Wire EDM Monitoring for Zero-defect Manufacturing based on Advanced Sensor Signal Processing

Caggiano, Alessandra; Teti, Roberto; Pérez, Rebeca; Xirouchakis, Paul

doi:10.1016/j.procir.2015.06.065

Cited by 35 publications

(9 citation statements)

References 15 publications

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“…Dena den, gero eta ohikoagoa da, horretarako, esperimentuen diseinua erabiltzea [8][9][10][11]. Bigarrenari dagokionez, deskargen karakterizazioa eta pieza-altueraren detekzioa dira landutako gaietako batzuk [12][13].…”

Section: Artearen Egoeraunclassified

“…Bigarren kasu horretan sortzen dira arazo gehien, haria apurtzeko arriskua baitago. Hain zuzen ere, kasu horietan aplikatu dira arazo hori konpontzen saiatzeko sare neuronalak erabiltzen dituzten lanak [11] edo denbora errealean kontrolatzeko edo monitorizatzeko teknikak [12][13], parametroak une oro ebakitzen den altuerara egokitzeko. Hala eta guztiz ere, kontrol eta egokitzapen horiek behar bezala egiteko sistema bat lortzeko, oinarrizkoa daaldagaien azterketa.…”

Section: Artearen Egoeraunclassified

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Harizko elektrohigadura bidezko ebaketa-prozesuko aldagaien azterketa altuera aldakorreko piezak ebakitzeko

Conde

Sánchez²,

Plaza

et al. 2019

EKAIA

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Harizko elektrohigadura bidezko ebaketa-prozesua (WEDM) ezinbestekoa da gaur egun, industriak gainazal-akaberen eta dimentsio-perdoien inguruan eskatzen dituen fabrikazio-eskakizun zorrotzak betetzeko. Prozesuari buruzko ezagutzarekin batera softwarearen garapena integratzen duen ingurune batetik abiatuta, makina adimendunaren funtzionamenduaren helburua lortu ahal izango da; hots, makinak bakarrik, fidagarritasunez eta doitasunik galdu gabe funtzionatzea. Baina, horretarako, arazo larrien konponketak optimizatu behar dira; esate baterako, altuera aldakorreko piezak ebakitzeko arazoa. Lan honetan, altuera aldatzen den guneetan ebaketa-prozesuaren aldagairik esanguratsuenen portaera aztertzeko aukera ematen duen industria-sistema bat garatu da. Hau da, aurrea hartu ahal izango zaie arrazoi horrengatik gerta daitezkeen aldaketei, eta, horri esker, haria apurtzea edo prozesuaren harroketa-tasa txikitzea eragin dezaketen ezegonkortasun-arazoak saihestuko dira; hau da, produktibitateak behera egitea eragotz daiteke.

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Section: Artearen Egoeraunclassified

Harizko elektrohigadura bidezko ebaketa-prozesuko aldagaien azterketa altuera aldakorreko piezak ebakitzeko

Conde

Sánchez²,

Plaza

et al. 2019

EKAIA

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“…To find out the correlation between processing parameters and inappropriate processing conditions, the model was established with eight highly correlated parameters and monitored with a sampling interval of 32 milliseconds. Caggianoa et al [7] used a sensor to collect voltage and current signals at a high sampling rate to find ten most relevant features of electrical discharge machining. However, due to the long processing time and the huge amount of data, Gan et al [8] proposed to use data mining algorithms to solve the feature dimension problem.…”

Section: Introductionmentioning

confidence: 99%

Development of an On-Line Defect Detection System for EDM Process

Lyu¹,

Jan

Ay³

et al. 2022

Applied Sciences

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In the electrical discharge machining process, preliminary research has been able to effectively estimate machining accuracy in response to its long machining history and high discharge frequency characteristics. However, when processing abnormalities occur, it is difficult to identify them since the electrical discharge process contains multiple processing parameters, which increases the cost of repair or loss afterwards. Therefore, the question concerning how to monitor the abnormality of the discharge process in real time represents the main purpose of this research. This research develops an EDM process abnormal diagnosis system. First, the data are stored in a circular array to speed up the processing time, and the coefficient of variation feature is added, which has effectively extracted the abnormal characteristics. In terms of diagnostic methods, the composite voting model established by neural networks, random forests, and XGB-RF (extreme gradient boosting applying RF) can provide robust diagnostic results. Finally, through the Node-RED webpage and MQTT agreement, it can provide the ability to monitor machine abnormalities in real time. Through refinement and optimization of the previous research results, this study took the electrical discharge machining diamond grinding wheel as an example, and developed a warning that can be issued within 3 min when abnormalities (abnormal patterns such as polycrystalline diamond high protrusions) occur, with an accuracy of 93% and a false positive rate. The abnormal diagnosis ability is less than 0.2%. Therefore, the online abnormality monitoring system developed by this research institute will be able to provide online abnormality diagnosis for electrical discharge machining.

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“…The results revealed that the average deviation between network predictions and actual components is below 6µm, which falls within the current limits of process accuracy. The search and recognition of behavioral patterns of voltage and current signals in the WEDM process has been studied by Caggiano et al [ 31 , 32 ], who presented a SNN that effectively correlates voltage and current signals with the defects and marks originated on the machined component during the WEDM process. In all cases, the success of the network exceeded 81%.…”

Section: Introductionmentioning

confidence: 99%

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

Sánchez¹,

Conde²,

Arriandiaga

et al. 2018

Materials

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Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.

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Wire EDM Monitoring for Zero-defect Manufacturing based on Advanced Sensor Signal Processing

Cited by 35 publications

References 15 publications

Harizko elektrohigadura bidezko ebaketa-prozesuko aldagaien azterketa altuera aldakorreko piezak ebakitzeko

Harizko elektrohigadura bidezko ebaketa-prozesuko aldagaien azterketa altuera aldakorreko piezak ebakitzeko

Development of an On-Line Defect Detection System for EDM Process

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

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