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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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This content will become publicly available on December 1, 2025
Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data
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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- Award ID(s):
- 2133630
- PAR ID:
- 10582208
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Machines
- Volume:
- 12
- Issue:
- 12
- ISSN:
- 2075-1702
- Page Range / eLocation ID:
- 923
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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