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1. Rapid high-resolution volumetric imaging via laser ablation delayering and confocal imaging

   https://doi.org/10.1038/s41598-022-16519-2  

   Phoulady, Adrian; May, Nicholas; Choi, Hongbin; Suleiman, Yara; Shahbazmohamadi, Sina; Tavousi, Pouya (December 2022, Scientific Reports)

   Abstract Acquiring detailed 3D images of samples is needed for conducting thorough investigations in a wide range of applications. Doing so using nondestructive methods such as X-ray computed tomography (X-ray CT) has resolution limitations. Destructive methods, which work based on consecutive delayering and imaging of the sample, face a tradeoff between throughput and resolution. Using focused ion beam (FIB) for delayering, although high precision, is low throughput. On the other hand, mechanical methods that can offer fast delayering, are low precision and may put the sample integrity at risk. Herein, we propose to use femtosecond laser ablation as a delayering method in combination with optical and confocal microscopy as the imaging technique for performing rapid 3D imaging. The use of confocal microscopy provides several advantages. First, it eliminates the 3D image distortion resulting from non-flat layers, caused by the difference in laser ablation rate of different materials. It further allows layer height variations to be maintained within a small range. Finally, it enables material characterization based on the processing of material ablation rate at different locations. The proposed method is applied on a printed circuit board (PCB), and the results are validated and compared with the X-ray CT image of the PCB part. 

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2. Analyzing the evolution of tool wear area in trochoidal milling of Inconel 718 using image processing methodology

   https://doi.org/10.1016/j.mfglet.2022.08.002  

   Agarwal A; Potthoff N; Shah A; Mears L; Wiederkehr P (January 2023, Manufacturing Letters (special collection Proceedings of SME North American Manufacturing Research Conference))

   Nickel-based superalloys belong to a category of material employed for extreme conditions and exhibit high strength properties at elevated temperatures that result in poor machinability. Machining such di cult-to-cut materials like Inconel 718 leads to a high rate of tool wear, and therefore trochoidal milling toolpath is used to improve productivity and tool life. The current study analyzes the evolution of the flank wear area of the tool during trochoidal milling of Inconel 718 using an image processing methodology. It is attempted by performing experimental studies until tool failure occurs at several cutting conditions. The machining is performed through several iterations on an identical cutting path, and the number of iterations to failure is recorded. The microstructural image of a flank wear area is captured upon each iteration and processed using an image processing algorithm. It is realized that the evaluation of flank wear area can be an e ective parameter to analyze tool wear. Also, the image processing methodology works e ectively and can be extended during real-time machining. 

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3. Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data

   https://doi.org/10.3390/machines12120923  

   Alberts, Matthew; St_John, Sam; Odie, Simon; Khojandi, Anahita; Jared, Bradley; Schmitz, Tony; Karandikar, Jaydeep; Coble, Jamie B (December 2024, Machines)

   Chatter, a self-excited vibration phenomenon, is a critical challenge in high-speed machining operations, affecting tool life, product surface quality, and overall process efficiency. While machine learning models trained on simulated data have shown promise in detecting chatter, their real-world applicability remains uncertain due to discrepancies between simulated and actual machining environments. The primary goal of this study is to bridge the gap between simulation-based machine learning models and real-world applications by developing and validating a Random Forest-based chatter detection system. This research focuses on improving manufacturing efficiency through reliable chatter detection by integrating Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL). The study applies a Random Forest classification model trained on over 140,000 simulated machining datasets, incorporating techniques like Operational Modal Analysis (OMA), Receptance Coupling Substructure Analysis (RCSA), and Transfer Learning (TL) to adapt the model for real-world operational data. The model is validated against 1600 real-world machining datasets, achieving an accuracy of 86.1%, with strong precision and recall scores. The results demonstrate the model’s robustness and potential for practical implementation in industrial settings, highlighting challenges such as sensor noise and variability in machining conditions. This work advances the use of predictive analytics in machining processes, offering a data-driven solution to improve manufacturing efficiency through more reliable chatter detection. 

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4. Modeling for Chemical-Etching Enhanced Pulsed Laser Ablation

   https://doi.org/10.1115/MSEC2019-2844  

   Zhang, Xing; Mao, Bo; Histed, Rebecca; Liao, Yiliang (November 2019, Proceedings of the ASME 2019 14th International Manufacturing Science and Engineering Conference)

   Pulsed laser ablation (PLA) under active liquid confinement, also known as chemical etching enhanced pulsed laser ablation (CE-PLA), has emerged as a novel laser processing methodology, which breaks the current major limitation in underwater PLA caused by the breakdown plasma and effectively improves the efficiencies of underwater PLA-based processes, such as laser-assisted nano-/micro-machining and laser shock processing. Despite of experimental efforts, little attention has been paid on CE-PLA process modeling. In this study, an extended two-temperature model is proposed to predict the temporal/spatial evolution of the electron-lattice temperature and the ablation rate in the CE-PLA process. The model is developed with considerations on the temperature-dependent electronic thermal properties and optical properties of the target material. The ablation rate is formulated by incorporating the mutual promotion between ablation and etching processes. The simulation results are validated by the experimental data of CE-PLA of zinc under the liquid confinement of hydrogen peroxide. 

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5. A Novel Laser-Aided Machining and Polishing Process for Additive Manufacturing Materials with Multiple Endmill Emulating Scan Patterns

   https://doi.org/10.3390/app11209428  

   Parvez, Mohammad Masud; Patel, Sahil; Isanaka, Sriram Praneeth; Liou, Frank (October 2021, Applied Sciences)

   In additive manufacturing (AM), the surface roughness of the deposited parts remains significantly higher than the admissible range for most applications. Additionally, the surface topography of AM parts exhibits waviness profiles between tracks and layers. Therefore, post-processing is indispensable to improve surface quality. Laser-aided machining and polishing can be effective surface improvement processes that can be used due to their availability as the primary energy sources in many metal AM processes. While the initial roughness and waviness of the surface of most AM parts are very high, to achieve dimensional accuracy and minimize roughness, a high input energy density is required during machining and polishing processes although such high energy density may induce process defects and escalate the phenomenon of wavelength asperities. In this paper, we propose a systematic approach to eliminate waviness and reduce surface roughness with the combination of laser-aided machining, macro-polishing, and micro-polishing processes. While machining reduces the initial waviness, low energy density during polishing can minimize this further. The average roughness (Ra=1.11μm) achieved in this study with optimized process parameters for both machining and polishing demonstrates a greater than 97% reduction in roughness when compared to the as-built part. 

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