In-Process Detection of Tool Failure in Milling Using Cutting Force Models

Altintaş, Yusuf; Yellowley, I.

doi:10.1115/1.3188744

Cited by 151 publications

(61 citation statements)

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“…Although the indirect methods, particularly those based on acoustic emission [6,5,25,23], are quite promising, more research is needed before they can be widely implemented. The cutting-forcebased methods have, on the other hand, more solid theoretical background, and therefore they are more attractive for modeling the metal-cutting processes [3,19,20,7,22,27,17]. It is interesting to note that a combined approach, incorporating an 'intelligent sensor', becomes a new paradigm in metal-cutting-process control [26,31].…”

Section: Metal-cutting-process Space and Sensorsmentioning

confidence: 99%

“…The measurements are taken at times of the tool rotation incremented by one degree When all cutting edges of the tool are sharp, the cutting force is periodic, with approximately the same frequency. If, however, one of the teeth is worn, then the force amplitude for this tooth, and perhaps for the following teeth, is disturbed [3]. The following force model was used for the case when the first tooth is worn:…”

Section: Ntmentioning

confidence: 99%

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Learning to monitor a machine tool

Kokar

Letkowski

Callahan³

1995

J Intell Robot Syst

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Abstract. This paper deals with the issue of automatic learning and recognition of various conditions of a machine tool. The ultimate goal of the research discussed in this paper is to develop a comparehensive monitor and control (M&C) system that can substitute for the expert machinist and perform certain critical in-process tasks to assure quality production. The M&C system must reliably recognize and respond to qualitatively different behaviours of the machine tool, learn new behaviors, respond faster than its human counterpart to quality threatening circumstances, and interface with an existing controller. The research considers a series of face-milling anomalies that were subsequently simulated and used as a first step towards establishing the feasibility of employing machine learning as an integral component of the intelligent controller. We address the question of feasibility in two steps. First, it is important to know if the process models (dull tool, broken tool, etc.) can be learned (model learning). And second, if the models are learned, can an algorithm reliably select an appropriate model (distinguish between dull and broken tools) based on input from the model learner and from the sensors (model selection). The results of the simulation-based tests demonstrate that the milling-process anomalies can be learned, and the appropriate model can be reliably selected. Such a model can be subsequently utilized to make compensating in-process machine-tool adjustments. In addition, we observed that the learning curve need not approach the 100% level to be functional.

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Section: Metal-cutting-process Space and Sensorsmentioning

confidence: 99%

Section: Ntmentioning

confidence: 99%

Learning to monitor a machine tool

Kokar

Letkowski

Callahan³

1995

J Intell Robot Syst

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“…For example, Altintas [2] has shown that the first-order autoregressive time series model AR1 can be used to distinguish the force signal during normal flank wear to that when tool failure occurs. Elbestawi et al [3] found that certain harmonics of the cutting force increase significantly with flank wear, the number of such sensitive harmonics being related to the number of inserts of the milling cutter and the immersion rate.…”

Section: Kious Is With Semiconductor and Functional Materials Labomentioning

confidence: 99%

“…In most approaches, proposed for the tool wear monitoring area, several parameters can be measured, such as forces, vibrations and acoustic emission, which are directly correlated with tool wear [3]- [7]. Furthermore, these parameters are measured on-line during the machining process.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Kious

Aissa

Ameur

et al. 2014

AMM

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Abstract-The wear of cutting tool degrades the quality of the product in the manufacturing processes. The on line monitoring of the cutting tool wear level is very necessary to prevent the deterioration of the quality of machining. Unfortunately there is not a direct manner to measure the cutting tool wear on line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission etc…. In this work, a neural network system is elaborated in order to estimate the flank wear from the cutting force measurement and the cutting conditions.

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“…A frequent approach used in laboratories is to attach vibration, acoustic emission and dynamometer sensors to the machine and then monitor the signals obtained. The measurement of cutting force is commonly taken using a table-mounted dynamometer, which is an essential tool for laboratory based experimental work [4][5][6]. Flank wear and tooth breakage can be monitored from acceleration and vibration signals during milling [7,8].…”

Section: Introductionmentioning

confidence: 99%

Integration of Pre-Simulation and Sensorless Monitoring for Smart Mould Machining

Kim¹

2016

Int. j. simul. model.

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A smart mould machining system, which detects machining status and controls the CNC through presimulation and real-time data, was developed. Pre-simulation predicts cutting forces, and inserts the best feed rate and virtual load on each line of the NC data. The active feed rate reduced machining by up to 36 %, without increasing the maximum cutting forces. The actual cutting load was computed from spindle load data and a friction load compensation algorithm. Collision and tool wear were detected by comparing the actual and virtual load, with the time synchronised by using tool position data. The system machined an automotive grill mould cavity for 34 hours without the supervision of a worker, because pre-simulation had stabilized the milling process and monitoring would have stopped the process if the actual load was different to the virtual load. Pre-simulation has been verified by thousands of mould makers and integrated with sensorless monitoring in an open CNC.

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In-Process Detection of Tool Failure in Milling Using Cutting Force Models

Cited by 151 publications

References 0 publications

Learning to monitor a machine tool

Learning to monitor a machine tool

Neural Network Monitoring Strategy of Cutting Tool Wear of Horizontal High Speed Milling

Integration of Pre-Simulation and Sensorless Monitoring for Smart Mould Machining

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