Failure analysis of an EDM machined mold-printing die used for the production of truck spare parts

Leptidis, S.; Medrea, C.; Chicinaş, Ionel

doi:10.1016/j.engfailanal.2015.08.007

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…A characteristic of the production process of stamping progressive dies is periodic motion, which ensures highly stable production quality. However, the state of the progressive die often includes anomalies such as residual processing waste and foreign object splashes on the production line, as well as contamination and severe deformation of the workpiece [4,5]. If these anomalies occur at the stations of the progressive die and stamping continues, these anomalies may cause damage to the expensive mold and even pose a threat to production safety.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Ma,

Meng

2023

Sensors

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In the production process of progressive die stamping, anomaly detection is essential for ensuring the safety of expensive dies and the continuous stability of the production process. Early monitoring processes involve manually inspecting the quality of post-production products to infer whether there are anomalies in the production process, or using some sensors to monitor some state signals during the production process. However, the former is an extremely tedious and time-consuming task, and the latter cannot provide warnings before anomalies occur. Both methods can only detect anomalies after they have occurred, which usually means that damage to the die has already been caused. In this paper, we propose a machine-vision-based method for real-time anomaly detection in the production of progressive die stamping. This method can detect anomalies before they cause actual damage to the mold, thereby stopping the machine and protecting the mold and machine. In the proposed method, a whole continuous motion scene cycle is decomposed into a standard background template library, and the potential anomaly regions in the image to be detected are determined according to the difference from the background template library. Finally, the shape- and size-adaptive descriptors of these regions and corresponding reference regions are extracted and compared to determine the actual anomaly regions. The experimental results indicate that this method can achieve reasonable accuracy in the detection of anomalies in the production process of stamping progressive dies. The experimental results demonstrate that this method not only achieves satisfactory accuracy in anomaly detection during the production of progressive die stamping, but also attains competitive performance levels when compared with methods based on deep learning. Furthermore, it requires simpler preliminary preparations and does not necessitate the adoption of the deep learning paradigm.

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Section: Introductionmentioning

confidence: 99%

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Ma,

Meng

2023

Sensors

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“…A characteristic of the main manufacturing procedure in most processing machines is periodic motion, which ensures highly stable production quality [1]. However, the production process often involves anomalies, such as splashing of foreign bodies and loosening of machine parts [2,3], which result in workpiece damage, waste products, and even equipment damage and device malfunction, causing significant economic losses [4]. Therefore, anomaly monitoring of processing equipment has become an important method for ensuring normal operation.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection in periodic motion scenes based on multi-scale feature Gaussian weighting analysis

Wang¹,

Meng²,

Huang³

et al. 2019

Meas. Sci. Technol.

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Anomaly monitoring of production line processing equipment based on machine vision is an important method for ensuring its efficient and stable operation. However, problems related to dynamic scenes, accidental and non-transcendental anomalies, image vulnerability to the severe vibrations of machining equipment, and difficulty in accepting the missing detection are significant obstacles to abnormality monitoring in the machining process. A periodic motion scene decomposition method is presented in this paper to solve dynamic scenes, occasional anomalies, severe vibrations, and other issues. Through optimization of the morphological structural elements, the feature points of the 'abnormality' region are obtained, and a Gaussian weighting formula is derived to detect the anomaly and improve the accuracy of detection. This method, which is verified by experiments, effectively overcomes problems related to the machining process and achieves good detection results.

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“…The MRR was found to decrease with an increase in the percent volume of SiC, whereas the TWR and the surface roughness increase with an increase in the volume of Sic. it shown the graph between interactive effect of the percent volume of Sic and the current on MRR [10]. Taweel [11] fabricated Tool electrode material such as Al-Cu-Si-Tic composite produced using powder metallurgy (P/M) technique and using workpiece material CK45 steel.…”

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confidence: 99%

Parametric Analysis during Electric Discharge Machining of Aluminium MMC using Taguchi’s Methodology

Kumar¹

2018

IJRASET

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The advancement in the material processing has leads to the development of composite materials which possess the better mechanical properties than conventional materials. In the present paper, machining of developed aluminum metal matrix composite was performed using electric discharge machining process. the experimentation was planned as per Taguchi's methodology using L 9 orthogonal array. Voltage, current, and pulse on time were used as input process parameters whereas surface roughness was used as response parameters for the process. The results predicted that the parametric combination In order to analyze their machining behavior, a number of machining processes are being widely used [4,5]. A lot new machining techniques are being developed in the field of micromachining. In the field of micromachining, electric discharge machining (EDM) process is a quite familiar name. A part of it, EDM drilling is excellent for machining deep and narrow holes regardless of material hardness and location, whereas die-sinking EDM works well to machine micro-parts and perpendicular walls of die and molds [6].Current paper investigates the carbonaceous layer and its formation during the erosion. During Electric Discharge Machining process several material loadings take place on the workpiece surface within the processing zone. To model the comprehensive material removal and its effects on the resulting material properties especially in the rim zone and on surface integrity it is necessary to describe these loadings on several length scales in detail [7]. The influence of EDM parameters on material removal rate, electrode wear, machining time and micro-hole quality when machining Ti6Al4V is studied and conclusion says that due to an inefficient removal of debris when increasing hole depth, a new strategy based on the use of helical-shaped electrodes has been proposed [8]. Dhar and Purohit [9] evaluated the effect of current (c), pulse-on time (p) and air gap voltage (v) on MRR, TWR, ROC of EDM with Al-4Cu-6Si alloy-10 wt. % SiC P composites. The MRR was found to decrease with an increase in the percent volume of SiC, whereas the TWR and the surface roughness increase with an increase in the volume of Sic. it shown the graph between interactive effect of the percent volume of Sic and the current on MRR [10]. Taweel [11] fabricated Tool electrode material such as Al-Cu-Si-Tic composite produced using powder metallurgy (P/M) technique and using workpiece material CK45 steel. B.Mohan et.al [12] explains the evolution of effect of the EDM Current, electrode marital polarity, pulse duration and rotation of electrode on metal removal rate. TWR and SR. EDM of Al-Sic with 20-25 vol. %, the MRR increased with increased in discharge current and specific current it decreased with increase in pulse duration. The EDM process's workpiece generated by the superposition of multiple discharges, as it happens during an actual EDM operation, by diameter of the discharge channel and material removal efficiency can be estimated using i...

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Failure analysis of an EDM machined mold-printing die used for the production of truck spare parts

Cited by 5 publications

References 31 publications

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Anomaly Detection in the Production Process of Stamping Progressive Dies Using the Shape- and Size-Adaptive Descriptors

Anomaly detection in periodic motion scenes based on multi-scale feature Gaussian weighting analysis

Parametric Analysis during Electric Discharge Machining of Aluminium MMC using Taguchi’s Methodology

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