Tool condition monitoring in interrupted cutting with acceleration sensors

Ratava, Juho; Lohtander, Mika; Varis, Juha

doi:10.1016/j.rcim.2016.11.008

Cited by 48 publications

(20 citation statements)

References 10 publications

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“…The experimental procedure includes the placement of the accelerometer to the extruder and the overall experimental setup that was used for nozzle condition monitoring using the vibration sensor [46]. The accelerometer installation on Direct and the Bowden extruder are shown in Figure 8 and Figure 9.…”

Section: Methodsmentioning

confidence: 99%

Nozzle condition monitoring in 3D printing

Tlegenov

Hong

2018

Robotics and Computer-Integrated Manufacturing

138

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Section: Methodsmentioning

confidence: 99%

Nozzle condition monitoring in 3D printing

Tlegenov

Hong

2018

Robotics and Computer-Integrated Manufacturing

138

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show abstract

“…While tool wear frequency fluctuates in different stages of wear, AE signal amplitude increases with the increased tool wear and depth of cut. Based on the previous literature [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], it can be concluded that none of the previous studies utilized various sensors.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

Kuntoğlu

Aslan

Sağlam

et al. 2020

Sensors

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Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

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“…All the signs are able to be utilized for forecasting thoroughly the tool failure achieved. Apart from that, downtime which affected by the failure of the tool could be diminished by monitoring system even though foreseeing it is particularly troublesome in interfered cutting because of greatly changeable stress causing risen tool wear [10]. Therefore, with an efficient monitoring system, it can help to avoid the damage to the machine apparatus, downtime and rejected components [7] [8].…”

Section: Introductionmentioning

confidence: 99%

In-situ worn geometry effect over the surface roughness propagation during micro milling process

Mohadzir¹,

Rosli²,

Jamaludin

et al. 2020

JMMST

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High-precision miniaturized components for micro-machining operations has an increasingly demand for numerous developing sectors such as medical instrumentations, electronics components, computer manufacturing, aerospace and automotive engineering. Micro-milling has known as a flexible micro machining process and the most familiar micro mechanical machining method. Due to overcome a few difficulties in micro fabrication, micro milling is picked as an alternative way as it has potential and imperative for high accuracy machining. However, micro tools have low tool life as it is unpredictably and wear quickly. Furthermore, it also has tendency to break easily due to its micro size dimension. The study observe the behaviour of micro milling worn geometry during machining and includes a non-conventional method to measure surface roughness resulted by micro milling process in machining of mild steel AISI1045. The workpiece is prepared by using CNC milling machine with facing and slotting process. Then, the mild steel AISI1045 will undergo a machining process by a 1 mm size end mill diameter with different set of parameters which are spindle speed, feed rate, radial depth and axial depth. Lastly, for the results, the surface roughness of the machined surface will be observed and the condition of tool and the measurement of wear for the tool will be investigated.

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Tool condition monitoring in interrupted cutting with acceleration sensors

Cited by 48 publications

References 10 publications

Nozzle condition monitoring in 3D printing

Nozzle condition monitoring in 3D printing

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140

In-situ worn geometry effect over the surface roughness propagation during micro milling process

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