Diagnosis of tapping operations using an AI approach

Liu, T. I.; Ko, E. J.; Sh, Sha

doi:10.1007/bf02833631

Cited by 18 publications

(5 citation statements)

References 6 publications

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“…Focusing on tapping operation, Chen et al [12] proposed a classification system based on a set of parameters from the torque and force signals (radial and thrust) applying condition probability functions to classify three common tapping faults: tap wear level, misalignment between hole and tap axis and under/oversized predrilled holes diameter. Liu et al [13] developed a neural network strategy based on ten parameters from the same signals to address the same types of faults as the previous author. These two first strategies used intrusive sensors in manufacturing cell.…”

Section: Introductionmentioning

confidence: 99%

Oversizing Thread Diagnosis in Tapping Operation

Val¹,

Veiga²,

Penalva³

et al. 2021

Metals

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Automotive, railway and aerospace sectors require a high level of quality on the thread profiles in their manufacturing systems knowing that the tapping process is a complex manufacturing process and the last operation in a manufacturing cell. Therefore, a multivariate statistical process control chart, for each tap, is presented based on the principal components of the torque signal directly measured from spindle motor drive to diagnosis the thread profile quality. This on-line multivariate control chart has implemented an alarm to avoid defected screw threads (oversized). Therefore, it could work automatically without any operator intervention assessing the thread quality and the safety is guaranteed during the tapping process.

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Section: Introductionmentioning

confidence: 99%

Oversizing Thread Diagnosis in Tapping Operation

Val¹,

Veiga²,

Penalva³

et al. 2021

Metals

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“…Focusing on tapping monitoring systems, Chen et al [10] proposed a classification approach of three common faults (tap wear level, misalignment between hole and tap axis and under/oversized predrilled holes diameter) based on condition probability functions throughout torque and forces parameters. Then, Liu et al [11] studied the same faults analyzing the same signals based on neuronal networks. Both investigations used intrusive sensors to register the signals.…”

Section: Introductionmentioning

confidence: 99%

Thread Quality Control in High-Speed Tapping Cycles

Val¹,

Veiga²,

Suárez³

et al. 2020

JMMP

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Thread quality control is becoming a widespread necessity in manufacturing to guarantee the geometry of the resulting screws on the workpiece due to the high industrial costs. Besides, the industrial inspection is manual provoking high rates of manufacturing delays. Therefore, the aim of this paper consists of developing a statistical quality control approach acquiring the data (torque signal) coming from the spindle drive for assessing thread quality using different coatings. The system shows a red light when the tap wear is critical before machining in unacceptable screw threads. Therefore, the application could reduce these high industrial costs because it can work self-governance.

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“…A classification accuracy of 95% was reported on the training data. A neural network approach to the same classification problem addressed by Chen et al (1990) was proposed in the work of Liu et al (1991). A set of ten features derived from the same torque and cutting force signals were used as the network input nodes.…”

Section: Introductionmentioning

confidence: 99%