bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimized Deep Convolutional Neural Network for Wafer Map Defects Classification
Authors: Amogne Zemenu Endalamaw , Chou Jia-Hong 
Published: August 2024

Abstract
Wafer maps contain important information about defect patterns, and are used by engineers to trace the root causes of abnormal processes; early detection can improve the yield of a manufacturing process. Wafer map defects have been classified using a variety of deep convolutional neural networks (DCNN). However, factors such as the number of hidden layers, the input image’s size, and the dropout values in the dense layers can affect classification performance. Class imbalance in the training and testing data sets also affects model performance. We therefore propose an optimal DCNN model, called Opt-DCNN, and compare its defect pattern classification with other DCNN models using an actual wafer map data set (WM-811K). Opt-DCNN is shown to be superior to the three existing DCNN models among the three tested input image sizes, in terms of F1 score, precision, accuracy, and recall. For the 26 × 26 input image size, the proposed method achieves a test accuracy of 99.345%, which is better than the three published models with 98.768%, 98.442%, and 67.622%, respectively. The comparisons indicate that using a DCNN with dropout can improve wafer defect pattern recognition.
Design Strategy for Realizing Optimized Calibration Rod for an End Mill Cutter Based on Receptance Coupling Substructure Analysis
Authors: Tudu Anirban , Gunjan Shashi Bhushan , Devadula Sivasrinivasu 
Published: August 2024

Abstract
Chatter in machining caused by self-excited vibration limits the capability of high-speed spindles and results in poor surface finish and tool wear. Stability lobe diagram (SLD) is widely used to determine chatter-free machining parameters. The SLD for milling needs accurate measurement of frequency response function (FRF) at the cutting tool tip. However, asymmetry and complex geometry of end mills contribute to FRF data with low coherence. The impact testing is difficult due to the minimum area available on the cutting tool’s sharp edges for its excitation and attaching accelerometer. Hence, accurate FRF measurement is time-consuming. The problem gets more prominent when cutting tool rotates. To address this, a calibration rod that mimics the cutting tool is proposed. However, there are limited efforts in identifying optimal design of the calibration rod to capture tool’s dynamic behavior.
The present work aims to develops a design strategy for realizing a calibration rod for an end mill cutter. Towards this, a receptance coupling substructure analysis (RCSA) based approach is employed to evaluate the tool tip FRF for a given length (L) and diameter (D) of the calibration rod. The spindle with solid HSS cylinder of 10 mm diameter as substructure A and cylinder with varying L and D as substructure B were considered. The FRF of substructure A is evaluated by impact test, whereas for substructure B, is evaluated analytically. By minimizing the error between the FRFrcsa and FRFexp of the targeted end mill cutter, the optimal L and D for a calibration rod were obtained. The optimal rods are manufactured to validate the equivalency with the end mill cutter. Experimental validation showed close dynamic responses from the end mill and designed calibration rod. The proposed method’s performance is evaluated for two fluted and four fluted end mills. For the 10 mm four-fluted end mill cutter, the optimal L and D are obtained as 33.5 mm and 7mm, respectively. The evaluated natural frequencies for milling cutter and corresponding calibration rod are observed as 832Hz and 849 Hz, respectively. For 10 mm two-fluted end mill the optimized length and diameter are obtained as 28 mm and 7 mm. Corresponding natural frequencies for end mill and calibration rod are observed as 886 Hz and 888 Hz, respectively.
Particle Analysis From Heterogeneous Background With Deep Learning Tool
Authors: Alam Adeeb , Hassan Md Rafiul , Khoda Bashir 
Published: August 2024

Abstract
The performance of conventional image processing techniques is highly dependent on many parameters like image quality, light source, background surface texture, optimal threshold value and particle morphology. However, during intermediate stages of manufacturing processes (such as continuous deposition, coating, mixing, and transfer), complex backgrounds can arise from heterogeneous particle-substrate (HPS) systems. In such HPS environments, particles become integrated with substrates or suspended in liquid carriers or etching media, making them challenging to identify using traditional particle analysis tools and techniques. In response to this challenge, a deep learning object detection algorithm (YOLO) has been put into practical use. Initially, an HPS (heterogeneous particle-substrate) system was created using a wet-deposition particle transfer process that involved the immersion of poly-disperse particles on to a cylindrical substrate. By manipulating the capillary number in the wet-deposition process, four distinct HPS morphologies were captured, each characterized by variations in image heterogeneity. These morphologies were subsequently subjected to detailed analysis with neural network-based AI algorithm. The proposed artificial intelligence tool has demonstrated an impressive ability to identify and analyze poly-dispersed particles within HPS morphologies, achieving an accuracy rate of over 97%. We can evaluate the quality of sorting by calculating the particle size distribution using the proposed method and find the ideal process parameters for the particle transfer process. The results of this study, outlined in this paper, underscore the potential of deep learning as a particle analysis tool for in-situ applications, even in environments with heterogeneous backgrounds. This developed tool holds promise for various manufacturing processes, including semiconductor industries, high-density powder-based 3D printing, powder metallurgy, refractory coatings in harsh environments, and particle sorting, among others.
Sustainability of Gear Manufacturing With Dry Grinding
Authors: Cestone Lorenzo , Iqbal Hambal , Liverani Erica , Ascari Alessandro , Fortunato Alessandro 
Published: August 2024

Abstract
High quality gears for automotive applications require a high level of geometrical accuracy and surface finish of the gear flank to improve the operational performance while reducing the noise during their lifecycle. Generally, the high-quality gears are produced using a final grinding process for surface finishing, this reduces the surface roughness and geometrical errors caused by the hobbing process and surface hardening techniques. Wet grinding is the most common method in conventional gear manufacturing for this final stage, however, it involves a massive amount of lubricant. Typically, oil or emulsion of oil and water are used as cooling fluids. As a result, every year tonnes of lubricant fluids are incinerated. This causes a significant environmental impact. Moreover, the use of lubricants possesses higher capital and operational costs and requires additional auxiliary equipment in the operation. Three grinding processes, which comprise the final finishing stage in gear production after the hobbing process were compared in this study. These processes include standard wet grinding, optimized wet grinding and dry grinding. In this study, five gears for each of the three process categories were tested under the same loading conditions and number of cycles i.e., 350 Nm and 1.5 million cycles respectively. The standard wet grinding was used as the reference process from Fiat Chrysler Automobiles (FCA) for this comparative study. After the torsional fatigue tests, the surface damages were measured and analysed on the gear flanks produced with each process category. Similarly, the damages for each gear’s flank surface, which resemble a micro-pitting type of damage, were compared and the contribution of the final grinding process was identified to evaluate the vulnerability induced on the component by these finishing processes. Similarly, microstructural analysis and Vickers hardness results further added valuable conclusions for the three process categories. It was concluded that the dry grinding process possesses similar performance with respect to the reference grinding process (FCA) and wet optimised process. Therefore, the feasibility of adopting the dry grinding process in the final stage of gear production is proposed considering the environmental impacts and additional cost of auxiliary equipment related to the use of lubricants. This research highlights the potential for innovative and eco-friendly practices within automotive industry, proposing a cleaner and more efficient gear production process.
Electrospinning Process Modeling: A Multi-Stage View
Authors: Zuniga-Navarrete Christian , Segura Stalin Jamil , Baidya Sabur , Narváez-Muñoz Christian , Toscano Juan Diego , Segura Luis Javier 
Published: August 2024

Abstract
Electrospinning is a versatile electrohydrodynamic-based manufacturing process that can produce micro/nanofibers from polymeric solutions (e.g., polylactide (PLA), polyacrylonitrile (PAN), etc.). It has a variety of applications, including tissue engineering, flexible electronics, filtering, etc. However, the final product performance is drastically affected by the diameter variation of the deposited micro/nanofibers, which is attributed to the process settings, material composition, and ambient conditions. Having a uniform and repeatable electrospun fibers is fundamental to enhancing product functionality, but this is challenging due to the intricate processes that the solutions undergo, from material preparation to product performance. Therefore, it is paramount to understand the different stages that the electrospinning experiences during micro/nanofiber fabrication. In particular, the electrospinning process can be discretized into several stages, namely, stable regimen (i.e., Taylor cone and jet formation), whipping instability, and fiber collection, where the individual stage’s behavior influences the others downstream. The objective of this study is to systematically model the mechanical properties of the non-woven mat via a two-stage simplified framework: (1) the micro/nano fibers’ diameter (i.e., fiber collection stage) is modeled considering process parameters (i.e., voltage, flow rate, and tip-to-collector distance) and in-situ data (i.e., streaming videos from stable regimen and ambient temperature and humidity signals) as predictors. (2) The mechanical properties (i.e., failure-load point) of the non-woven mats are modeled with significant factors of (1), predicted micro/nano fibers’ diameters in (1), and electrospinning manufacturing time (time to build the non-woven mat), as predictors. To achieve this goal, feature extraction via tensor train (TT) is performed to deal with the captured diameters from the stable regimen videos (in our previous work) and ambient temperature and humidity signals. Subsequently, lasso regression is deployed to model each stage in a sequential fashion, resulting in accurate micro/nano fibers’ diameters prediction and subsequent non-woven mat failure-load point prediction. In addition, the framework offers important interpretability that supports not only process understanding but also proper data acquisition recommendations. The framework is demonstrated by electrospinning a PAN-based polymeric solution (PAN 10%).
Dynamic Contact Force Alignment Strategy Against Abrasive Wear in Robotic Compliant Disc Grinding for Uniform Material Removal
Authors: Wang Jingyun , Yang Zeyuan , Yan Sijie , Cai Wenqi , Yang Yisheng 
Published: August 2024

Abstract
Maintaining consistent material removal is a significant concern in robotic compliant disc grinding (RCDG), particularly when confronted with the issue of progressive abrasive wear. To tackle this issue, the present work develops a material removal-based abrasive wear model, and a suitable dynamic contact force alignment strategy is implemented to compensate for wear losses, thus ensuring a uniform material removal process. First, the grinding trajectory and material removal behavior of abrasive grains characterized by random cone angles and heights are developed. Next, the abrasive wear model is presented, which reveals the relationship between uniform material removal and grinding parameters. Then, the contact force variation law, on the target of uniform material removal, is determined by compensating for the losses in material removal caused by abrasive wear. On this basis, a set of active force control devices is designed and employed, which consist of a PC interface, a 6-axis force sensor, and a high-speed motorized spindle, to realize the highly real-time dynamic adjustment of the contact force. Finally, both simulations and experiments were carried out, which indicated that the method proposed is capable of enhancing the machining consistency within a certain grinding time range and providing a more uniform material removal process.
Combining Physics-Based Modeling and Artificial Intelligence for the Optimization of Battery Manufacturing Processes
Authors: Franco Alejandro A. , Vijay Utkarsh , Saravanan Soorya 
Published: August 2024

Abstract
The rapid scaling up and process optimization of manufacturing are essential to keep up with current demand and reduce the cost of lithium-ion batteries. In particular, electrode and cell processing and optimization are key in ensuring reliable lithium-ion batteries. Here, we explain how our hybrid modeling approach, combining physics-based simulations and artificial intelligence, can be used to gain insights into performance-manufacturing relationships and optimize the battery cell manufacturing processes. This hybrid approach is a powerful tool that can simulate the various manufacturing steps from powder to power. With this approach, in combination with the acquisition of battery manufacturing pilot line data, it is possible to perform inverse design of the electrode microstructures and cells for optimal properties. A wide diversity of process parameters is taken into account, such as those in the slurry, its drying, and the resulting electrode calendering, together with the parameters associated with the electrolyte filling, the formation, and the electrochemical operation. We briefly discuss various works done under the umbrella of our ARTISTIC project initiative, demonstrating a comprehensive hybrid approach to pave the way toward digital twins of the manufacturing process of lithium-ion and (also) next-generation battery cells.
Aerosol Jet and Inkjet Printing of Electrical Interconnects for Die Level Packaging of Custom MEMS Devices
Authors: Sherehiy Andriy , Sassa Michael , Jackson Douglas , Sills Daniel , Ratnayake Dilan , Zhang Ruoshi , Yang Zhong , Walsh Kevin , Naber John , Popa Dan O. 
Published: August 2024

Abstract
In this study we propose an alternative, lowcost packaging method using both inkjet and aerosol jet printing to fabricate functional interconnects for custom die-level packages assembled on Printed Circuit Boards (PCB).
Our process involves manufacturing of the stacked structure with insulating and conducting layers, fabricated using inkjet, and aerosol jet printing, respectively. In the first part of the study, we characterized UV adhesive deposition on the standard resin coated PCB, with the help of a Nordson EFD Pico Pulse drop on demand inkjet printer. This was done in order to acquire the optimal parameters necessary for the fabrication of an insulating layer with a thickness around 100 μm.
In the second part of our study, we developed precise printing of silver conducting lines using OPTOMEC Decathlon, an aerosol jet printer, which enables realization of structures with features as small as 50 microns on non-planar surfaces. The insulating part of the MEMS/PCB structure serves as a ramp ensuring continuity of the aerosol jet printed connection between the PCB and Si die. A 1 cm × 1 cm Si chip with custom MEMS microrobot was used to demonstrate the feasibility and flexibility of our approach in practice. After the fabrication, our interconnects were evaluated for conductivity and repeatability. The packaging and inspection process was carried out using our unique robotic system, Nexus, which integrates additive manufacturing, robotic transport, and metrology. Nexus allowed precision control during printing and transition between the main steps of manufacturing process: material deposition, curing, sintering, and microscope inspection. The results show that we successfully fabricated printed interconnects between copper PCB pads and Si die cleanroom fabricated gold pads, whereas electrical characterization revealed resistances in the range 1 Ω – 10 Ω. Our approach can be utilized in manufacturing of electrical interconnects for custom devices on different substrates including traditional and flexible PCBs. Furthermore, applied printing techniques enable the use of other insulating or conducting inks and formation of the structures of custom geometries at wide range of scale – 20 μm to 1 mm.
Deep Learning-Based Recognition of Manufacturing Components Using Augmented Reality for Worker Training of Assembly Tasks
Authors: Deshpande Sourabh , Aryal Manish Raj , Anand Sam 
Published: August 2024

Abstract
Current smart manufacturing computational tools and optimization methods leverage advances from artificial intelligence (AI), augmented / mixed/virtual reality, and predictive modeling to meet factory production goals. With significant developments in computer graphics tools, deep learning methods, and immersive experiences (metaverse), there is a need to study its seamless adaptation to AR-based manufacturing-specific scenarios, especially for worker training. In this work, we develop an augmented reality-based worker training framework aimed at helping novice technicians with complex machine component identification in several smart factory tasks and shop-floor training environments. A Deep-Learning (DL) semantic segmentation network U-Net, prominently applied in several manufacturing use cases, is enhanced for detailed pixel-level component identification. We formulated an automatic synthetic image generation algorithm that uses computer-aided design (CAD) models generated using Siemens NX to create images and automatic labels for training the neural network. These images are custom-rendered with variable lighting and randomized orientations to mimic real-life occlusions and improve data diversity for neural network training. In this approach, arbitrary textured materials are assigned to the 3D objects through Blender, an open-source computer graphics software using Python programming in the input scene environment. The background for these images is set from the multiple real-world environment scenes. A custom Python script integrated into Blender captures the image snapshots through the built-in camera system. It automatically separates render and mask images, thus saving time on the tedious manual labeling process. We pass the virtual images (both render and mask images) to the DL pipeline for training. After training the data through Pytorch and CUDA parallel computing, the Open Neural Network Exchange (ONNX) format output model is imported to the AR app. The application is developed using the Unity 3D game engine platform and deployed on Android. Specifically, the Unity Barracuda toolkit maps the input and output tensors as rendering textures and recognizes components in colored pixels. We present a prototype three-a stage automotive engine assembly case study to evaluate the proposed framework’s effectiveness. The engine components include tools (mallet, chisel, wrench), machine tooling equipment (nuts, bolts, Allen keys, screwdriver), and assembly equipment (engine piston, cylinder head, and camshaft). The 3D-printed components mimicking engine parts are identified in real-time along their exact pixel-level contours in the AR app. This framework can extend AR-based methods to visualize components in complex wiring, cluttered environments, and intricate assemblies on the factory floor, reducing the training time performed through conventional approaches and providing an interactive training approach through gamification.
An Active Machine Learning Approach for Spatial-Temporal Temperature Prediction of a Moving Heat Source
Authors: Heydari Mahtab , Tai Bruce 
Published: August 2024

Abstract
Moving heat source problems are commonly seen in many manufacturing applications, such as machining, laser cutting, welding, and additive manufacturing processes, while numerical modeling often takes time to analyze. A data-driven framework for predicting the spatial-temporal temperature response of a moving heat source has been developed to address the need for real-time temperature prediction of a moving heat source. This paper presents a method to reduce the training data requirements and improve the overall efficiency and accuracy of such a framework. This process, referred to as the boundary-focused training approach, is a type of active learning approach which allows for further scalability of the framework to large and/or complicated geometries by using a clustering approach to classify the spatial data according to differences in its thermal response. Then, a tandem learning approach is adopted to train a series of neural networks for each respective cluster. This boundary-focused training approach is implemented on two materials with different thermal diffusivities to evaluate the suitability of this framework in various contexts. The boundary-focused method showed improvement on the boundary-adjacent cases.
Reconfigurable Tooling for Customized Stretch Forming of Profiles
Authors: Ma Jun , Tronvoll Sigmund A. , Blindheim Jørgen , Ringen Geir , Welo Torgeir 
Published: August 2024

Abstract
Aluminum profiles are increasingly used in the manufacture of lightweight components for electric vehicles. Stretch forming is a key operation for producing extrusion-based components, owing to its volume production capabilities. However, traditional stretch forming is characterized by limited flexibility and high tooling costs, which makes it less attractive in today’s dynamic market demanding rapid adaptation solutions.
In this research, a new 3D reconfigurable multi-piece tooling for a recently developed novel flexible stretch forming process was proposed to improve the flexibility. The new tooling concept is realized by dividing the unified 3D die into multiple adjustable pieces. Each unit includes an adjustable “bottom holder” and an adjustable “L-shape” bracket insert with a curved surface installed on the bottom holder. By controlling the positions of the array of L-shape brackets and holders, a wide range of reconfigurable tooling curvatures can be achieved for customized 3D forming. Using rectangular, hollow AA6082 profiles in T4 temper, the new tooling strategy has been verified, demonstrating its good capability for producing different part configurations. Looking forward, this reconfigurable tooling concept has demonstrated great potential in terms of dimensional accuracy and springback compensation in stretch forming, enabling customized, cost-effective, and rapid development of aluminum extrusion components.