Integrated optimisation of surface roughness and tool performance when face milling 416 SS

Muñoz‐Escalona, Patricia; Maropoulos, Paul

doi:10.1080/09511920903529230

Cited by 15 publications

(10 citation statements)

References 23 publications

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“…9 the surface roughness increases when increasing the feed per tooth. This result is in agreement with previous research and is probably due to the fact that as the feed is increased the thickness of the chip also increases resulting in an increase of cutting forces [8,13,14].…”

Section: Surface Roughnesssupporting

confidence: 94%

Influence of cutting environments on surface integrity and power consumption of austenitic stainless steel

Muñoz‐Escalona

Shokrani

Newman

2015

Robotics and Computer-Integrated Manufacturing

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Surface roughness is a result of the cutting parameters such as: cutting speed, feed per tooth and the axial depth of cut, also the tool’s geometry, tool’s wear vibrations, etc. Moreover, the surface finish influences mechanical properties such as fatigue behaviour, wear, corrosion, lubrication and electrical conductivity and the combination of cutting parameters influence the power consumption during the machining process affecting the environment. The research reported herein is focused mainly on searching for an optimum combination of cutting parameters to obtain a low value of surface roughness and minimize energy consumption when milling an austenitic stainless steel in different cutting environments. The experiments were conducted on a Siemens 840D Bridgeport Vertical Machining Centre 610XP2. The selection of this workpiece material was based on it’s widely applications in cutlery, surgical instruments, industrial equipment and in the automotive and aerospace industry due to its high corrosion resistance and high strength characteristics. The results show that the dry cutting environment is the best option in terms of power consumption and surface roughness values to conduct the milling of an austenitic stainless steel under the selected cutting parameters

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Section: Surface Roughnesssupporting

confidence: 94%

Influence of cutting environments on surface integrity and power consumption of austenitic stainless steel

Muñoz‐Escalona

Shokrani

Newman

2015

Robotics and Computer-Integrated Manufacturing

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“…In accordance with the cutting theory, 8,30 once the relief angle second takes its optimum value (30°), the surface roughness decreases while the cutting speed increases. Additionally the core diameter takes its optimum value (50%).…”

Section: Meansupporting

confidence: 56%

Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: An aluminium alloy 5083 case study

Karagiannis

Stavropoulos

Ziogas

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part B:

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A mastering of surface quality issues during machining helps avoiding failure, enhances component integrity and reduces overall costs. Surface roughness significantly affects the quality performance of finished components. A number of parameters, both material and process oriented, influence at a different extend the surface quality of the finished product. Aluminium alloy 5083 component surface quality, achieved in side end milling, constitutes the subject of the present case study. The design of experiment method is employed: that is, 18 carbide two-flute end mill cutters -manufactured by a five-axis grinding machine -have been assigned to mill 18 pockets in finishing conditions -having different combinations of geometry and cutting parameters values, according to the L 18 (2 1 3 3 7 ) standard orthogonal array. Process performance is estimated using the statistical surface texture parameters R a , R y and R z -measured during three different passes on the side surface of the pockets. The results indicate that process parameters -such as the cutting speed, the peripheral second relief angle and the core diameter -mostly influence surface texture. The experimental values are used to train a feed forward back-propagation artificial neural network for the prediction of the yield surface roughness magnitude.

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“…Saï and Bouzid [12] optimized cutting parameters for face milling process to optimize the roughness parameter. Muñoz-Escalona and Maropoulos [13] experimentally investigated the effects of machining parameters of face milling responses, such as on the material removal amount, then the performance of tool that served, and the surface roughness parameter. On the other hand, the Taguchi technique was applied by Fratila and Caizar [14] in face milling of Al-Mg alloy.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics

et al. 2019

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In face milling one of the most important parameters of the process quality is the roughness of the machined surface. In many articles, the influence of cutting regimes on the roughness and cutting forces of face milling is considered. However, during flat face milling with the milling width B lower than the cutter’s diameter D, the influence of such an important parameter as the relative position of the face mill towards the workpiece and the milling kinematics (Up or Down milling) on the cutting force components and the roughness of the machined surface has not been sufficiently studied. At the same time, the values of the cutting force components can vary significantly depending on the relative position of the face mill towards the workpiece, and thus have a different effect on the power expended on the milling process. Having studied this influence, it is possible to formulate useful recommendations for a technologist who creates a technological process using face milling operations. It is possible to choose such a relative position of the face mill and workpiece that will provide the smallest value of the surface roughness obtained by face milling. This paper shows the influence of the relative position of the face mill towards the workpiece and milling kinematics on the components of the cutting forces, the acceleration of the machine spindle in the process of face milling (considering the rotation of the mill for a full revolution), and on the surface roughness obtained by face milling. Practical recommendations on the assignment of the relative position of the face mill towards the workpiece and the milling kinematics are given.

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Integrated optimisation of surface roughness and tool performance when face milling 416 SS

Cited by 15 publications

References 23 publications

Influence of cutting environments on surface integrity and power consumption of austenitic stainless steel

Influence of cutting environments on surface integrity and power consumption of austenitic stainless steel

Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: An aluminium alloy 5083 case study

Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics

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