Advanced Monitoring of Tool Wear and Cutting States in CNC Turning Process by Utilizing Sensor Fusion

Tangjitsitcharoen, Somkiat; Rungruang, Channarong; Pongsathornwiwat, Narongsak

doi:10.4028/www.scientific.net/amr.189-193.377

Cited by 13 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A radial basis function was used by Manan to train the network which consists of features extracted from sound signal and surface roughness [10].This classification model is able to recognise tool wear states which classify tool states (defined as 'sharp', 'semi-dull' and 'dull'), and the proposed system can be utilised to effectively monitor the condition of the cutting tool. Tangjitsitcharoen et al used a sensor fusion-based approach to monitor tool wear using sound, force, vibration and AE signals and found that the energy spectrum density of sound and AE signals are sensible to tool wear [17].…”

Section: Introductionmentioning

confidence: 99%

Using spindle noise to monitor tool wear in a turning process

Seemuang

McLeay

Slatter

2016

Int J Adv Manuf Technol

View full text Add to dashboard Cite

A tool condition monitoring system can increase the competitiveness of a machining process by increasing the utilised tool life and decreasing instances of part damage from excessive tool wear or tool breakage. This article describes an inexpensive and non-intrusive method of inferring tool condition by measuring the audible sound emitted during machining. The audio signature recorded can be used to develop an effective in-process tool wear monitoring system where digital filters retain the signal associated with the cutting process but remove audio characteristics associated with the operation of the machine spindle. This study used a microphone to record the machining sound of EN24 steel being face turned by a CNC lathe in a wet cutting condition using constant surface speed control. The audio signal is compared to the flank wear development on the cutting inserts for several different surface speed and cutting feed combinations. The results show that there is no relationship between the frequency of spindle noise and tool wear, but varying cutting speed and feed rate have an influence on the magnitude of spindle noise and this could be used to indicate the tool wear state during the process.

show abstract

Section: Introductionmentioning

confidence: 99%

Using spindle noise to monitor tool wear in a turning process

Seemuang

McLeay

Slatter

2016

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…During the manufacturing operations, several parameters influenced and could be employed for monitoring the state of the tool or metal removal process [79]. Among those parameters measurement of cutting forces [80,81], acoustic emission signal [82,83], ultrasonic signal [84], currents of servo drivers [84] and the combination of them [86] have been used more frequently.…”

Section: Monitoring the Performance Of Machining Operationsmentioning

confidence: 99%

Development of New Structural Health Monitoring Techniques

Fekrmandi¹

View full text Add to dashboard Cite

During the past two decades, many researchers have developed methods for the detection of structural defects at the early stages to operate the aerospace vehicles safely and to reduce the operating costs. The Surface Response to Excitation (SuRE) method is one of these approaches developed at FIU to reduce the cost and size of the equipment.The SuRE method excites the surface at a series of frequencies and monitors the propagation characteristics of the generated waves. The amplitude of the waves reaching to any point on the surface varies with frequency; however, it remains consistent as long as the integrity and strain distribution on the part is consistent. These spectral characteristics change when cracks develop or the strain distribution changes. The SHM methods may be used for many applications, from the detection of loose screws to the monitoring of manufacturing operations.A scanning laser vibrometer was used in this study to investigate the characteristics of the spectral changes at different points on the parts. The study started with detecting a load on a plate and estimating its location. The modifications on the part with manufacturing operations were detected and the Part-Based Manufacturing Process vii Performance Monitoring (PbPPM) method was developed. Hardware was prepared to demonstrate the feasibility of the proposed methods in real time.Using low-cost piezoelectric elements and the non-contact scanning laser vibrometer successfully, the data was collected for the SuRE and PbPPM methods.Locational force, loose bolts and material loss could be easily detected by comparing the spectral characteristics of the arriving waves. On-line methods used fast computational methods for estimating the spectrum and detecting the changing operational conditions from sum of the squares of the variations. Neural networks classified the spectrums when the desktop -DSP combination was used. The results demonstrated the feasibility of the SuRE and PbPPM methods.

show abstract