Sensor Fusion for Condition Monitoring System of End Milling Operations

Abbas, Jabbar; Al‐Habaibeh, Amin; Su, D

doi:10.4028/www.scientific.net/kem.450.267

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Subsequently, sensors are positioned correctly to capture precise data. Sensor fusion technologies are employed in numerous studies to obtain the most accurate results from the trained algorithm [31]. The signal generated by the sensors is captured and conditioned using data acquisition systems.…”

Section: Advanced Tcm Techniquesmentioning

confidence: 99%

Review of advances in tool condition monitoring techniques in the milling process

E S,

Madheswaran

et al. 2024

Meas. Sci. Technol.

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Milling is an extremely adaptable process that can be utilized to fabricate a wide range of shapes and intricate 3D geometries. The versatility of the milling process renders it useful for the production of a diverse range of components and products in several industries, including aerospace, automotive, electronics, and medical equipment. Monitoring tool conditions is essential for maintaining product quality, minimizing production downtime, and maximizing tool life. Advances in this field have been driven by the need for increased productivity, reduced tool wear, and improved process efficiency. Tool condition monitoring (TCM) in the milling process is a critical aspect of machining operations. TCM involves assessing the health and performance of cutting tools used in milling machines. As technology evolves, staying updated with the latest developments in this field is essential for manufacturers seeking to optimize their milling operations. However, addressing the challenges associated with sensor integration, data analysis, and cost-effectiveness remains crucial. To fill this research gap, this paper provides an overview of the extensive literature on monitoring milling tool conditions. It summarizes the key focus areas, including tool wear sensors and the application of various machine learning and deep learning algorithms. It also discusses the potential applications of tool condition monitoring beyond wear detection, such as predicting tool breakage, tool wear, the cutting tool's remaining lifetime, and the challenges faced by TCMs. This review also provides suggestions for potential future research endeavors and is anticipated to offer valuable insights for the development of advanced TCMs in terms of tool wear monitoring and predicting remaining useful life.

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Section: Advanced Tcm Techniquesmentioning

confidence: 99%

Review of advances in tool condition monitoring techniques in the milling process

E S,

Madheswaran

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Later other novel accomplishments have been executed using ASPS approach undertaken by [47][48][49] to efficiently determine the key features as well as the sensor in terms of condition monitoring of gearbox, tool and water leakage detection system respectively. The rationales for using ASPS here are: I.…”

Section: Related Workmentioning

confidence: 99%

A novel approach for communicating with patients suffering from completely locked-in-syndrome (CLIS) via thoughts: Brain computer interface system using EEG signals and artificial intelligence

Majumdar

Al‐Habaibeh

Omurtag

et al. 2023

Neuroscience Informatics

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“…Moreover, [26] describes the use of ultrasonics to monitor tool wear during the turning process, but there has been no report about such a use in milling. The AE signals with cutting force, current, vibration [27] and laser techniques [28] give valuable information about the cutting process.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the condition of the cutting tool using self-powering wireless sensor technologies

Ostaševičius

Jurenas

Augutis

et al. 2016

Int J Adv Manuf Technol

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An effective system for monitoring the wear of the machine tool inserts could significantly contribute to saving costs in manufacturing. One of the most recent and popular of effective monitoring methods revolves around the use of sensing technologies for indirect estimation of tool wear. The sensory information is difficult to collect from machine tools due to the extremely poor signal-to-noise ratio of the relevant tool wear-related information because the milling operation is of interrupted nature since the work piece is in contact with the tool edge several times per second. Another issue is the varying thickness of the chip during the penetration of the work piece. Yet, many challenges still impede the practical application of this method for industrial environment. Therefore, the paper presents a method that could solve these challenges especially in industrial environment. The proposed selfpowered wireless sensor node is integrated in the structure of the cutting tool. The voltage generated from the cutting tool vibrations of the harvester exponentially rises when the capacitor is fully charged and wireless signal sent to the receiver. As the intensity of energy accumulation depends on the state of the cutting tool wear, it indicates and detects the tool condition. The proposed technique could be useful for the identification of the cutting tool quality and the relative tool-work piece position. The information about the variation of tool wear is beneficial for helping the manufacturers to control the cutting process, to minimize the product cost as well as to improve the machining quality and efficiency.

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Sensor Fusion for Condition Monitoring System of End Milling Operations

Cited by 5 publications

References 8 publications

Review of advances in tool condition monitoring techniques in the milling process

Review of advances in tool condition monitoring techniques in the milling process

A novel approach for communicating with patients suffering from completely locked-in-syndrome (CLIS) via thoughts: Brain computer interface system using EEG signals and artificial intelligence

Monitoring the condition of the cutting tool using self-powering wireless sensor technologies

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