Monitoring cutting tool operation and condition with a magnetoelastic rate of change of torque sensor

Garshelis, Ivan J.; Kari, Ryan J.; Tollens, Stijn P. L.; Cuseo, James M.

doi:10.1063/1.2833316

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Kaya et al [ 16 ] proposed an online TCM system based on the cutting force and torque signals, and a high correlation and low error ratio between the actual and predicted values of flank wear were obtained. A torque sensor was used to monitor the condition of milling cutters [ 17 ], and the efficiency and quality of the product were improved obviously.…”

Section: Literature Reviewmentioning

confidence: 99%

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

Zhang

Yao

Zhang

et al. 2016

Sensors

127

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Tool breakage causes losses of surface polishing and dimensional accuracy for machined part, or possible damage to a workpiece or machine. Tool Condition Monitoring (TCM) is considerably vital in the manufacturing industry. In this paper, an indirect TCM approach is introduced with a wireless triaxial accelerometer. The vibrations in the three vertical directions (x, y and z) are acquired during milling operations, and the raw signals are de-noised by wavelet analysis. These features of de-noised signals are extracted in the time, frequency and time–frequency domains. The key features are selected based on Pearson’s Correlation Coefficient (PCC). The Neuro-Fuzzy Network (NFN) is adopted to predict the tool wear and Remaining Useful Life (RUL). In comparison with Back Propagation Neural Network (BPNN) and Radial Basis Function Network (RBFN), the results show that the NFN has the best performance in the prediction of tool wear and RUL.

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Section: Literature Reviewmentioning

confidence: 99%

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

Zhang

Yao

Zhang

et al. 2016

Sensors

127

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“…The proposed system was shown to achieve a high correlation rate and low error ratio between the actual and predicted values of the ank wear in the machining of Inconel 718. Garshelis et al [7] developed a method for monitoring the cutting tool condition and operating parameters in a general machining process through an inspection of the magnetoelastic rate of change of a torque sensor signal. Ahmad et al [8] used a three-component piezoelectric transducer to examine the effects of the machining parameters (i.e., the cutting speed and feed rate) and workpiece shape on the cutting performance of a band sawing machine with a variable pitch combination blade.…”

Section: Introductionmentioning

confidence: 99%

A Robust Embedded Load Cell Sensor for Tool Life Prognosis and Smart Sawing of Medium Carbon Steel

Jeng

Tsai²,

Tseng³

2022

Preprint

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An embedded load cell sensor is proposed for the tool life prognosis and thrust force control of a band saw machine. The sensor enables the tool life and surface quality of the machined workpiece to be effectively improved through the use of a single sensing device strategically located in the cutting machine. The feasibility of the proposed sensor is demonstrated experimentally using a double-column horizontal sawing machine with medium carbon steel bars as the workpiece material. An investigation is performed into the effects of the cutting force, feed rate, and machining time on the tool wear and surface roughness of the machined workpiece. It is shown that the thrust force, tool wear and surface roughness of the machined workpiece are strongly correlated and increase over time. Based on the experimental results, a feedback control system is proposed for maintaining a constant thrust force on the band saw during cutting under even the most challenging conditions. Overall, the results confirm that the embedded load cell sensor and force monitoring and control methodology proposed in the present study provide an effective approach for maintaining the optimal cutting conditions in the sawing of medium carbon steel and improving the tool life and machined part quality.

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Development of friction stir welding technologies for in-space manufacturing

Longhurst

Cox

Gibson

et al. 2016

Int J Adv Manuf Technol

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Monitoring cutting tool operation and condition with a magnetoelastic rate of change of torque sensor

Cited by 4 publications

References 7 publications

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

Tool Condition Monitoring and Remaining Useful Life Prognostic Based on a Wireless Sensor in Dry Milling Operations

A Robust Embedded Load Cell Sensor for Tool Life Prognosis and Smart Sawing of Medium Carbon Steel

Development of friction stir welding technologies for in-space manufacturing

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