Review on sensor design for cutting force measurement

Mohanraj, T.; Uddin, Md. Sharif; Thangarasu, S. K.

doi:10.1177/09544089221106264

Cited by 9 publications

(3 citation statements)

References 74 publications

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“…The static cutting force can be measured with strain gaugebased dynamometers [45][46][47]. Strain gauge technology-based torque sensors provide distortionless torque measurements in rotating spindles over a predetermined bandwidth, usually from DC to a few kHz [9].…”

Section: Sensor Technologiesmentioning

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: Sensor Technologiesmentioning

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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“…The waviness causes a variable load to be experienced by the next cutting edge. After this, the unknown factors disturb the vibration already in place, worsening it [ 9 ]. Excessive vibration may be generated during the machining process.…”

Section: Introductionmentioning

confidence: 99%

Development of Deep Belief Network for Tool Faults Recognition

Kale

Wahul

Patange³

et al. 2023

Sensors

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The controlled interaction of work material and cutting tool is responsible for the precise outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and depth of cut causes a disturbance to the machining. This leads to the deterioration of a cutting edge and unfinished work material. Recognition and description of tool failure are essential and must be addressed using intelligent techniques. Deep learning is an efficient method that assists in dealing with a large amount of dynamic data. The manufacturing industry generates momentous information every day and has enormous scope for data analysis. Most intelligent systems have been applied toward the prediction of tool conditions; however, they must be explored for descriptive analytics for on-board pattern recognition. In an attempt to recognize the variation in milling operation leading to tool faults, the development of a Deep Belief Network (DBN) is presented. The network intends to classify in total six tool conditions (one healthy and five faulty) through image-based vibration signals acquired in real time. The model was designed, trained, tested, and validated through datasets collected considering diverse input parameters.

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“…The vapor deposition of thin film sensors on the flat and curved surface was performed to detect flank wear land width [ 24 ]. A strain gauge sensor was integrated into the tool to measure forces during machining operations [ 25 ]. An MXene thin film sensor was used to monitor the health condition of the repaired composite structure [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Online Tool Wear-Out Detection System Using Silver–Polyester Thick Film Sensor for Low-Duty Cycle Machining Operations

Jegadeeshwaran

Jakkamputi

Mohanraj

et al. 2022

Sensors

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This paper deals with the design and development of a silver–polyester thick film sensor and associated system for the wear-out detection of single-point cutting tools for low-duty cycle machining operations. Conventional means of wear-out detection use dynamometers, accelerometers, microphones, acoustic emission sensors, thermal infrared cameras, and machine vision systems that detect tool wear during the process. Direct measurements with optical instruments are accurate but affect the machining process. In this study, the use of a thick film sensor to detect wear-out for aa real-time low-duty machining operation was proposed to eliminate the limitations of the current methods. The proposed sensor monitors the tool condition accurately as the wear acts directly on the sensor, which makes the system simple and more reliable. The effect of tool temperature on the sensor during the machining operation was also studied to determine the displacement/deformation of tracing and the polymer substrate at different service temperatures. The proposed tool wear detection system with the silver–polyester thick film sensor mounted directly on the cutting tool tip proved to be highly capable of detecting the tool wear with good reliability.

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Review on sensor design for cutting force measurement

Cited by 9 publications

References 74 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

Development of Deep Belief Network for Tool Faults Recognition

Development of Online Tool Wear-Out Detection System Using Silver–Polyester Thick Film Sensor for Low-Duty Cycle Machining Operations

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