Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning

Kuntoğlu, Mustafa; Sağlam, Hacı

doi:10.1016/j.measurement.2020.108582

Cited by 133 publications

(74 citation statements)

References 82 publications

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“…In the analysis of surface roughness, the cutting speed and the feed rate parameters, which are frequently used in the literature [21], were examined. In addition to these parameters, the parameters of spheronization time and spheronization temperature were also used in the analysis.…”

Section: Methodsmentioning

confidence: 99%

Investigation and optimization of surface roughness with experimental design methods by turning of AISI-1050 after spheroidization heat treatment

2020

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In this study, surface roughness values that were obtained by turning a medium-carbon steel (AISI 1050) applied to spheroidization heat treatment were modeled with two different optimization methods. The test materials were subjected to spheroidization heat treatment for three different periods as 15, 60, and 180 min. Three different cutting speeds and three different feed rates were used for the turning operations. Surface roughness values obtained from the surface of the workpieces after turning were analyzed by the response surface method (RSM) and Taguchi method (TM). These analyses show that the surface roughness changes directly depending on the feed rate and other parameters have a limited effect on the surface roughness. The regression analysis revealed that in both methods, the R 2 values were higher than 0.99, and it showed the effectiveness of these two methods. As a result, these two different experimental design methods showed high stability in solving this engineering problem. Additionally, in the comparison of the methods with each other, the ratio of similarity to the experimental results was evaluated by considering the percentage. The ratio of similarity was obtained by over 90% in both methods. When the ratio of similarity was analyzed, RSM had more effective results than TM.

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Section: Methodsmentioning

confidence: 99%

Investigation and optimization of surface roughness with experimental design methods by turning of AISI-1050 after spheroidization heat treatment

2020

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“…El Hakim et al [39] showed that in hard turning of high alloy steel with secondary hardening operation, CF and FW represented increasing trend simultaneously. Kuntoğlu and Sağlam [4] In references [4,5,[35][36][37][38][39][40][41][42][43][44][45][46], a general opinion about the CFs is the increase in time with TW, shows sudden increase before tool breakage and decrease after tool breakage. Moreover, it can be stated that there is a consensus for dynamometer; this sensor is necessary for monitoring tool condition safely, reliably and accurately providing online information about tool life, TW condition and possible tool breakage.…”

Section: Cutting Forcesmentioning

confidence: 99%

“…Minor and major changes during the cutting operation can be precisely sensed via AE sensor when it is positioned in the right way. This brings a clear advantage References [4,5,43,[47][48][49][50][51][52][53] indicated that AE sensor signals have great importance on detecting tool FW and possible tool breakage with the help of various signal features identified theoretically. Being a non-destructive method and easily implemented structure for monitoring area, having various signal features for characterization of different phenomena, namely, plastic deformation, TW, chip production and tool breakage, AE sensors are heavily preferred in TCMS during turning operation.…”

Section: Acoustic Emissionmentioning

confidence: 99%

“…Prasad and Babu [59] tried to correlate vibration and TW in turning of AISI 4140 steel, and the authors indicated that there was a close relationship between TW and vibration. In the references [5,33,36,40,43,50,54,55,[57][58][59]111], it was stated that there was a correlation between FW and vibration signals; however, the reflection capability of vibration signals might not be the solution on its own in the TCMS.…”

Section: Vibrationmentioning

confidence: 99%

“…The membership functions of inputs and outputs, type of fuzzy and decision methods are demonstrated in Figure 13. In [5], in addition to cutting parameters, AE and tool tip tem- In the particular example of this paper, FW prediction via artificial neural network in turning operations using different types of materials has been an attractive issue in the past. In references [35,[82][83][84][85][86], artificial-neural-network-based TW prediction was successfully carried out and highly recommended.…”

Section: Fuzzy Logicmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

Kuntoğlu

Aslan

Pimenov

et al. 2020

Sensors

Self Cite

190

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The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

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Mathematical Foundations of Machining System Monitoring

Zhu

2021

Springer Series in Advanced Manufacturing

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Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning

Cited by 133 publications

References 82 publications

Investigation and optimization of surface roughness with experimental design methods by turning of AISI-1050 after spheroidization heat treatment

Investigation and optimization of surface roughness with experimental design methods by turning of AISI-1050 after spheroidization heat treatment

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

Mathematical Foundations of Machining System Monitoring

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