Acoustic emission—A promising and challenging technique for process monitoring in sheet metal forming

Behrens, Bernd‐Arno; Hübner, Sven; Wölki, Kai

doi:10.1016/j.jmapro.2017.08.002

Cited by 39 publications

(18 citation statements)

References 2 publications

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“…In this study, we focus on fault diagnosis using vibration signals collected from mechanical transfer press. Although there are few studies previously focused on fault diagnosis using vibration data, data used in these studies obtained from hydraulic press [11]. In this work, the analyses are carried out from signal processing point of view.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis: Spectral Analysis of the Vibration Signals in Transfer Press

Yüce¹,

Aslan²,

Hanilçi³

et al. 2020

acperpro

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Fault diagnosis using vibration signals is a major challenge for industrial manufacturing. Obtaining defect information is an important step to make decisions about the maintenance in prognostic and health management systems. Existing studies mostly considers vibration signals collected from elements such as rolling element bearings and hydraulic presses. In this paper, we use the vibration signals obtained from the mechanical transfer press during metal forming process and analyze them from the signal processing point of view. Experimental results reveals that spectral analysis is a good candidate for fault diagnosis and it provides important information about the localized faults embedded in the vibration signals.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis: Spectral Analysis of the Vibration Signals in Transfer Press

Yüce¹,

Aslan²,

Hanilçi³

et al. 2020

acperpro

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“…(7) A technique for process monitoring in the deep drawing of a steel sheet was presented. (8) Smart metal forming with a digital process and IoT was reviewed. (9) In-process sensors for measuring the material deformation in a die cavity were discussed for deep drawing and tube hydroforming processes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tool Fracture in Punching of Thick Steel Plate

Yamashita¹,

Murase²,

Nikawa³

2020

Sensors and Materials

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The sensing of acoustic emission (AE) is efficiently used for the detection of the crack initiation of brittle materials and defects in plastic forming and other manufacturing processes. We carried out circular hole punching tests using carbon steel S45C plates of 5 or 6 mm thickness. The objective was to capture the occurrence of cracks as a predictive phenomenon of punch fracture and their propagation during punching by using an AE sensor. The impulsive stress acting on the punch due to the sudden drop in punch force at the fracture of the plate was also measured using strain gages. This stress induced fracture at the stepped portion of the punch. The maximum tensile stress of the punch depended on the fillet radius at the stepped portion. A reciprocal punching test using both plates alternately was performed to evaluate the detection of crack initiation using an AE waveform. Features in the AE waveform due to crack initiation and the frictional behavior of the crack were captured.

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“…AE technology is advantageous to conventional means due to the fact that AE sensors do not require direct contact to the die itself. AE signals were used to identify conditions of occurring cracks and lubrication, and material differences in deep drawing process using both the maximum amplitude and number of generations (Behrens et al, 2017). Using the maximum amplitude of AE signals in the blanking, conditions of burr occurrence was discussed (Nishimoto et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Forming state recognition in deep drawing process with machine learning

Tsuruya

Danseko

Sasaki

et al. 2019

JAMDSM

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In press processing, quality inspection of a product is often carried out for each lot in the post process stage. When a failure occurs, it may result in a large number of defective products due to the fast processing speed. In order to prevent this, it is ideal to immediately stop the processing just after the defect occurs. Therefore, confirming the processing state in-process is required. This study proposes a new quality inspection method for deep drawing processes by using the count rate of acoustic emission (AE) signals. To analyze the AE count, deep learning, which is a multilayered neural network, is employed to recognize defects during a deep drawing process. The material used was a ductile material of cold rolled steel plate and is relatively difficult to find cracks during the deep drawing process. Characteristics were clarified by analysis of AE counts at plastic deformation and brake of material, and performing the forming state recognition experiment by a multilayer neural network (deep learning) showed a maximum recognition rate of 97.3%. High recognition rate was obtained despite the small number of data used.

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Acoustic emission—A promising and challenging technique for process monitoring in sheet metal forming

Cited by 39 publications

References 2 publications

Fault Diagnosis: Spectral Analysis of the Vibration Signals in Transfer Press

Fault Diagnosis: Spectral Analysis of the Vibration Signals in Transfer Press

Prediction of Tool Fracture in Punching of Thick Steel Plate

Forming state recognition in deep drawing process with machine learning

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