during the facing process. As a result, impedance responses were remarkably changed under various machining conditions. The impedance was further characterized under the varying depth of contact and the impedance was decreased as the depth of contact increased. Therefore, the preliminary study demonstrated that an electrical impedance model of the dielectric coating layer may be applied for an in-process machining process monitoring method to analyze and assess the phenomenon of the machining process at the local TCI region. This study is expected to potentially provide utilization in advanced manufacturing to improve final part quality and productivity.