Tool wear monitoring of high-speed broaching process with carbide tools to reduce production errors

Olmo, Ander del; Lacalle, Luís Norberto López de; Pissón, G. Martínez de; Pérez-Salinas, Cristian; Ealo, J.A.; Sastoque, L.; Fernandes, María Helena

doi:10.1016/j.ymssp.2022.109003

Cited by 38 publications

(14 citation statements)

References 26 publications

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“…Broaching remains the predominant method for mass production of turbine disc slots. However, as the contours of slots grow more intricate, certain structures, notably circular slots, elude broaching, exacerbating tool wear and cost escalation [7]. While milling can accommodate certain structures, achieving high surface integrity for slots proves challenging due to the limitations inherent in its tool structure and cutting principle [8].…”

Section: Introductionmentioning

confidence: 99%

Experimental investigations on WEDM performance characteristics of FGH99 powder metallurgy superalloy

Qian,

Zhao,

et al. 2024

Preprint

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In response to challenges posed by conventional machining methods, an exploratory experimental investigation was conducted to assess the effectiveness of wire electric discharge machining (WEDM) in fabricating turbine disc slots from powder metallurgy superalloy FGH99. The present study conducted a single-factor experiment on WEDM using FGH99 alloys, with the aim of investigating the impact of different WEDM parameters on the material removal rate, surface roughness, and surface morphology. Additionally, the analysis encompassed the characterization of both the heat-affected zone and plastic deformation zone on the workpiece surface. Results show that there is a positive correlation between surface roughness and variables such as pulse width, pulse gap, servo voltage, and wire tension. Conversely, wire speed exhibits an inverse relationship with surface roughness. Notably, the impact of pulse width on surface roughness surpasses that of other parameters. Metallographic examination reveals that under the selected process parameters, while ensuring the efficiency of material removal, superior machining quality was achieved. The absence of conspicuous recast layers on the workpiece surface, with an average thickness of the plastic deformation layer measured at 3.97 µm, indicating favorable processing outcomes. The aforementioned findings provide valuable experimental insights into the WEDM process of FGH99 alloys and offer foundational engineering data for the fabrication of turbine disc slots made from this material.

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Section: Introductionmentioning

confidence: 99%

Experimental investigations on WEDM performance characteristics of FGH99 powder metallurgy superalloy

Qian,

Zhao,

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…In most cases, the quality of a part is understood as a combination of the above criteria. A global trend in manufacturing is to relate the quality of parts with process parameters [ 6 ]. However, multiple process parameters of injection molding can vary in a wide range, and moreover, they interrelate in a complex way and, consequently, sometimes have a non-obvious influence on the quality of parts.…”

Section: Introductionmentioning

confidence: 99%

“…However, multiple process parameters of injection molding can vary in a wide range, and moreover, they interrelate in a complex way and, consequently, sometimes have a non-obvious influence on the quality of parts. Therefore, finding a relation between product quality and process parameters is a non-trivial task that requires a comprehensive approach, which often involves a combination of sensoring and information techniques [ 6 ]. Statistical analysis [ 7 , 8 ] and artificial intelligence (AI) [ 9 ], especially machine learning (ML) [ 10 , 11 , 12 ], are used more and more frequently today for the process optimization and quality control of industrial manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

Párizs

Török

Ageyeva

et al. 2023

Sensors

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The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements.

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“…Automated production processes are an important component of Industry 4.0, and mechanized machining automation is one of the main components of automated production processes. In the machining process, cutting tools are end-effectors that come into direct contact with the workpiece and are prone to wear, which can affect the surface quality of the workpiece [ 1 ]. Zhou et al [ 2 ] stated that tool wear and damage are the main factors leading to process failure in machining.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals

Zhang

Wang

2023

Sensors

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Tool wear condition monitoring is an important component of mechanical processing automation, and accurately identifying the wear status of tools can improve processing quality and production efficiency. This paper studied a new deep learning model, to identify the wear status of tools. The force signal was transformed into a two-dimensional image using continuous wavelet transform (CWT), short-time Fourier transform (STFT), and Gramian angular summation field (GASF) methods. The generated images were then fed into the proposed convolutional neural network (CNN) model for further analysis. The calculation results show that the accuracy of tool wear state recognition proposed in this paper was above 90%, which was higher than the accuracy of AlexNet, ResNet, and other models. The accuracy of the images generated using the CWT method and identified with the CNN model was the highest, which is attributed to the fact that the CWT method can extract local features of an image and is less affected by noise. Comparing the precision and recall values of the model, it was verified that the image obtained by the CWT method had the highest accuracy in identifying tool wear state. These results demonstrate the potential advantages of using a force signal transformed into a two-dimensional image for tool wear state recognition and of applying CNN models in this area. They also indicate the wide application prospects of this method in industrial production.

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Tool wear monitoring of high-speed broaching process with carbide tools to reduce production errors

Cited by 38 publications

References 26 publications

Experimental investigations on WEDM performance characteristics of FGH99 powder metallurgy superalloy

Experimental investigations on WEDM performance characteristics of FGH99 powder metallurgy superalloy

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals

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