Analysis and prediction of cutting force through lathe tool dynamometer in CNC turning process

Projoth, T. Niruban; Poures, Melvin Victor De; Nanthakumar, P.

doi:10.1016/j.matpr.2021.02.681

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…Identified the complexity of a rapidly evolving, dynamic industrial setting, machine learning (ML), as component of artificial intelligence (AI), has the ability to adapt to alterations, reducing the necessity for system designer to anticipate and respond to every possible scenario. As a consequence of its capacity to automatically learn from and adapt to changing surroundings, as well as its ability to cope with adaptation, ML offers a convincing argument for why it should be utilised in the manufacturing industry [11].…”

Section: Section 3: Machine Learning Approachmentioning

confidence: 99%

A Deep Study on Machine Learning Techniques for Tool Condition Monitoring in Turning of Titanium-based Superalloys.

Jakati,

Koti,

S. Kataraki

et al. 2023

jmmf

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The current state-of-the-art review on tool condition monitoring for turning of titanium-based superalloys is presented in this paper. Titanium (Ti) superalloys are widely utilised in aerospace industry, automobile industry, petrochemical applications. Ti superalloys are also used in fabrication of biomedical components due to their outstanding combination of mechanical properties and strong corrosion resistance at extreme temperatures. But these superalloys are difficult-to-cut because to their low heat conductivity, low elastic modulus, high strength, and strong chemical resistance. Literature review highlights the drastic reduction in tool life of titanium superalloys at highspeed and feed rates throughout the machining process. The review paper focuses on (i) various reasons to deploy tool condition monitoring; and (ii) study of tool condition monitoring methods based on machine learning techniques to identify the ideal parameters for the prevention of catastrophic tool failure.

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Section: Section 3: Machine Learning Approachmentioning

confidence: 99%

A Deep Study on Machine Learning Techniques for Tool Condition Monitoring in Turning of Titanium-based Superalloys.

Jakati,

Koti,

S. Kataraki

et al. 2023

jmmf

View full text Add to dashboard Cite

show abstract

“…Resistance strain load cell is a kind of mechanical measurement sensor which is wildly used in various field such as industrial robotics [1,2], agriculture [3][4][5], medicine [6][7][8][9], sports industry [10,11], and many high-precision weighing applications [12][13][14][15][16][17]. It is a sensitive component that converts the deformation of the strain gage under force into an electrical signal [18].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Return-to-Zero Error after the First Load of Load Cell

Zhuang,

Yang,

Cheng

et al. 2023

Sensors

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The return-to-zero error of the resistance strain load cell is most obvious in the first zero-return process during loading and unloading. To improve the accuracy of the load cell, it is necessary to figure out the cause of the error. The influence of the temperature, material, and weld cup were analyzed in this paper. It was concluded that the hysteresis is the main factor affecting the return-to-zero error after the first load. The relationship between hysteresis and zero-return error after first load was obtained by a data fitting algorithm. A method to improve the return-to-zero error after the first load was proposed.

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“…They have a range of industrial uses in addition to scientific and technological research and development [ 2 ]. Load cells have been providing quality measurements in robotics and automation [ 3 , 4 , 5 ], agriculture [ 6 , 7 ], medicine [ 8 , 9 , 10 ], industrial weighing [ 11 , 12 ], and many other applications for decades [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. The load cell can also be used to gauge the interference force in wearable robotic applications (such as exoskeletons and prostheses) or to detect the comparison force as the robot walks to provide information to the stable-controlling system [ 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Shape Optimization of Strain Gauge Load Cell for Axial Force Measurement for Test Benches

Al-Dahiree

Tokhi

Hadi

et al. 2022

Sensors

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The load cell is an indispensable component of many engineering machinery and industrial automation for measuring and sensing force and torque. This paper describes the design and analysis of the strain gauge load cell, from the conceptional design stage to shape optimization (based on the finite element method (FEM) technique) and calibration, providing ample load capacity with low-cost material (aluminum 6061) and highly accurate force measurement. The amplifier circuit of the half Wheatstone bridge configuration with two strain gauges was implemented experimentally with an actual load cell prototype. The calibration test was conducted to evaluate the load cell characteristics and derive the governing equation for sensing the unknown load depending on the measured output voltage. The measured sensitivity of the load cell is approximately 15 mV/N and 446.8 µV/V at a maximum applied load of 30 kg. The findings are supported by FEM results and experiments with an acceptable percentage of errors, which revealed an overall error of 6% in the worst situation. Therefore, the proposed load cell meets the design considerations for axial force measurement for the laboratory test bench, which has a light weight of 20 g and a maximum axial force capacity of 300 N with good sensor characteristics.

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Analysis and prediction of cutting force through lathe tool dynamometer in CNC turning process

Cited by 8 publications

References 23 publications

A Deep Study on Machine Learning Techniques for Tool Condition Monitoring in Turning of Titanium-based Superalloys.

A Deep Study on Machine Learning Techniques for Tool Condition Monitoring in Turning of Titanium-based Superalloys.

Analysis of Return-to-Zero Error after the First Load of Load Cell

Design and Shape Optimization of Strain Gauge Load Cell for Axial Force Measurement for Test Benches

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