A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

Silva, Rodrigo Henriques Lopes da; Silva, Márcio Bacci da; Hassui, Amauri

doi:10.1080/10910344.2016.1191026

Cited by 45 publications

(19 citation statements)

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“…Bhattacharyya and Sengupta (2009) used a combination of signal processing techniques to obtain improved and robust estimates of tool wear. da Silva et al (2016) demonstrated the use of a probabilistic neural network in monitoring tool wear in the end-milling operation via acoustic emission and cutting power signals. Pimenov (2015) suggested a mathematical model of main drive power that can be used for controlling face milling conditions in the process of cutting tool wear.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N , in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power-that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements. Keywords

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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“…The effective sound pressure in mPa (milli-pascals) can be calculated using Equation (2). doing any machining operation on the work-piece.…”

Section: Methodsmentioning

confidence: 99%

“…In the past two decades, tool condition monitoring (TCM) has been explored and improved for better machining. It is noted that TCM can be sensed by process variables such as higher cutting forces, higher vibrations, higher temperature, acoustic emission, higher noise, and surface roughness quality [2,3]. These variables are sensed by starting conditions of tool quality and the properties of the material been machined [2].…”

Section: Introductionmentioning

confidence: 99%

“…It was found that the process parameters need major tuning for precise detection of tool wear and the estimation of tool life [7]. For effective TCM the chosen parameters are very important and an incorrect choice could lead to a poor response [2]. It was emphasized that a single sensor would not be able to provide a reliable result [2,6,8].…”

Section: Introductionmentioning

confidence: 99%

“…For effective TCM the chosen parameters are very important and an incorrect choice could lead to a poor response [2]. It was emphasized that a single sensor would not be able to provide a reliable result [2,6,8]. During machining, if the tool is progressing towards sudden wear, this results…”

Section: Introductionmentioning

confidence: 99%

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Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

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Abstract:Machining is one of the major manufacturing techniques where the material is removed to prepare the complete or sub-part. In general, this is also referred to as subtractive manufacturing. Due to solid-to-solid contact between the cutting tool and the work-piece, the machine dynamics get influenced by various operating parameters. This generates force and vibration, and thus noise. Over time the cutting tool reaches its end-of-life which increases the force to cut, and thus produces more vibration and noise. The noise parameter was considered in this work. A 32-element spherical microphone array acoustic camera system was used to record and analyze the sound that was emitted during the machining processes. The startup, idle, and load operating characteristics for various industrial machining equipment were monitored with the acoustic beam former microphone system. The industrial applications included a bench grinder, surface grinder, vertical band saw, lathe machine, and vertical milling machine. Analysis of the acoustic noise generated from these processes could demonstrate the similarities between the cyclical patterns of resonating sound.

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Intelligent Modelling of Hard Materials Machining

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A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

Cited by 45 publications

References 45 publications

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Understanding Acoustic Emission for Different Metal Cutting Machinery and Operations

Intelligent Modelling of Hard Materials Machining

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