Surface Roughness Model for Worn Inserts of Face Milling: Part I — Factors that Affect Arithmetic Surface Roughness

Gu, Jie; Barber, Gary C.; Jiang, Qinyu; Tung, Simon C.

doi:10.1080/10402000108982425

Cited by 4 publications

(8 citation statements)

References 4 publications

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“…Note: Case 1 analysis above is identical to that reported in previous research [16], shown in Equations (32) and (33).…”

Section: Of 14supporting

confidence: 52%

“…Equation (34) should not be used for high feed rates, but this limitation is not given in [19]. = 0.0321 Gu et al [16] derived a model to predict the mean roughness under an assumption of perfect round insert, see Equations (32) and (33).…”

Section: Comparison Of Mathematical Model and Experimental Surface Romentioning

confidence: 99%

“…Franco et al [15] derived a numerical model to predict the surface roughness by studying round insert cutting tools and the influence of tool errors, such as radial and axial runouts. Gu et al [16] proposed a mathematical model of surface roughness in face milling based on a surface profile produced by a perfectly round insert. Their research was applicable to face milling using a feed rate below a critical value.…”

Section: Introductionmentioning

confidence: 99%

“…In this research, a model based on the surface profile of the workpiece produced by face milling was developed to predict the mean roughness for three different cases based on the magnitude of feed rate. The approach for modeling is the same as one the used by Gu et al [16]. However, additional analysis has been carried out in the present research to allow for prediction of mean roughness at feed rates above those considered in the previous research.…”

Section: Introductionmentioning

confidence: 99%

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Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Wang

Barber

et al. 2019

Lubricants

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Research was carried out to develop a mathematical model based on the cutting tool surface profile geometry to predict surface roughness in face milling. Previous models were derived using either the simple assumption of a perfectly round tool nose or statistical analysis based on a large number of experiments. In this research, three milling cases were defined based on the magnitude of the feed rate using a triangular insert with a round corner. In case 1, the machine marks only consisted of a series of arcs. In case 2, the machine marks included a series of an arc and one straight line. In case 3, the machine marks consisted of a series of an arc and two straight lines. Three different equations for surface roughness prediction were obtained based on each of the three cases. Experiments were done to validate the models, and the results showed that the mathematical models had good correlation with experimental results.

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“…Note: Case 1 analysis above is identical to that reported in previous research [16], shown in Equations (32) and (33).…”

Section: Of 14supporting

confidence: 52%

Section: Comparison Of Mathematical Model and Experimental Surface Romentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Wang

Barber

et al. 2019

Lubricants

Self Cite

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“…Often, the surface roughness of a mechanical component determines its functionality in the range of its intended use [ 25 , 26 ]. The values of surface roughness parameters mainly depend on the cutting process parameters [ 27 ] and the cutting edge geometry, which is taken into account by models predicting the values of these parameters [ 28 , 29 , 30 ]. The surface roughness also depends on the relative position of the face milling tool towards the workpiece [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Surface Roughness Evaluation in Thin EN AW-6086-T6 Alloy Plates after Face Milling Process with Different Strategies

et al. 2021

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Lightweight alloys made from aluminium are used to manufacture cars, trains and planes. The main parts most often manufactured from thin sheets requiring the use of milling in the manufacturing process are front panels for control systems, housing parts for electrical and electronic components. As a result of the final phase of the manufacturing process, cold rolling, residual stresses remain in the surface layers, which can influence the cutting processes carried out on these materials. The main aim of this study was to verify whether the strategy of removing the outer material layers of aluminium alloy sheets affects the surface roughness after the face milling process. EN AW-6082-T6 aluminium alloy thin plates with three different thicknesses and with two directions relative to the cold rolling process direction (longitudinal and transverse) were analysed. Three different strategies for removing the outer layers of the material by face milling were considered. Noticeable differences in surface roughness 2D and 3D parameters were found among all machining strategies and for both rolling directions, but these differences were not statistically significant. The lowest values of Ra = 0.34 µm were measured for the S#3 strategy, which asymmetrically removed material from both sides of the plate (main and back), for an 8-mm-thick plate in the transverse rolling direction. The highest values of Ra = 0.48 µm were measured for a 6-mm-thick plate milled with the S#2 strategy, which symmetrically removed material from both sides of the plate, in the longitudinal rolling direction. However, the position of the face cutter axis during the machining process was observed to have a significant effect on the surface roughness. A higher surface roughness was measured in the areas of the tool point transition from the up-milling direction to the down-milling direction (tool axis path) for all analysed strategies (Ra = 0.63–0.68 µm). The best values were obtained for the up-milling direction, but in the area of the smooth execution of the process (Ra = 0.26–0.29 µm), not in the area of the blade entry into the material. A similar relationship was obtained for analysed medians of the arithmetic mean height (Sa) and the root-mean-square height (Sq). However, in the case of the S#3 strategy, the spreads of results were the lowest.

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Tool wear monitoring using a fast Hough transform of images of machined surfaces

Mannan

Mian

Kassim

2004

Machine Vision and Applications

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Surface Roughness Model for Worn Inserts of Face Milling: Part I — Factors that Affect Arithmetic Surface Roughness

Cited by 4 publications

References 4 publications

Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Surface Roughness Evaluation in Thin EN AW-6086-T6 Alloy Plates after Face Milling Process with Different Strategies

Tool wear monitoring using a fast Hough transform of images of machined surfaces

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