Machining feature process route planning based on a graph convolutional neural network

Wang, Zhen; Zhang, Shusheng; Zhang, Hang; Zhang, Yajun; Liang, Jiachen; Huang, Rui; Huang, Bo

doi:10.1016/j.aei.2023.102249

Cited by 8 publications

(2 citation statements)

References 44 publications

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“…The study was successfully validated using parts for the aeronautical industry. In a related work using the same methodology, the same team [23] reports that, after training, the model revealed an accuracy of 93.31% in predicting process routes for machining features, thus showing its high effectiveness. Given the accentuated wear usually presented by ball-end tools, and considering the theory of uniform wear, Guo et al [24] developed a continuous oscillating milling strategy, which aims to dynamically adjust the oscillating angle of the tool axis, promoting a reduction in wear and thus significantly prolonging the useful life of the tool.…”

Section: Milling Strategiesmentioning

confidence: 94%

A Comparative Study of Different Milling Strategies on Productivity, Tool Wear, Surface Roughness, and Vibration

Silva,

Martinho,

Magalhães

et al. 2024

JMMP

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Strategies for obtaining deep slots in soft materials can vary significantly. Conventionally, the tool travels along the slot, removing material mainly with the side cutting edges. However, a “plunge milling” strategy is also possible, performing the cut vertically, taking advantage of the tip cutting edges that almost reach the center of the tool. Although both strategies are already commonly used, there is a clear gap in the literature in studies that compare tool wear, surface roughness, and productivity in each case. This paper describes an experimental study comparing the milling of deep slots in AA7050-T7451 aluminum alloy, coated with a novel DLCSiO500W3.5O2 layer to minimize the aluminum adhesion to the tool, using conventional and plunge milling strategies. The main novelty of this paper is to present a broad study regarding different factors involved in machining operations and comparing two distinct strategies using a novel tool coating in the milling of aeronautical aluminum alloy. Tool wear is correlated with the vibrations of the tools in each situation, the cycle time is compared between the cases studied, and the surface roughness of the machined surfaces is analyzed. This study concludes that the cycle time of plunge milling can be about 20% less than that of conventional milling procedures, favoring economic sustainability and modifying the wear observed on the tools. Plunge milling can increase productivity, does not increase tool tip wear, and avoids damaging the side edges of the tool, which can eventually be used for final finishing operations. Therefore, it can be said that the plunge milling strategy improves economic and environmental sustainability as it uses all the cutting edges of the tools in a more balanced way, with less global wear.

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Section: Milling Strategiesmentioning

confidence: 94%

A Comparative Study of Different Milling Strategies on Productivity, Tool Wear, Surface Roughness, and Vibration

Silva,

Martinho,

Magalhães

et al. 2024

JMMP

View full text Add to dashboard Cite

show abstract

“…Shao et al [75] proposed a machining metabody-based framework to facilitate the automatic identification and localization of manufacturing features in CAD models for process model generation. Wang et al [76] utilized a graphical convolutional neural network to efficiently plan machining features, achieving a high predictive accuracy of 93.31% for machining routes.…”

Section: Build Different Granularity Informationmentioning

confidence: 99%

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

Wu,

Liang

2024

Machines

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The evolution of knowledge acquisition and representation in manufacturing technologies is vital for translating complex manufacturing data into actionable insights and advancing a comprehensive knowledge framework. This framework is pivotal in driving innovation and efficiency in intelligent manufacturing. This review aggregates recent research on knowledge acquisition and representation within the manufacturing process, addressing existing challenges and mapping potential future developments. It includes an analysis of 123 papers that focus on harnessing advanced intelligent analytics to extract operationally relevant knowledge from the extensive datasets typical in manufacturing environments. The narrative then examines the methodologies for constructing models of knowledge in manufacturing processes and explores their applications in manufacturing principles, design, management, and decision-making. This paper highlights the limitations of current technologies and projects emerging research avenues in the acquisition and representation of process knowledge within intelligent manufacturing systems, with the objective of informing future technological breakthroughs.

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SparseDet: Towards efficient multi-view 3D object detection via sparse scene representation

Li,

Yang,

Shi

et al. 2024

Advanced Engineering Informatics

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Machining feature process route planning based on a graph convolutional neural network

Cited by 8 publications

References 44 publications

A Comparative Study of Different Milling Strategies on Productivity, Tool Wear, Surface Roughness, and Vibration

A Comparative Study of Different Milling Strategies on Productivity, Tool Wear, Surface Roughness, and Vibration

A Review and Prospects of Manufacturing Process Knowledge Acquisition, Representation, and Application

SparseDet: Towards efficient multi-view 3D object detection via sparse scene representation

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