Preliminary study for online monitoring during the punching process

Sari, Delima Yanti; Wu, Tsung-Liang; Lin, Bor-Tsuen

doi:10.1007/s00170-016-8956-y

Cited by 31 publications

(9 citation statements)

References 26 publications

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“…In concurrent studies, our research team has made signi cant strides in monitoring the service life of individual punch heads during the punching process. As detailed in our previous publication [5], we successfully demonstrated that both time and frequency analyses of extracted feature signals could establish a quanti able relationship between the wear of punch heads and the resulting burr height.…”

Section: Methodsmentioning

confidence: 68%

Evaluating Service Life of Metal Processing Machinery: An Intelligent Monitoring Perspective

Wang,

Hung,

Chen

2024

Preprint

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This investigation addresses a range of critical challenges within the domain of mechanical engineering and anticipates their potential impacts. The study's goals include developing methods for detecting tool breakage in integrated milling-turning machines, evaluating the service life of punching machine components, and determining the durability of molds in forging equipment, alongside other complex issues. The primary aim is to devise a specialized equipment health diagnostic system, designed for complex industrial environments. Industry consultation has revealed that effective monitoring strategies and threshold values must be tailored to the specific characteristics of each piece of equipment and their respective sectors. Despite the metal processing industry lagging roughly a decade behind the semiconductor sector in adopting intelligent monitoring systems, it encounters similar hurdles. These include shrinking labor demographics necessitating increased reliance on shift-based external labor, higher turnover rates impacting the retention of skilled workers for essential tasks such as tool replacements and machinery maintenance. Furthermore, there is a pressing need to maintain traceability for the usage history of molds and punching heads, especially to meet aerospace industry regulations. In response, the sector aims to accomplish two primary goals for its critical production machinery: firstly, to implement diagnostic tools for evaluating the wear and overall quality of tools and molds; secondly, to shift from time-based to condition-based maintenance protocols, adaptable to the frequent mold changes required for varied product fabrication.

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Section: Methodsmentioning

confidence: 68%

Evaluating Service Life of Metal Processing Machinery: An Intelligent Monitoring Perspective

Wang,

Hung,

Chen

2024

Preprint

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“…Therefore, sensors are increasingly integrated into the processes and attempts are made to identify correlations between process anomalies and features of the recorded time series. Conventional approaches monitor the time series with the help of thresholds [1], linear discriminant functions [2] or envelope curves and can thus distinguish binary process states from each other. In particular, the occurrence of punch wear, which has a negative impact on the resulting product quality [3], is a widely researched application scenario.…”

Section: Introductionmentioning

confidence: 99%

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Molitor

Kubik

Hetfleisch

et al. 2022

Prod. Eng. Res. Devel.

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Blanking processes belong to the most widely used manufacturing techniques due to their economic efficiency. Their economic viability depends to a large extent on the resulting product quality and the associated customer satisfaction as well as on possible downtimes. In particular, the occurrence of increased tool wear reduces the product quality and leads to downtimes, which is why considerable research has been carried out in recent years with regard to wear detection. While processes have widely been monitored based on force and acceleration signals, a new approach is pursued in this paper. Blanked workpieces manufactured by punches with 16 different wear states are photographed and then used as inputs for Deep Convolutional Neural Networks to classify wear states. The results show that wear states can be predicted with surprisingly high accuracy, opening up new possibilities and research opportunities for tool wear monitoring of blanking processes.

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“…Zhang et al [ 8 ] used piezoelectric strain sensors to measure the strain on the press column surface through feature selection to monitor and detect failures in the punching process. Sari et al [ 9 ] demonstrated the relationship between increasing the amplitude within a certain frequency range and the appearance of burrs and onset of damage that leads to punch failure. Continuous use of the tool can cause various problems, such as wear, variation in the quality of the product, and damage to the machine.…”

Section: Introductionmentioning

confidence: 99%

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

Huang

Dzulfikri

2021

Sensors

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Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.

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Preliminary study for online monitoring during the punching process

Cited by 31 publications

References 26 publications

Evaluating Service Life of Metal Processing Machinery: An Intelligent Monitoring Perspective

Evaluating Service Life of Metal Processing Machinery: An Intelligent Monitoring Perspective

Workpiece image-based tool wear classification in blanking processes using deep convolutional neural networks

Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network

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