bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling and Model-Driven of Holonomic System Based on MBSE: a Case of Internet of Things Platform
Authors: Yang Hao , Zhao Chun , Shen Nana , Liu Wenzheng , Zhang Lin
Published: September 2022

By integrating the Internet of Things, artificial intelligence, 5G, and other new-generation electronic information technologies, the fourth industrial Revolution represented by intelligent manufacturing and industrial internet is promoted, which is the era of comprehensive intelligent industry 4.0. As a key technology of the industrial Internet, the Internet of Things (IoT) connects intelligent manufacturing complex systems and machines with built-in sensors to the network for real-time data collection, transmission, processing, and feedback, to optimize device management and production efficiency. With the increasing number and variety of IoT devices, improving the scalability and maintainability of IoT systems is a challenging demand and requires continuous efforts. This paper proposes an architecture of IoT platform based on Model-Based Systems Engineering (MBSE). In this architecture, a modeling method based on Integrated Modeling language and a model-driven method for cloud-edge collaboration platform is further proposed. The standardization, readability, and reusability of the model are used to drive the device expansion and management. The characteristics of interaction behaviours between cloud and edges are extracted, and models of Holonomic System are built by an integrated modeling language, called X language. Block Definition Diagram (BDD) of X language is used to build the static models of IoT devices and drive the platform to manage the devices. State Machine Diagram (SMD) of X language is used to build the dynamic models of process between the edges and cloud, and drive the processes of the platform. Through experiments and analysis, the feasibility and effectiveness of the X-Language-driven IoT platform are verified.
Effects of Pressure on Reusable Self-Assembled Microsphere Masks for Microsphere Photolithography
Authors: Zhu Chen , Kinzel Edward C.
Published: September 2022

Microsphere photolithography (MPL) has shown promise for the low-cost large-scale manufacturing of infrared (IR) metasurfaces. One challenge of the technique is that the microsphere array needs to be in immediate proximity to the photoresist because of the near-filed effect of the photonic jet. This is typically accomplished by directly transferring the microsphere array onto the photoresist layer. The microspheres are then washed away during the development of the photoresist. While there may be a possibility of recovering, cleaning, and reusing the microspheres, this is not typically done. This work studies the self-assembly of the microspheres on a superstrate which can be reused as a contact mask. The microspheres are fixed to this superstrate to minimize debonding when they are brought into contact with the substrate. IR metasurfaces are fabricated and spectrally characterized. The resonant wavelength of IR metasurfaces is shown to be a good statistical metric for the variation of the patterned surface. The results indicate pressure between the substrate and superstrate is a critical factor in maintaining a minimum gap between the microspheres and photoresist. This work shows a way forward for mask-based microsphere photolithography and provides guidance for future microlens array-based photolithographic techniques.
Experimental and Numerical Investigation of Heat Assisted Incremental Sheet Forming Process of Magnesium Alloy
Authors: Kumar Narinder , Mahala Mohit , Agrawal Anupam
Published: September 2022

Magnesium alloys possess exceptionally good mechanical properties, primarily their excellent high strength to weight ratio, and have attracted many applications in the automobile and aerospace industries. However, their use is limited by the poor formability at room temperature when processed through conventional processes because the crystal lattice structure of magnesium is hexagonal closed packed (hcp), due to which there are limited sliding planes. At the elevated temperature ranges of 200–300 °C, more sliding planes get activated, which increases the ductility and decreases the flow stress. It leads to enhanced formability at a higher temperature for magnesium alloys. Therefore, several methods of heat-assisted single point incremental forming process (HA-SPIF) have been established by many researchers in order to improve the forming limits of such hard-to-deform materials. In this study, a new method of the heat-assisted single-point incremental forming process (HA-SPIF) is developed by using cartridge heaters to enhance the forming limits. The influence of higher temperature on fracture depth and thickness distribution of AZ31B magnesium alloy sheet is studied in detail. Experimental results indicate that the fracture depth and thickness distribution increases as the temperature increases. A coupled thermo-mechanical numerical simulation model using ABAQUS/EXPLICIT® is developed to predict forming limits; it was validated using the experimental results. The Johnson-Cook model was implemented as the constitutive model and also to define the fracture criterion. A reasonably good agreement between the results of the numerical simulation and those of the experiment is observed.
Ultrafast Laser-Induced Surface Structuring of Anti-Fouling Steel Surfaces for Biomedical Applications
Authors: Cholkar Abhijit , McCann Ronan , Kinahan David , Brabazon Dermot
Published: September 2022

Metallic surfaces are increasingly used in medical applications due to their favorable material properties such as high strength and biocompatibility. In medical applications anti-fouling properties are an important requirement especially for implants and medical devices which come into contact with different types of fluid streams. These should be anti-fouling in order to prevent contamination and corrosion. Laser processing methods such as ultrafast laser processing is a one-step and scalable process for surface texturing. This process can be used to produce well-defined surface nano- and microscale superficial textures such as Laser-induced Periodic Surface Structures (LIPSS) which can enhance the anti-fouling capability of the surface.

In this study, micro and nano scaled LIPSS structures are manufactured on a biocompatible grade stainless steel 316L substrate using an ultrafast (< 370 fs) and low power (< 4 W) laser system. With an aim to optimize the anti-fouling properties, laser process parameters such as pulse energy, pulse repetition rate and beam scanning speed were varied to produce microstructures on the stainless-steel surface of varying dimensions. Surface roughness was analyzed using a laser surface profilometer and changes in the hydrophobicity were examined using water contact angle goniometry.
Tiling of Cellular Structures Into 3D Parts According to the Density Values of SIMP Topology Optimization
Authors: Ozkapici Helvaci Damla , Yaman Ulas
Published: September 2022

In this study, a novel approach is proposed to enhance the performance of the parts optimized by Solid Isotropic Material with Penalization (SIMP) method. SIMP is a topology optimization method that aims the optimum distribution of material in a design domain subjected to predefined loads, constraints and boundary conditions. The method forces every finite element composing the geometry to have a density of either 1 or 0. The main reason behind penalizing is that regions with intermediate densities are difficult to fabricate. However, including these regions in the optimization output may provide better performance results. Based on this idea, a method is proposed to utilize intermediate densities in a manufacturable form and is applied to 3D geometries. Besides, the remodeled topology is checked against any unconnected cells. In contrast to many methods, which delete the unconnected elements, the proposed method provides connectivity by adding cells.

The outputs of the proposed method are fabricated by using Electron Beam Melting (EBM) and Stereolithography (SLA) technologies. EBM uses material powder and a heat source to melt and fuse the powders while SLA uses photosensitive resin and an ultraviolet light to cure the resin. A common limitation of both technologies is that powder/resin may remain inside the internal features which do not have access to outer surface of the part through the channels. The proposed method ensures the easy removal of excess powder/resin after fabrication. Performance of the method is compared with the SIMP method through test and analysis.
A Deterministic Inspection of Surface Preparation for Metalization
Authors: Shokri Shiva , Sedigh Pooria , Hojjati Mehdi , Kwok Tsz-Ho
Published: September 2022

To improve the surface properties of fiber-reinforced polymer composites, one method is to employ thermal spray to apply a coating on the composite. For this purpose, it uses a metal mesh serving as an anchor between the composite and the coating to increase adhesion. However, the composite manufacturing covers the metal mesh with resin, and getting an acceptable coating is only possible through an optimum exposure of the metal mesh by sand blasting prior to coating. Therefore, this study aims to develop a computer vision and image processing method to inspect the parts and provide the operator with feedback. Initially, this approach takes the images from a single-view microscope as the inputs, and then it classifies the images into two regions of resin and metal mesh using the Otsu’s adaptive thresholding. Next, it segments the resin areas into distinct connected clusters, and it makes a histogram based on the clusters’ size. Finally, the distribution of the histogram can determine the status of the surface preparation. The state-of-the-art has only examined the sand-blasted composites manually, requiring expertise and experience. This research presents a deterministic method to automate the inspection process efficiently with an inexpensive portable digital microscope. This method is practical, especially when there is a lack of standardized data for machine learning. The experimental results show that the method can get different histograms for various samples, and it can distinguish whether a sample is under-blasted, proper-blasted, or over-blasted successfully. This study also has applications to various fields of manufacturing for defect detection and closed-loop control.
Individual-Based Transfer Learning for Dynamic Human-Robot Collaborative Disassembly Line Balancing
Authors: Fang Yilin , Zhang Xiao
Published: September 2022

This paper analyzed the human-robot collaborative disassembly line balancing problem, which is significantly different from the traditional disassembly line balancing problem. In a human-robot collaborative disassembly line, multiple people and robots perform disassembly tasks at each workstation. Due to the uncertainties such as product quality and human capabilities, the human-robot collaborative disassembly line balancing problem is a dynamic optimization problem. We take into account the uncertainty of product quality and personnel capabilities. In addition, dynamic optimization problems require fast and accurate tracking of Pareto’s optimal solution set in a changing environment, and transfer learning has been proven appropriate. Therefore, an individual-based transfer learning-assisted evolutionary dynamic optimization algorithm has been developed to handle the human-robot collaborative disassembly line balancing problem. The algorithm uses an individual-based transfer learning technique to reuse experience, which accelerates the generation of the initial population and improves the convergence speed of solutions. Finally, based on a set of problem examples generated in this paper, the proposed algorithm is compared and analyzed with several competitors in terms of the mean inverted generational distance and the mean hyper-volume, verifying the effectiveness of the proposed algorithm on the dynamic human-robot collaborative disassembly line balancing. The results show that the proposed algorithm has good performance in large scale problems.
Developing Hybrid Hydrogels for Full-Scale Scaffold Fabrication Using Extrusion-Based Bioprinting Process
Authors: Nelson Cartwright , Tuladhar Slesha , Habib MD Ahasan
Published: September 2022

Three-dimensional (3D) bioprinting is a technology that has the power to positively change the medical and pharmaceutical fields in a new and more intuitive way. The goal of this rapidly growing field is to recreate functional tissues, but the process requires the ability to achieve large full-scale scaffolds that replicate human organs. There are many challenges when attempting to print large scaffolds ensuring proper internal and external geometric fidelity that is also suitable for the living cells that undergo the printing process. In order to fabricate a larger and more structurally sound scaffold, higher material viscosities are necessary. This increase in viscosity comes with an increase in printing pressure, which can create unbearable shear stress and eventually damage cells, diminishing viability and proliferation. A set of biomaterial compositions with high structural integrity and shape fidelity that did not require harmful amounts of pressure for extrusion was identified by analyzing rheological, mechanical, and microstructural properties. Many different large-scale scaffolds maintaining geometric fidelity were fabricated with heights up to 3.0 cm and 74 layers using these hybrid hydrogels. This advancement can ensure precise internal and external geometries of full-scale functional tissue replicating scaffolds using 3D bio-printing processes that utilize pressures and materials safe for live cell viability and proliferation.
Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human-Robot Collaboration
Authors: Liao Hao-yu , Chen Yuhao , Hu Boyi , Behdad Sara
Published: September 2022

Disassembly is an integral part of maintenance, upgrade, and remanufacturing operations to recover end-of-use products. Optimization of disassembly sequences and the capability of robotic technology are crucial for managing the resource-intensive nature of dismantling operations. This study proposes an optimization framework for disassembly sequence planning under uncertainty considering human-robot collaboration. The proposed model combines three attributes: disassembly cost, disassembleability, and safety, to find the optimal path for dismantling a product and assigning each disassembly operation among humans and robots. The multi-attribute utility function has been employed to address uncertainty and make a tradeoff among multiple attributes. The disassembly time reflects the cost of disassembly and is assumed to be an uncertain parameter with a Beta probability density function; the disassembleability evaluates the feasibility of conducting operations by robot; finally, the safety index ensures the safety of human workers in the work environment. The optimization model identifies the best disassembly sequence and makes tradeoffs among multi-attributes. An example of a computer desktop illustrates how the proposed model works. The model identifies the optimal disassembly sequence with less disassembly cost, high disassembleability, and increased safety index while allocating disassembly operations between human and robot. A sensitivity analysis is conducted to show the model’s performance when changing the disassembly cost for the robot.
Effect of Powder Layer Thickness on Residual Stress in Laser Powder Bed Fusion of IN718
Authors: Mirkoohi Elham , Tran Hong-Chuong , Lo Yu-Lung , Chang You-Cheng , Lin Hung-You , Liang Steven Y.
Published: September 2022

Understanding the relationships between the processing factors in laser powder bed fusion (L-PBF) and residual stress formation is critical for improving the final part performance since tensile residual stress has a critical impact on fatigue life performance and final material properties of the manufactured part. Laser powder bed fusion has virtually infinite number of processing factors each can impact the residual stress, among which, layer thickness is one of them. This paper investigates the impact of powder layer thickness on residual stress formation for the Ni-based super-alloy Inconel 718 (IN718). Test coupons with different layer thicknesses are fabricated with the same geometry. Residual stress is determined via X-ray diffraction (XRD) at different locations along the build direction and transverse direction. In addition, the previously developed physics-based analytical model is used to draw a relationship between powder layer thickness and residual stress through the prediction of temperature field and thermal stress using incremental plasticity. The results of computational modeling and experiments are in good agreement and shows that the increase in powder layer thickness increases the residual stress in an additively manufactured part.
Physics-Informed Machine Learning for Accelerated Testing of Roll-to-Roll Printed Sensors
Authors: Chandra Mouli S. , Sedaghat Sotoudeh , Ramazan Oduncu Muhammed , Saha Ajanta , Rahimi Rahim , A. Alam Muhammad , Wei Alexander , Shakouri Ali , Ribeiro Bruno
Published: September 2022

Roll-to-roll printing has significantly shortened the time from design to production of sensors and IoT devices, while being cost-effective for mass production. But due to less manufacturing tolerance controls available, properties such as sensor thickness, composition, roughness, etc., cannot be precisely controlled. Since these properties likely affect the sensor behavior, roll-to-roll printed sensors require validation testing before they can be deployed in the field. In this work, we improve the testing of Nitrate sensors that need to be calibrated in a solution of known Nitrate concentration for around 1–2 days. To accelerate this process, we observe the initial behavior of the sensors for a few hours, and use a physics-informed machine learning method to predict their measurements 24 hours in the future, thus saving valuable time and testing resources. Due to the variability in roll-to-roll printing, this prediction task requires models that are robust to changes in properties of the new test sensors. We show that existing methods fail at this task and describe a physics-informed machine learning method that improves the prediction robustness to different testing conditions (≈ 1.7× lower in real-world data and ≈ 5× lower in synthetic data when compared with the current state-of-the-art physics-informed machine learning method).
Experimental Analysis of Metal Inert Gas Based Wire Arc Additive Manufacturing of Aluminum Nanocomposite AA7075
Authors: Darnell Mason , Harwig Dennis , Liu Xun
Published: September 2022

This work studies Metal Inert Gas (MIG) based Wire Arc Additive Manufacturing (WAAM) for nanoparticle enhanced AA7075. MIG WAAM is important for production and large structures due to its high deposition rates compared to Tungsten Inert Gas (TIG) or powder-based AM processes. Both MIG and TIG take advantage of wire feedstock, which is more readily available than powdered metals since the welding technology has been established for decades. Powder based processes allow for more complicated geometries but take significantly more time to produce and can suffer from voids which lead to non-uniform part density. TIG is normally used in welding of aluminum because it results in fewer defects, but the TiC/TiB2 nanoparticles eliminate solidification cracking normally associated with high strength aluminum alloys during welding. Porosity is another problem faced when welding aluminum, which can be affected by many things including deposition parameters, atmosphere and even the welding equipment used. Effects of different deposition parameters have been comprehensively studied including the deposition geometry and metallurgical properties. The process is also monitored with current/voltage measurement and high-speed imaging to understand the droplet transfer mode and molten pool development. The results are used to optimize process parameters to achieve the fewest defects possible while comparing different metal transfer modes. Multi-scale characterizations will be performed to examine the porosity distribution, solidification mode and grain size through optical microscopy. Future works will explore the distribution of secondary phases, precipitates, and nanoparticles through scanning electron microscopy (SEM) as well as conducting some mechanical testing of the as built structures such as hardness mapping and tensile tests.
Machine Vision Tracking and Automation of a Microrobot (sAFAM)
Authors: Warn Colin , Sherehiy Andriy , Alqatamin Moath , Ritz Brooke , Zhang Ruoshi , Chowdhury Sri S. , Wei Danming , Popa Dan O.
Published: September 2022

In this paper, we propose a method for tracking a microrobot’s three-dimensional position using microscope machine vision. The microrobot, theSolid Articulated Four Axis Microrobot (sAFAM), is being developed to enable the assembly and manipulation of micro and nanoscale objects. In the future, arrays of sAFAMS working together can be integrated into a wafer-scale nanofactory, Prior to use, microrobots in this microfactory need calibration, which can be achieved using the proposed measurement technique. Our approach enables faster and more accurate mapping of microrobot translations and rotations, and orders of magnitude larger datasets can be created by automation. Cameras feeds on a custom microscopy system is fed into a data processing pipeline that enables tracking of the microrobot in real-time. This particular machine vision method was implemented with a help of OpenCV and Python and can be used to track the movement of other micrometer-sized features. Additionally, a script was created to enable automated repeatability tests for each of the six trajectories traversable by the robot. A more precise microrobot workable area was also determined thanks to the significantly larger datasets enabled by the combined automation and machine vision approaches.
Process Mechanics – a Guide for Industry 4.0: Modelling Cutting of Nimonic 90 and Ti-6Al-4V
Authors: Lortz W. , Pavel Radu
Published: September 2022

The chip formation models developed to date give no exact representation of the physics phenomena occurring during the complex machine cutting process. Despite the large number of investigations and simulations, there is still limited clarity of the real chip formation process. The models try to solve the plastic flow through force or stress simulation, without proper regard to adequate process mechanics. Due to these circumstances, practical evidence is missing. Analyzing this situation very carefully, some scientists founded the Industry 4.0 initiative to create scientific space with new opportunities. Whereas second and third industrial revolutions have been focused on organization and automation — Industry 4.0 is focused on technology, data integration and artificial intelligence (AI). However, before teaching a computer AI, the adequate process mechanics should be systematically developed and understood. This paper presents the complex process mechanics of chip formation with non-linear conditions in the metal microstructure, with two different friction zones, with self-hardening or temperatures effects. These entire phenomena can’t be solved separately because they have an interdependent relationship. The developed mathematical equations for strain and stress lead to square grid deformation in the chip formation zone, and this grid deformation does not disappear after completing the process, so that the theoretical development can be compared with practical results. This will be presented for two different materials Nimonic 90 and Ti-6Al-4V. For Nimonic 90 a built-up-edge (BUE) will be identified, and this is based on the stream-line inflow-angle. Quite contrary is the chip formation process for Ti-6Al-4V. A diffusion process in the interface chip-tool take place resulting in a self-blockade with segmented chip. In addition, the developed temperatures during cutting could be estimated and will be presented for the two different creep-resistant alloys. Finally, a high agreement between the theoretical and experimental results could be documented.
Trajectory Control of 6-DOF Welding Robot Based on STM32
Authors: Li Dingzhong , Lu Hong , Zhang Yongquan , Wu Zidong , Huang He , Liu Meng , Wang Shaojun
Published: September 2022

Automatic welding technology has been widely used in China’s industrial production. Welding robots with the characteristics of high efficiency and high qualification rate began to appear in the industrial production line. However, in order to protect their own competitiveness, most companies use closed source control mode for welding robots, resulting in high control cost, low system openness and poor flexibility. Therefore, it is of great significance to design an open-source and low-cost six degree of freedom welding robot motion control system based on STM32 microcontroller. According to the requirements of motion control system, the development board of dual core processor is selected as the core to complete the hardware design of motion control system. The software design of the control system adopts the framework of cooperative work between the upper computer and the lower computer, and the upper computer is developed with software, which has good human-computer interaction function; The lower computer is programmed through the modular idea, which is convenient for secondary development and has good expansibility. According to the standard DH method, the trajectory planning of the 6-DOF robot is realized, and the S-type acceleration and deceleration algorithm and other related algorithms are used to realize its more efficient and smooth motion control.
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

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

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

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

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

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

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

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

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

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

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

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

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

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.