Analysis of Crack Deffect at Nut M 14 Cold Forging Process with FTA Methode

Haris, Oscar; Sumarno, Dodi Iwan; Arifin, Zaenal; Nurzaman, Alfi; Darmawan, Agus; Kurtubi, Dede

doi:10.1109/icced46541.2019.9161093

Cited by 1 publication

(1 citation statement)

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“…16 A different approach using fault tree analysis identified when failure occurred within the manufacturing stage and proposed a change in the die design to reduce defects in the final nut part. 17 In recent years, artificial intelligence (AI) image recognition technology was developed to optically inspect samples of finished products to confirm if their measurements were within the required dimensional specifications. 18,19 The most recent study was on a screw-forming machine in which the forging force was recorded in real-time, and kinetic analysis was done to identify error values that would trigger machine stoppage to check for mould damage.…”

Section: Introductionmentioning

confidence: 99%

Application of vibration analysis using time-frequency analysis to detect and predict mechanical failure during the nut manufacturing process

Novelo

Chu

2022

Advances in Mechanical Engineering

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The nut manufacturing machine operates almost continuously throughout the day and, consequently, requires frequent maintenance, the duration of which depends on the type of fault. Unpredicted downtime can cause loss of production volume, inability to meet critical timelines, and product quality degradation. Vibration research was conducted on a six-die nut manufacturing machine, which experienced frequent faults on the fourth and sixth dies that exerted the greatest impact on the nut material. The application of several analytical techniques on vibration data – the fast Fourier Transform, Empirical Mode Decomposition, Multiscale Entropy, Shock Response Spectrum, and correlation – enabled the identification of fault indicators, which would be useful for predictive maintenance. These features were detectable up to 8 h 28 min and up to 1 h 9 min prior to failure or deterioration resulting from severe and regular damage, respectively. Faults included quality deterioration and die and non-die breakage, fusion, bending and cracking. This research can optimise machine efficiency through timely upkeep facilitated by predictive maintenance.

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Section: Introductionmentioning

confidence: 99%