Machinability of Scots pine during peripheral milling with helical cutters

Dong, Jiancheng; Wei, Kejun

doi:10.15376/biores.16.4.8172-8183

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…The optimal cutting parameters determined in this study were rake angle 2 • , cutting speed 9.0 m/s, feed per tooth 0.3 mm, and cutting depth 1.5 mm. This is in contrast to traditional wood materials [20][21][22][23]. Optimal cutting parameters for WPC yielding the lowest surface roughness were determined to include a higher rake angle and cutting speed, at a lower feed per tooth and cutting depth.…”

Section: Optimal Cutting Parameters For Wood-plastic Composite Machiningmentioning

confidence: 99%

Machinability of Different Wood-Plastic Composites during Peripheral Milling

et al. 2022

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The aim of this study was to improve the machinability of wood-plastic composites by exploring the effects of different wood-plastic composites on machinability. In particular, the effects of milling with cemented carbide cutters were assessed by investigating cutting forces, cutting temperature, surface quality, chip formation, and tool wear. The cutting parameters determined to yield an optimal surface quality were rake angle 2°, cutting speed 9.0 m/s, feed per tooth 0.3 mm, and cutting depth 1.5 mm. In these optimized milling conditions, the wood-plastic composite with polypropylene exhibited the highest cutting forces, cutting temperature, and tool wear, followed by polyethylene and polyvinyl chloride wood-plastic composites. Two wear patterns were determined during wood-plastic composite machining, namely chipping and flaking. Due to the different material composition, semi-discontinuous ribbon chips and continuous ribbon chips were generated from the machining process of wood-plastic composites with polypropylene and polyethylene, respectively. The wood-plastic composite with polyvinyl chloride, on the other hand, formed needle-like chips. These results contribute to a theoretical and practical basis for improved wood-plastic composite machining in industrial settings.

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Section: Optimal Cutting Parameters For Wood-plastic Composite Machiningmentioning

confidence: 99%

Machinability of Different Wood-Plastic Composites during Peripheral Milling

et al. 2022

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“…Beech wood (Fagus sylvatica L.) is one of the ideal materials for furniture with respect to its excellent mechanical properties and beautiful appearance and texture [5]. To process components of differing sizes and shapes, beech wood requires multiple cutting processes, such as turning, milling, and planing [6][7][8]. Therefore, the cutting performance of beech wood has always been a research hotspot in the field of material processing.…”

Section: Introductionmentioning

confidence: 99%

Cutting Power, Temperature, and Surface Roughness: A Multiple Target Assessment of Beech during Diamond Milling

Buck

Yang

et al. 2023

Forests

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Beech wood is a material commonly used for furniture, and cutting performance is the key to improving product quality and enterprise benefits. In this work, beech milling experiments using diamond cutters were carried out, and the changes in cutting power, temperature, and surface roughness were examined using the factor analysis method. The main results of this work are listed as follows: Firstly, a higher cutting speed and depth led to higher cutting power, temperature, and surface roughness. Meanwhile, cutting power and surface roughness were negatively related to the rake angle; however, cutting temperature first increased and then decreased with the increase in rake angle. Furthermore, cutting depth had greatest impact on the cutting power and surface roughness, followed by rake angle and cutting speed. Cutting speed had the greatest contribution to the cutting temperature, followed by cutting depth and rake angle. Only the cutting depth had a significant contribution to both cutting power, temperature, and surface roughness. Finally, optimal cutting parameters were determined to be a rake angle of 15°, cutting speed of 54 m/s, and depth of 0.5 mm. These values best meet the multiple objectives of lower cutting power, temperature, and surface roughness, which relate to superior product quality and enterprise benefits.

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“…Milling is the most commonly used processing method for wood machining for outstanding machining efficiency [13]. However, due to the lack of research on the milling performance of beech and the non-uniformity of beech wood, there are still quality problems such as abnormal surface damage during industrial beech machining, and how to reduce surface roughness is the key to improve product quality [14,15].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System

Zhu

Dong

et al. 2022

Machines

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This work focused on changes in surface roughness under different cutting conditions for improving the cutting quality of beech wood during milling. A response surface methodology and an adaptive network-based fuzzy inference system were adopted to model and establish the relationship between milling conditions and surface roughness. Moreover, the significant impact of each factor and two-factor interactions on surface roughness were explored by analysis of variance. The specific objective of this work was to find milling parameters for minimum surface roughness, and the optimal milling condition was determined to be a rake angle of 15°, a spindle speed of 3357 r/min and a depth of cut of 0.62 mm. These parameters are suggested to be used in actual machining of beech wood with respect of smoothness surface.

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Machinability of Scots pine during peripheral milling with helical cutters

Cited by 7 publications

References 19 publications

Machinability of Different Wood-Plastic Composites during Peripheral Milling

Machinability of Different Wood-Plastic Composites during Peripheral Milling

Cutting Power, Temperature, and Surface Roughness: A Multiple Target Assessment of Beech during Diamond Milling

Assessment of Surface Roughness in Milling of Beech Using a Response Surface Methodology and an Adaptive Network-Based Fuzzy Inference System

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