An object-oriented architecture for sensorless cutting force feedback for CNC milling process monitoring and control

Loenzo, Roberto Augusto Gómez; Lumbreras, Pedro Daniel Alaniz; Romero-Troncoso, René de Jesús; Ruíz, Gilberto Herrera

doi:10.1016/j.advengsoft.2009.12.016

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…Arsitektur yang akan digunakan dalam proses kendali ini mengutamakan tiga proses utama yaitu samppling, filtering, dan processing data untuk menghasilkan sinyal kendali [21]. Susunan perintah akan dibuat dalam bentuk teks dalam bahasa G-Code yang disesuaikan dengan perangkat lunak yang dipergunakan.…”

Section: B Pembuatan Program Cncunclassified

The Use of Open Source Software for Open Architecture System on CNC Milling Machine

Subagio

Atmaja

2012

J. Mechatron. Electr. Power Veh. Technol.

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AbstrakSistem kerja mesin computer numerical control (CNC) milling tidak dapat terlepas dari perangkat lunak sebagai penerjemah bahasa operator ke bahasa mesin. Perangkat lunak ini dituntut untuk mengikuti ketentuan open architecture yang antara lain mempunyai kapabilitas portability, extendability, interoperability, dan scalability. Proses manufaktur modern terkadang menggunakan mesin CNC milling untuk waktu yang lama bahkan melebihi waktu kerja yang ditentukan oleh produsen. Ketika mesin sudah melewati masa yang ditentukan dan produsen memutuskan discontinue, maka pengguna akan menemui masalah untuk mempertahankan kinerja mesin jika diharuskan tetap tergantung pada perangkat lunak bawaan dari produsen. Tulisan ini bertujuan menunjukkan bahwa penggunaan open source software (OSS) adalah jalan keluar untuk mempertahankan kinerja mesin. Dengan penggunaan OSS, pengguna tidak lagi tergantung pada perangkat lunak dari produsen karena OSS bersifat terbuka dan dapat dikembangkan secara mandiri. Dalam tulisan ini dipergunakan USBCNC V.3.42 sebagai alternatif OSS pada mesin CNC milling. OSS ini menunjukkan hasil uji coba benda kerja yang sesuai dengan pola yang diinginkan. Hasil uji coba menunjukkan kinerja mesin dengan OSS serupa dengan kinerja mesin ketika menggunakan perangkat lunak dari produsen.

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Section: B Pembuatan Program Cncunclassified

The Use of Open Source Software for Open Architecture System on CNC Milling Machine

Subagio

Atmaja

2012

J. Mechatron. Electr. Power Veh. Technol.

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“…The virtual sensors developed in this work consist of a system for the data acquisition of internal CNC signals, a module for signal processing and an intelligent decision-making scheme. The approaches that can be found in the literature for this task are mainly spectral analysis [ 12 – 14 ], wavelet transforms [ 15 , 16 ], fuzzy logic [ 17 – 19 ], neural networks [ 20 – 22 ], time domain processing [ 14 , 23 , 24 ] and hybrid systems [ 25 , 26 ]. In this paper, the time domain processing approach is considered, where a segmentation of the electrical power consumption takes place before a Bayesian network (BN) analysis is done to identify faults.…”

Section: Improved Diagnosis Of Faults In Multitooth Tool Machining: Amentioning

confidence: 99%

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Bustillo

Correa

Reñones

2011

Sensors

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The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.

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“…Wang et al [10] leveraged the clustering energy of the AE burst signal as a means to decide the wear state of the tool flank. Loenzo et al [11] comprehensively investigated cutting tool lifecycle from initial use to failure, subsequently discovering the capacity of cumulative cutting power to accurately pinpoint the tool's fractured condition. Despite the capability of these indirect measurement technologies to monitor tool wear state, practical application is significantly curtailed by the intricate and personalized sensor installation and fixation procedures, coupled with their substantial cost.…”

Section: Introductionmentioning

confidence: 99%

Identification of Tool Wear Based on Infographics and a Double-Attention Network

Ni,

Liu,

Meng

et al. 2023

Machines

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Tool wear is a crucial factor in machining as it directly impacts surface quality and indirectly decreases machining efficiency, which leads to significant economic losses. Hence, monitoring tool wear state is of the utmost importance for achieving high performance and efficient machining. Although monitoring tool wear state using a single sensor has been validated in laboratory settings, it has certain drawbacks such as limited feature information acquisition and inability to learn important features adaptively. These limitations pose challenges to quickly extending the monitoring function of tool wear state of the machine tools. To solve these problems, this paper proposes a double-attention deep learning network based on vibroacoustic signal fusion feature infographics. The first solution is the construction of novel infographics using tool-intrinsic characteristics and multi-domain fusion features of multi-sensor inputs, which includes correlation analysis, principal component analysis, and feature fusion. The second solution is to build a novel deep network with a double-attention module and a spatial pyramid pooling module which can accurately and quickly identify tool wear state by successfully extracting critical spatial data from the infographics at various scales. The validity of the network is examined through an interpretability analysis based on the class activation graph. In terms of the tool wear status recognition task, the F1 score of the double-attention model based on an information graph is 11.61% higher than Resnet18, and peak recognition accuracy reaches 97.98%.

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An object-oriented architecture for sensorless cutting force feedback for CNC milling process monitoring and control

Cited by 7 publications

References 13 publications

The Use of Open Source Software for Open Architecture System on CNC Milling Machine

The Use of Open Source Software for Open Architecture System on CNC Milling Machine

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Identification of Tool Wear Based on Infographics and a Double-Attention Network

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