Examination of surface roughness values of 6061-T6 aluminum material after machining and after anodizing process

Karakoç, Erdal; Çakır, O.

doi:10.1016/j.matpr.2022.10.058

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…To achieve a lower surface roughness, a smaller feed rate is needed. Increasing the feed rate results in higher surface roughness, which is consistent with previous research [13]. Zhu et al stated that regardless of drill geometry or material type, a higher feed rate increased chip thickness, which lowered surface roughness [14].…”

supporting

confidence: 88%

Study of Hole Cylindricity and Surface Roughness in Drilling Application on Aircraft Wings

Ibrahim,

binti Radzali,

Mustapa

et al. 2023

MSF

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This study aimed to determine the effect of drilling parameters on hole cylindricity and surface roughness in drilling applications on aircraft wings. It was determined that increasing feed rate resulted in poor hole cylindricity and surface roughness. The ideal feed rate for better hole cylindricity is 0.230 mm/rev, while the ideal feed rate for better surface roughness is 0.200 mm/rev. Both hole cylindricity and surface roughness have a minimum correlation with each other in producing a good quality of drilled holes.

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supporting

confidence: 88%

Study of Hole Cylindricity and Surface Roughness in Drilling Application on Aircraft Wings

Ibrahim,

binti Radzali,

Mustapa

et al. 2023

MSF

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“…With a well-designed cutting edge, this tool delivers efficient material removal and contributes high precision, performance, and efficiency for aluminum machining applications [41]. The aluminum alloy 6061-T6 manufactured by Crown Aluminum Co., Ltd., Kaohsiung, Taiwan with dimensions of 100 × 35 × 50 mm and properties as shown in Table 4 was used as the workpiece material [42][43][44][45]. With favorable characteristics like good thermal conductivity, moderate hardness, and excellent machinability, it allows for efficient material removal and precise machining operations on 5-axis CNC machines.…”

Section: Materials and Equipmentmentioning

confidence: 99%

Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency

Nugrahanto,

Gunawan,

Chen

2024

Sustainability

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Computer Numeric Control (CNC) five-axis milling plays a significant role in the machining of precision molds and dies, aerospace parts, consumer electronics, etc. This research aims to explore the potential of the machine learning (ML) technique in improving energy efficiency during the CNC five-axis milling process for sustainable manufacturing. The experiments with various machining parameters, forms of toolpath planning, and dry cutting conditions were carried out, and the data regarding energy consumption were collected simultaneously. The relationship between machine parameters and energy consumption was analyzed and built. Subsequently, a machine learning algorithm was developed to classify test methods and identify energy-efficient machining strategies. The developed algorithm was implemented and assessed using different classification methods based on the ML concept to effectively reduce energy consumption. The results show that the Decision Tree and Random Forest algorithms produced lower Root Mean Square Error (RMSE) values of 4.24 and 4.28, respectively, compared to Linear, Lasso, and Ridge Regression algorithms. Verification experiments were conducted to ascertain the real-world applicability and performance of the ML-based energy efficiency approach in an operational CNC five-axis milling machine. The findings not only underscore the potential of ML techniques in optimizing energy efficiency but also offer a compelling pathway towards enhanced sustainability in CNC machining operations. The developed algorithm was implemented within a simulation framework and the algorithm was rigorously assessed using machine learning analysis to effectively reduce energy consumption, all while ensuring the accuracy of the machining results and integrating both conventional and advanced regression algorithms into CNC machining processes. Manufacturers stand to realize substantial energy savings and bolster sustainability initiatives, thus exemplifying the transformative power of ML-driven optimization strategies.

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Examination of surface roughness values of 6061-T6 aluminum material after machining and after anodizing process

Cited by 2 publications

References 3 publications

Study of Hole Cylindricity and Surface Roughness in Drilling Application on Aircraft Wings

Study of Hole Cylindricity and Surface Roughness in Drilling Application on Aircraft Wings

Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency

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