Acoustic Emission Analysis for Online Monitoring in Sheet Metal Forming

在钣金成形行业，由于对质量要求的持续需求，特别是由于高强度和超高强度钢的大量使用，增加了工具的复杂性以及所需的零件精度，对100%检验的需求一直在增加。这些过程通常是自动化的，因此对工具和产品中出现的缺陷的监视和早期检测变得越来越必要。声发射技术（AET）是一种无损检测方法，它通过传感器记录材料在结构变化过程中释放的弹性超声波。在声发射分析中，对记录的信号进行分析和评价，以得出有关工艺和/或产品状况的结论。一些研究已经证明了AET在表征变形、裂纹萌生和相变缺陷方面的适用性。目前还没有关于板料成形过程的在线监测或300 kHz以上板料成形缺陷的确定的科学研究。对声发射信号的记录和分析允许对裂纹、缺乏润滑、加倍毛坯或不正确插入毛坯进行过程监控。监测效果、现象和尺寸的多样性以及分析方法的多样性（包括时间分析、频率分析、时频分析、模式识别）为在钣金成形领域使用声发射方法进行不同的监测任务提供了几种选择，应该在本研究项目中进行更精确的研究。

In the sheet metal forming industry, the need for 100% inspection has been increasing due to the continuous demand on quality requirements and particularly due to the intensive use of high and ultra-high strength steels, increasing tool complexity as well as the required part accuracy. These processes are commonly automated, so the monitoring and early detection of occurring defects of the tool and the product are becoming increasingly necessary. The acoustic emission technique (AET) is a non-destructive testing method, which records the released elastic ultrasonic waves by sensors generated during the structural change of the material. Within the acoustic emission analysis, the recorded signals get analyzed and evaluated in order to draw conclusions about the condition of the process and / or product.The applicability of AET for characterizing defects due to deformation, crack initiation and phase transformation could be already proofed in several research studies. There are no scientific investigations dealing with online monitoring of sheet metal forming processes or with the determination of sheet metal forming defects above 300 kHz.Recording and analyzing of acoustic emission signals allows the process monitoring with regard of cracks, lack of lubrication, doubling of blanks or incorrect insertion of the blanks. The diversity of the monitored effects, phenomena and sizes as well as the variety of analytical methods (including time analysis, frequency analysis, time-frequency analysis, pattern recognition) have several options for using the AE method for different monitoring tasks in the field of sheet metal forming and should be investigated more precisely within this research project.