Adaptive optimization of cutting parameters in milling industry considering dynamic tool wear in intelligent manufacturing driven by reinforcement learning

Gao, Zhilie; Li, Liang

doi:10.1007/s00170-024-13779-2

Int J Adv Manuf Technol

2024

DOI: 10.1007/s00170-024-13779-2

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Adaptive optimization of cutting parameters in milling industry considering dynamic tool wear in intelligent manufacturing driven by reinforcement learning

Zhilie Gao,

Liang Li

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2024

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Shape Accuracy Improvement of the Flange Turning Process in Aluminum Aerosol Can Production

Sztankovics,

Felho,

Kun-Bodnar

et al. 2024

Eng

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This study investigates the flange turning process in the production of aluminum aerosol bottles. Aluminum discs are lubricated, extruded, trimmed, washed, painted, and lacquered before undergoing necking, where flange turning ensures a secure, aesthetically pleasing fit. Errors in flange turning, such as uneven or tapered surfaces, can compromise bottle functionality and appearance. To address this, experiments were performed with different tool geometries, feed rates, and rotational speeds. The investigations aimed to achieve flat, consistent flange surfaces with minimal deviation from the desired geometry. Two main variables were examined: a 1 s waiting time at the end position and variations in feed rate and cutting depth. The waiting time improved flatness, halving surface deviations, while regrinding the tool reduced flatness errors to a tenth of the original values. Higher feed rates and speeds also enhanced surface quality, with flatness errors ranging from 371 μm to 75 μm. Overall, this study demonstrates that optimizing parameters like cutting angle, feed rate, and rotational speed, along with a waiting period, significantly enhances surface accuracy. These findings support more efficient production processes for aluminum aerosol bottles.

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Shape Accuracy Improvement of the Flange Turning Process in Aluminum Aerosol Can Production

Sztankovics,

Felho,

Kun-Bodnar

et al. 2024

Eng

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show abstract

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Adaptive optimization of cutting parameters in milling industry considering dynamic tool wear in intelligent manufacturing driven by reinforcement learning

Cited by 1 publication

References 12 publications

Shape Accuracy Improvement of the Flange Turning Process in Aluminum Aerosol Can Production

Shape Accuracy Improvement of the Flange Turning Process in Aluminum Aerosol Can Production

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