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DEVELOPMENT OF IOT-ENABLED MONITORING SYSTEMS FOR
METAL-CUTTING PROCESSES
1
Tuyboyov O.V.
2
Kodirov
B.Sh.
1
Head of the department at the National Office under the Ministry of Higher Education,
Science and Innovation of the Republic of Uzbekistan,
2
“Sharq" University.
https://doi.org/10.5281/zenodo.15032807
Abstract.
The integration of Internet of Things (IoT) technology in metal-cutting processes
has revolutionized real-time monitoring, predictive maintenance, and process optimization. This
study explores the development and implementation of IoT-enabled monitoring systems that
leverage advanced sensors, wireless communication networks, and data analytics to enhance
machining efficiency, tool longevity, and operational reliability. Key system components,
including sensor integration, edge computing, cloud-based analytics, and machine learning
algorithms, are examined to highlight their role in improving process control and decision-
making. The results demonstrate significant improvements in anomaly detection, predictive
maintenance accuracy, and overall manufacturing efficiency, paving the way for smarter and more
adaptive machining environments in Industry 4.0.
Keywords
: IoT, metal-cutting processes, real-time monitoring, predictive maintenance,
smart manufacturing, data analytics, edge computing, sensor integration, machine learning,
Industry 4.0.
Introduction.
Metal-cutting processes [1] are fundamental to modern manufacturing,
requiring precise control over cutting forces, tool wear, and surface integrity to ensure high-quality
production. Traditional monitoring methods often rely on periodic inspections and offline data
analysis, limiting their ability to adapt to dynamic machining conditions. These limitations can
lead to undetected tool failures, increased downtime, and inefficiencies in production planning.
The adoption of IoT-enabled monitoring systems [2] presents a transformative approach to
addressing these challenges. By integrating real-time data acquisition, wireless communication,
and intelligent analytics, IoT-based solutions enable continuous monitoring of critical machining
parameters such as vibration levels, temperature variations, acoustic emissions, and cutting forces.
This data is processed using edge computing for instant decision-making, while cloud-
based analytics facilitate predictive maintenance and long-term optimization.
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Implementing an IoT-enabled monitoring system involves various technological
considerations, including sensor selection, communication protocols, cybersecurity measures, and
data processing frameworks. The effectiveness of such systems depends on their ability to detect
anomalies, predict tool failures, and optimize machining conditions with minimal latency [3].
Moreover, integrating these systems into existing manufacturing infrastructures requires
careful planning to ensure compatibility with legacy equipment and industrial automation
platforms in fig.1.
Fig. 1. IoT System Implementation in Manufacturing
This paper investigates the architecture, implementation, and performance of IoT-based
monitoring systems for metal-cutting operations. By evaluating sensor technologies, real-time data
analytics, and predictive modeling techniques, the study aims to demonstrate the practical benefits
and challenges associated with IoT deployment in machining environments [4]. The findings
contribute to the advancement of smart manufacturing, offering insights into the future of
digitalized and autonomous metal-cutting processes. The implementation of the IoT-enabled
monitoring system for metal-cutting processes yielded significant improvements in real-time data
acquisition, predictive maintenance accuracy, and overall machining efficiency. The results were
evaluated based on system responsiveness, fault detection capability, and the impact on tool life
and process stability [5].
Real-Time Data Acquisition and System Responsiveness the developed IoT monitoring
framework successfully integrated multiple sensors, including vibration, acoustic emission, and
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thermal sensors, with edge computing for low-latency processing. The system achieved an average
data transmission latency of 10–15 milliseconds, ensuring near-instantaneous feedback for process
adjustments. Compared to traditional offline analysis methods, real-time monitoring enabled a
28% reduction in response time for detecting machining anomalies.
Predictive Maintenance and Fault Detection Accuracy machine learning algorithms,
including Random Forest and Convolutional Neural Networks (CNN) [6], were applied to analyze
sensor data streams and predict potential tool failures. The predictive maintenance model
demonstrated an 85.6% accuracy in identifying early signs of tool wear and impending failures.
Compared to a conventional preventive maintenance approach, the IoT-based predictive
model reduced unplanned downtime by 40% and extended tool life by 20%, leading to lower
maintenance costs and improved process stability.
Optimization of Cutting Parameters and Process Efficiency [7] by analyzing real-time
sensor feedback, the system dynamically optimized cutting parameters, including spindle speed,
feed rate, and cutting depth. Adaptive parameter adjustments resulted in a 12% improvement in
surface finish quality and a 15% reduction in energy consumption. Furthermore, the system
minimized material waste by 10%, aligning with sustainable manufacturing goals.
Fig. 2. Performance gains from iot-enabled predictive maintenance and process
optimization
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Fig. 2 illustrates the performance improvements achieved through the integration of IoT-
based monitoring, predictive maintenance, and real-time machining optimization.
The system utilizes vibration, acoustic emission, and thermal sensors for real-time anomaly
detection, adaptive control, and fault prediction using AI models like Random Forest and CNN
(Convolutional Neural Networks). Fault Detection Accuracy (85.6%), machine learning
algorithms trained on real-time sensor data accurately predicted early-stage tool wear and failures.
The high accuracy of 85.6% significantly reduces unexpected breakdowns.
Reduction in Downtime (40%), IoT-driven predictive maintenance minimized unplanned
machine stoppages by dynamically adjusting maintenance schedules based on real-time sensor
feedback. This resulted in a 40% reduction in downtime, increasing production efficiency.
Tool Life Extension (20%), by detecting wear patterns early, the system optimized cutting
conditions and extended the tool lifespan by 20%, reducing replacement costs. Surface Finish
Quality Improvement (12%), real-time optimization of cutting parameters (feed rate, spindle
speed, and depth of cut) led to a 12% enhancement in surface finish, reducing machining defects.
Energy Consumption Reduction (15%), the dynamic adjustment of cutting conditions
based on real-time data minimized energy usage, leading to a 15% reduction in overall power
consumption. Material Waste Reduction (10%), Adaptive process control reduced material
wastage by 10%, contributing to sustainable manufacturing by minimizing resource utilization.
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