Relevant bibliographies by topics / Smart CNC milling

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  4. Conference papers

Academic literature on the topic 'Smart CNC milling'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Smart CNC milling"

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Ceclan, Vasile Adrian, Ioan Alexandru Popan, Sorin Dumitru Grozav, Cristina Ștefana Miron-Borzan, and Ivan Kuric. "The Analyses of Working Parameters for a 3D Complex Part Manufacturing by CNC Machine." Applied Mechanics and Materials 808 (November 2015): 286–91. http://dx.doi.org/10.4028/www.scientific.net/amm.808.286.

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In this paper I want to presents the process for manufacturing one complex parts made by aluminum alloy. For manufacturing this complex part I used CAD/CAM software, CNC milling machine and same special tools. Starting from the 3D model made in SolidWorks was manufactured this complex part, using new strategies for CNC milling. To be made this chain of pieces it is necessary to use smart software for this process.
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Myrsini, Ntemi, Paraschos Spyridon, Karakostas Anastasios, Gialampoukidis Ilias, Vrochidis Stefanos, and Kompatsiaris Ioannis. "Infrastructure monitoring and quality diagnosis in CNC machining: A review." CIRP Journal of Manufacturing Science and Technology Volume 38 (August 1, 2022): 631–49. https://doi.org/10.1016/j.cirpj.2022.06.001.

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Infrastructure monitoring and rapid quality diagnosis comprise the key solutions to achieve zero-defect smart manufacturing. The most fundamental systems in manufacturing industries are computer numerical controlled (CNC) tools. Automating and optimizing their functionality is a highly challenging task because complex dynamics and non-linear relationships govern the overall machining operations. Recent scientific advances in machining processes, incorporate intelligence in CNC tools to improve both the reliability and the productivity of the real-time cutting operations, while reducing waste and cost. This study extensively reviews these advances focusing on three fundamental aspects: Surface roughness prediction, tool wear prediction, and chatter detection in CNC cutting processes.
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Sudianto, Agus, Zamberi Jamaludin, Azrul Azwan Abdul Rahman, Sentot Novianto, and Fajar Muharrom. "Automatic Temperature Measurement and Monitoring System for Milling Process of AA6041 Aluminum Aloy using MLX90614 Infrared Thermometer Sensor with Arduino." Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 82, no. 2 (2021): 1–14. http://dx.doi.org/10.37934/arfmts.82.2.114.

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Manufacturing process of metal part requires real-time temperature monitoring capability to ensure high surface integrity is upheld throughout the machining process. A smart temperature measurement and monitoring system for manufacturing process of metal parts is necessary to meet quality and productivity requirements. A smart temperature measurement can be applied in machining processes of conventional, non-conventional and computer numerical control (CNC) machines. Currently, an infrared fusion based thermometer Fluke Ti400 was employed for temperature measurement in a machining process. However, measured temperature in the form of data list with adjustable time range setting is not automatically linked to the computer for continuous monitoring and data analysis purposes. For this reason, a smart temperature measurement system was developed for a CNC milling operation on aluminum alloy (AA6041) using a MLX90614 infrared thermometer sensor operated by Arduino. The system enables data linkages with the computer because MLX90614 is compatible and linked to Microsoft Exel via the Arduino. This paper presents a work-study on the performance of this Arduino based temperature measurement system for dry milling process application. Here, the Arduino based temperature measurement system captured the workpiece temperature during machining of Aluminum Alloy (AA6041) and data were compared with the Fluke Ti400 infrared thermometer. Measurement results from both devices showed similar accuracy level with a deviation of ± 2 oC. Hence, a smart temperature measurement system was succeesfully developed expanding the scopes of current system setup.
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Park, Hong-seok, Bowen Qi, Duck-Viet Dang, and Dae Yu Park. "Development of smart machining system for optimizing feedrates to minimize machining time." Journal of Computational Design and Engineering 5, no. 3 (2017): 299–304. http://dx.doi.org/10.1016/j.jcde.2017.12.004.

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Abstract Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. Highlights The smart machining system was implemented in the CNC machine. Optimal feed rates enhance machine tool efficiency. The smart machining system is reliable to reduce machine time.
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LIANG, Yuming. "Curriculum reform and innovation for CNC lathe and milling processing certificate under the "1+X" certification system." Region - Educational Research and Reviews 5, no. 5 (2023): 206. http://dx.doi.org/10.32629/rerr.v5i5.1475.

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With the development of smart manufacturing, CNC lathe and milling processing technology play an increasingly vital role in modern manufacturing. The "1+X" certification system offers a new pathway for vocational education to cultivate highly skilled professionals. This paper focuses on the curriculum system of the CNC lathe and milling processing certificate and explores strategies for curriculum reform and innovation under the guidance of the "1+X" certification system. By examining the existing curriculum system and analyzing the discrepancies between industry demands and the current teaching situation, specific reform measures are proposed. Additionally, in line with the concept of industry-education integration, innovative approaches to curriculum content and teaching methods are explored, as well as ways to leverage information technology for improving teaching efficiency and quality. The research shows that by optimizing curriculum structure, updating teaching content, improving teaching methods, and enhancing practical components, it is possible to significantly enhance students' skills and innovation capabilities, better meeting the needs of enterprises in the context of Industry 4.0.
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Huang, Yi-Cheng, and Ching-Chen Hou. "Using Feature Engineering and Principal Component Analysis for Monitoring Spindle Speed Change Based on Kullback–Leibler Divergence with a Gaussian Mixture Model." Sensors 23, no. 13 (2023): 6174. http://dx.doi.org/10.3390/s23136174.

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Machining is a crucial constituent of the manufacturing industry, which has begun to transition from precision machinery to smart machinery. Particularly, the introduction of artificial intelligence into computer numerically controlled (CNC) machine tools will enable machine tools to self-diagnose during operation, improving the quality of finished products. In this study, feature engineering and principal component analysis were combined with the online and real-time Gaussian mixture model (GMM) based on the Kullback–Leibler divergence’s measure to achieve the real-time monitoring of changes in manufacturing parameters. Based on the attached accelerometer device’s vibration signals and current sensing of the spindle, the developed GMM unsupervised learning was successfully used to diagnose the spindle speed changes of a CNC machine tool during milling. The F1-scores with improved experimental results for X, Y, and Z axes were 0.95, 0.88, and 0.93, respectively. The established FE-PCA-GMM/KLD method can be applied to issue warnings when it predicts a change in the manufacturing process parameter. A smart sensing device for diagnosing the machining status can be fabricated for implementation. The effectiveness of the developed method for determining the manufacturing parameter changes was successfully verified by experiments.
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Atishey, Mittal*, Kumar Atul, and Daniel Freedon. "OPTIMIZATION OF CUTTING PARAMETER FOR EFFICIENT ENERGY IN CNC MILLING MACHINE- A REVIEW." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 3 (2016): 384–89. https://doi.org/10.5281/zenodo.47506.

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Energy efficiency is one of the main drivers for achieving sustainable manufacturing. Advances in machine tool design have reduced the energy consumption of such equipment, but still machine tools remain one of the most energy demanding equipment in a workshop. This study presents a novel approach aimed to improve the energy efficiency of machine tools through the online optimization of cutting conditions. The study is based on an industrial CNC controller with smart algorithms optimizing the cutting parameters to reduce the overall machining time while at the same time minimizing the peak energy consumption. In the current trends of optimizing machining process parameters, various evolutionary or meta- heuristic techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining from 2007 to 2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances.  
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Han, Zhenyu, Hongyu Jin, Dedong Han, and Hongya Fu. "ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system." International Journal of Advanced Manufacturing Technology 89, no. 9-12 (2016): 2731–46. http://dx.doi.org/10.1007/s00170-016-9863-y.

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Tnani, Mohamed-Ali, Michael Feil, and Klaus Diepold. "Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring." Procedia CIRP 107 (2022): 131–36. http://dx.doi.org/10.1016/j.procir.2022.04.022.

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Kim, Hyungjung, Woo-Kyun Jung, In-Gyu Choi, and Sung-Hoon Ahn. "A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)." Sensors 19, no. 20 (2019): 4506. http://dx.doi.org/10.3390/s19204506.

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In the new era of manufacturing with the Fourth Industrial Revolution, the smart factory is getting much attention as a solution for the factory of the future. Despite challenges in small and medium-sized enterprises (SMEs), such as short-term strategies and labor-intensive with limited resources, they have to improve productivity and stay competitive by adopting smart factory technologies. This study presents a novel monitoring approach for SMEs, KEM (keep an eye on your machine), and using a low-cost vision, such as a webcam and open-source technologies. Mainly, this idea focuses on collecting and processing operational data using cheaper and easy-to-use components. A prototype was tested with the typical 3-axis computer numerical control (CNC) milling machine. From the evaluation, availability of using a low-cost webcam and open-source technologies for monitoring of machine tools was confirmed. The results revealed that the proposed system is easy to integrate and can be conveniently applied to legacy machine tools on the shop floor without a significant change of equipment and cost barrier, which is less than $500 USD. These benefits could lead to a change of monitoring operations to reduce time in operation, energy consumption, and environmental impact for the sustainable production of SMEs.
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Books on the topic "Smart CNC milling"

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Roberts, Tony, and Admire Mare, eds. Digital Surveillance in Africa. Bloomsbury Publishing Plc, 2025. https://doi.org/10.5040/9781350422117.

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Media coverage and scholarly research on digital surveillance has focused primarily on the USA and Europe. Everyone knows about Cambridge Analytica’s social media surveillance; Edward Snowden’s revelations of the West’s mass internet and phone surveillance; and Pegasus Spyware’s mobile phone surveillance of activists, journalists, judges, and presidents across the world. Comparatively little is known about the millions of dollars now being spent on digital technologies for use in the illegal and illegitimate surveillance of citizens in Africa. In this open-access third volume of Bloomsbury’sDigital Africaseries, a broad range of African and European scholars and practitioners map the development, procurement and (mis)use of the ever-expanding suite of digital surveillance and policing technologies across the continent. Drawing on the empirically rich, theoretically sophisticated research of the African Digital Rights Network, this book examines how public and private actors in Africa use spyware, mobile phone extraction, biometric and face recognition systems, and other technologies for smart-city and other social, and social-control, applications. Eight chapters examine eight African countries, and each of these begins with a thorough political history of the nature of surveillance there under colonial and post-liberation political settlements. This enables new analyses of the socio-cultural, political, and economic drivers and characteristics of contemporary digital surveillance in each country, all of which ultimately leads to concrete policy recommendations at local, national, and international levels. For its empirical richness and breadth, as well as its theoretical sophistication,Digital Surveillance in Africais essential reading for anyone interested in contemporary African studies, and it is of keen interest to anyone concerned with how digital surveillance affects everyday lives across the world. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.
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Book chapters on the topic "Smart CNC milling"

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Wang, Xiaogang, Lin Zhu, and Chen Yi-Chang. "Prediction and Analysis of Milling Cutter Wear Degree Based on CNN Model." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8764-7_11.

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Rathee, Lokesh, Rakesh Rathee, and Ajay Kumar. "Experimental Study of Impact Parameters on the Deforming Loads in Incremental Forming." In Modeling, Characterization, and Processing of Smart Materials. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9224-6.ch006.

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Single point incremental forming (SPIF) is an emerging sheet forming method and has been widely accepted by the researchers for fabricating the customized items for the industrial uses and the end users. The measurement of the deforming loads during this viable method would assess the need of specific capacity of the SPIF machines for the given process conditions and materials. Therefore, this work aims at investing the influence of the punch feed rate and the forming angle on the peak values of the deforming loads. The conical frustums of the varied forming angles are produced from the AA2024 aluminum alloy sheets. The CNC milling machine was utilized for this die-less sheet forming. The table type dynamometer was utilized for recording the peak values of the deforming loads during the fabrication of the frustums. Experimental results confirmed that both the input factors were significant to impact the deforming loads. It was also confirmed that the small increment in the forming angle led to the significant rise in the deforming loads for all punch feed rates.
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Biswas, Neepa, Debarpita Santra, Bannishikha Banerjee, and Sudarsan Biswas. "Harnessing the Power of Machine Learning for Parkinson's Disease Detection." In AIoT and Smart Sensing Technologies for Smart Devices. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0786-1.ch008.

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Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide. Early detection of PD is crucial for effective treatment and management of the disease. Deep learning (DL) and machine learning (ML) have emerged as promising approaches for detecting PD. In this study, a comparative performance analysis is done for DL and ML applications based on speech signals. DL methods using convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and ML methods employing random forest and the XGBoost model were trained and assessed. Performance of the models are evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1-score. Results showed that the XGBoost model outperformed the DL models in terms of accuracy and F1 score, while the CNN and LSTM models achieved higher precision and recall. These findings suggest that XGBoost can be a useful tool for detecting PD based on speech signals, particularly in scenarios where interpretability and computational efficiency are important.
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Dharmireddy, Ajay Kumar, Kambham Jacob Silva Lorraine, Ravi Kumar Maddumala, and Kotha Lavanya. "Pesticide Prediction and Disease Identification with AIoT." In The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815274349124010007.

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Agriculture is vital to human survival and has a significant impact on the economy of any nation. Crop protection costs millions of dollars per year. Insects and other pests pose a serious threat to the health of a harvest. Excessive use of chemical fertilizers and pesticides negatively affects the crop and soil quality. Therefore, one way to safeguard the harvest and mitigate potential losses is through early identification of the pests. Examining the crop at the right moment is the best technique to determine its overall health. While manual inspection is the standard way of conducting field inspection, it becomes challenging for large fields. In addition, manual inspection would be exceedingly expensive and tedious. To address this, an automated system is needed to detect pests, identify them, and recommend appropriate fertilizers using an IoT system. Therefore, automated pest detection has become a major focus for researchers globally, as it offers a more efficient and cost-effective alternative to manual inspection. In this work, a smart agriculture system has been proposed that monitors crops, identifies pests, and allows remote control. The dataset comprises over 4000 images of corn leaves, categorized into rust, blight, grey spots, and healthy leaves. By employing Convolutional Neural Networks (CNN), the system has achieved a remarkable 99% accuracy in pest detection.
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Conference papers on the topic "Smart CNC milling"

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Choudhary, Rohit, Sambhav, Sunny David Titus, P. Akshaya, Jose Alex Mathew, and N. Balaji. "CNC PCB milling and wood engraving machine." In 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon). IEEE, 2017. http://dx.doi.org/10.1109/smarttechcon.2017.8358577.

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Jin, Hongyu, Zhenyu Han, and Zhongxi Shao. "On-line chatter recognition and supression in milling based on smart CNC." In 2016 International Symposium on Flexible Automation (ISFA). IEEE, 2016. http://dx.doi.org/10.1109/isfa.2016.7790173.

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Harmon, Andrew, Barry K. Fussell, and Robert B. Jerard. "Calibration and Characterization of a Low-Cost Wireless Sensor for Applications in CNC End Milling." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7386.

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This paper describes recent research progress at the University of New Hampshire in the area of smart machining systems. Central to creating a smart machining system is the challenge of collecting detailed information about the milling process at the tool tip. This paper discusses the design, static calibration, dynamic characterization, and implementation of a low-cost wireless force sensor for end-milling. The sensor is observed to accurately measure force when most of the cutting power is band-limited below the sensor’s natural frequency. Sensor geometry constrains the milling application to a single tooth cutter; while this constraint is impractical for industrial applications, our sensor is shown to provide useful information in a laboratory setting.
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Hidayatullah, Muhammad, Farkhad Ihsan Hariadi, and Arif Sasongko. "Development of interface and coordination module of FPGA-based controller for CNC PCB milling and drilling machine." In 2017 International Symposium on Electronics and Smart Devices (ISESD). IEEE, 2017. http://dx.doi.org/10.1109/isesd.2017.8253315.

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Patel, Deep, Chayan Maiti, and Sreekumar Muthuswamy. "Real-Time Performance Monitoring of a CNC Milling Machine using ROS 2 and AWS IoT Towards Industry 4.0." In IEEE EUROCON 2023 - 20th International Conference on Smart Technologies. IEEE, 2023. http://dx.doi.org/10.1109/eurocon56442.2023.10199020.

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Nopriandri, Farkhad Ihsan Hariadi, and Arif Sasongko. "Development of FPGA-based module of three-phase spindle motor speed-controller for CNC PCB milling and drilling machine." In 2017 International Symposium on Electronics and Smart Devices (ISESD). IEEE, 2017. http://dx.doi.org/10.1109/isesd.2017.8253308.

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Chuang, Ho-Yu, and Jen-Yuan (James) Chang. "3D Surface Scanning for Smart Repair Manufacturing Application." In ASME 2020 29th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/isps2020-1910.

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Abstract At present, automatic machines have gradually replaced human labor for improving both the stability and effectiveness of manufacturing operations. Five-axis CNC machine tools are commonly used for traditional surface milling machining. However, it is still necessary to manually reposition the work-piece for the CNC machine tools that enviably lowers position accuracy. As such, it is essential to develop an integrated fusion system that can scan any objects and conduct repair operations automatically. The primary purpose of this research is to integrate the stereo vision system into an existing industrial manipulator. With the proposed method and system, the position error can be reduced by avoiding moving the work-pieces during operations. Also, reconstructing the scan results from multiple views avoids the common blocking problem in vision. Finally, the distance of the manipulator path is optimized by the Hill Climbing algorithm, allowing to create a trajectory of the manipulator end-effector to reprocess the work-piece. In the future, this system can be implemented for automated optical inspection (AOI), such as pipeline maintenance, turbine engine maintenance, and automatic classification of defects.
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Madrin, Febby Purnama, Farkhad Ihsan Hariadi, and Arif Sasongko. "Design and implementation of FPGA-based control for linear and circular motion interpolator of PCB CNC-milling and drilling machine." In 2017 International Symposium on Electronics and Smart Devices (ISESD). IEEE, 2017. http://dx.doi.org/10.1109/isesd.2017.8253316.

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Koh, Min Hyong, Mehdi Nouri, and Barry K. Fussell. "Estimation of Milling Forces From Compliant Sensors Using a Harmonic Force Model and Kalman Filter." In ASME 2013 International Manufacturing Science and Engineering Conference collocated with the 41st North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/msec2013-1196.

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Force transducers such as the piezoelectric Kistler dynamometer and the wireless strain gage Smart Tool are used to monitor the cutting force during CNC machining. Due to sensor dynamics, there are differences between the actual cutting force and the measured force, characterized by phase delay and additional vibrations in the cutting profile. In this work, actual cutting forces are estimated from the measured sensor data using a Kalman filter. Since cutting forces are composed of harmonics of the tooth passing frequency and runout frequency, both a sensor dynamic model and harmonic cutting force model are included in the Kalman filter model in order to improve performance of the filter. The harmonic computations of the measured cutting force are used to self-tune the Kalman filter. Comparisons between the measured and estimated force show the ability of the Kalman filter to reduce sensor vibration and noise with no phase delay.
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Hoang, Danny, Hanning Chen, Mohsen Imani, Ruimin Chen, and Farhad Imani. "Brief Paper: Multi-Task Brain-Inspired Learning for Interlinking Machining Dynamics With Parts Geometrical Deviations." In ASME 2024 19th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/msec2024-125435.

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Abstract Increasing complexity, and requirements for the precise creation of parts, necessitate the use of computer numerical control (CNC) manufacturing. This process involves programmed instructions to remove material from a workpiece through operations such as milling, turning, and drilling. This manufacturing technique incorporates various process parameters (e.g., tools, spindle speed, feed rate, cut depth), leading to a highly complex operation. Additionally, interacting phenomena between the workpiece, tools, and environmental conditions further add to complexity which can lead to defects and poor product quality. Two main areas are of focus for an efficient automated system: monitoring and swift quality assessment. Within these areas, the critical aspects ascertaining the quality of a CNC manufacturing operation are: 1) Tool wear: the inherent deterioration of machine components caused by prolonged utilization, 2) Chatter: vibration that occurs during the machining process, and 3) Surface finish: the final product’s surface roughness. Many research domains tend to focus on just one of these areas while neglecting the interconnected influences of all three. Therefore, to capture a more holistic and comprehensive assessment of a manufacturing process, the overall product quality should be considered, as that’s what ultimately counts. The integration of CNC systems with in-situ monitoring devices such as acoustic sensors, high-speed cameras, and thermal cameras is aimed at understanding the underlying physical aspects of the CNC machining process, including tool wear, chatter, and surface roughness. The incorporation of these monitoring devices has allowed the use of artificial intelligence and machine learning (ML) in smart CNC systems with hopes of increasing productivity, minimizing downtime, and ensuring product quality. By capturing the underlying phenomena that occur during the manufacturing process, users hope to understand the interlinking dynamics for zero-defect automated manufacturing. However, even though the use of ML methods has yielded noteworthy results in analyzing in-situ process data for CNC manufacturing, the black-box nature of these models and their tendency to focus predominantly on single-task objectives rather than multi-task scenarios pose challenges. In real-world part creation and manufacturing scenarios, there is often a need to address multiple interconnected tasks simultaneously which demands models that can multitask effectively. Yet, many ML models designed and trained for singular objectives are limited in their applicability and efficiency in more complex multi-faceted environments. Addressing these challenges, we introduce MTaskHD, a novel multi-task framework, that leverages hyperdimensional computing (HDC) to effortlessly fuse data from various channels and process signals while characterizing quality within a multi-task manufacturing operation. Moreover, it yields interpretable outcomes, allowing users to understand the process behind predictions. In a real-world experiment conducted on a hybrid 5-axis CNC Deckel-Maho-Gildemeister, MTaskHD was implemented to forecast the quality of three distinct features: left 25.4 mm counterbore diameter, right 25.4 mm counterbore diameter, and 2.54 mm milled radius. Demonstrating remarkable performance, the model excelled in predicting the quality levels of all three features in its multi-task configuration with an F1-Score of 95.3%, outperforming alternative machine learning approaches, including support vector machines, Naïve Bayes, multi-layer perceptron, convolutional neural network, and time-LeNet. The inherent multi-task capability, robustness, and interpretability of HDC collectively offer a solution for comprehending intricate manufacturing dynamics and operations.
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