Detection of Damage and Fracture of Forging Die by Fractal Property of Acoustic Emission

Abstract

The detection of damage to and fracture of cold forging tools during forming operation by fractal property of acoustic emission (AE) is performed. First, a tensile test on tool steel is conducted to elucidate the fractal dimensions of deformation-induced and fracture-induced AEs. The resultant fractal dimensions are 1.97 and 1.44. Next, the change in the fractal dimension of AE from a die insert is investigated under cold forward extrusions. Workpieces with a conversion coating film are used to eliminate the effect of friction on AE. After 300 extrusions, no damage and wear is observed on the die surface, and the fractal dimension is almost constant at 2.04 on average. Then, another series of cold forward extrusions under high-friction conditions is performed with mineral oil VG2 and stearic acid to promote the onset of damage to and fracture of the die. A defect on the surface of the workpiece is observed at the 101st extrusion, which resulted from the onset of crack on the die radius. The former average fractal dimension, 2.01, of the onset of the crack changes to 1.52 after the onset of the crack. From the results, the fractal dimension can be concluded to be one of the most effective indicators of the progress of damage to a cold forging tool. Finally, a method of separating the die-induced AE from the total AE based on the Kaiser effect is proposed.

Research on Precursor Information of Brittle Rock Failure through Acoustic Emission

“…Virkar et al [29] found that the b value could be used to predict rock failure and analyze rock damage. Hayakawa et al [30] researched the AE fractal characteristic of cold forging tool damage during forming operation and calculated the AE fractal dimension of deformation and fracture, which verified that fractal dimension can be used to describe cold forging tool failure as an effective indicator. In summary, many domestic and foreign scholars have extensively investigated AE characteristics and precursor characteristics of rock failure.…”

Applications of deep learning for fault detection in industrial cold forging

“…Żabiński et al (2014) used force sensors and binary sensors to detect three types of defects in the cold forming of fasteners. Hayakawa et al (2004) found that cold forging die fracture was indicated by the decrease of AE fractal property.…”

Acoustic Emission (AE) Monitoring of Open Die and Closed Die Forging Processes of Al Alloy

“…AE generated during upset forging and diskforming operations of a C-Mn steel has been modeled, and it has been reported that the AE signals are characterized by a peak at low upset and reduced AE after 10% deformation [7]. A detection method of damage and fracture of forging tools during forming operation by using fractal property of AE signals has been proposed [11]. Pattern recognition analysis of AE signals has been used for enhancing the signal to noise ratio of the detected AE signals during an industrial deep drawing process [12].…”

“…However, complexity of force/displacement signals and associated difficulties in interpretation of the signals and limitations of their use do not usually give desirable results [4]. In comparison to such force/displacement signals, the use of AE for on-line monitoring of forming processes is known to offer unique advantages [4][5][6][7][8][9][10][11][12]. In the earlier investigations [5][6][7], AE associated with forming processes has been attributed to generation of dislocations in the workpiece material.…”