Abstract:It is estimated that around 65% of the cost of a die or mould is related to the machining processes. Moreover, the literature says that 70% of the time spent in the machining processes of this kind of parts is used in finishing and semi-finishing operations. The high complexity of the machined surfaces makes mandatory the use of ball nose tools, with large overhang, what increases vibration in the process. These problems have to be minimized, since dies and moulds demand a very good surface finish and tight di… Show more
“…According to Fallböhmer and Scurlock [34], cutting with a tool with a small level of wear may generate lower roughness than cutting with a fresh tool. Diniz et al [35] found similar results in the milling of H13 steel with a toroidal tool in semi-finishing conditions. A possible explanation for these results is that roughness values may be associated with tool coating defects on the cutting edge, as cited by Oliveira [13], which affect roughness at the beginning of tool life.…”
“…According to Fallböhmer and Scurlock [34], cutting with a tool with a small level of wear may generate lower roughness than cutting with a fresh tool. Diniz et al [35] found similar results in the milling of H13 steel with a toroidal tool in semi-finishing conditions. A possible explanation for these results is that roughness values may be associated with tool coating defects on the cutting edge, as cited by Oliveira [13], which affect roughness at the beginning of tool life.…”
“…Under the optimized cutting conditions, the surface roughness was less than 0.25 µm. Diniz et al [4] achieved about 0.8 µm surface roughness value in the toroidal milling of hardened SAE H13. Karkalos et al [30] obtained 0.19 µm surface roughness value when milling Ti-6Al-4V ELI alloy.…”
Section: Optimization For Ramentioning
confidence: 99%
“…The result of their research indicates that the surface roughness under the optimized cutting parameters is less 0.25 µm. Diniz et al [4] studied the toroidal milling of hardened SAE (Society of Automotive Engineers) H13 alloy steel using the minimum quantity of lubricant (MQL) technique. Their result indicates that the surface roughness value is 0.8 µm.…”
Abstract:Hard machining is an efficient solution that can be used to replace the grinding operation in the mold and die manufacturing industry. In this study, an attempt is made to analyze the effect of process parameters on workpiece surface roughness (Ra) in the hard milling of JIS (Japanese Industrial Standard) SKD61 steel, based on a combination of the Taguchi method and response surface methodology (RSM). The cutting parameters are selected based on the structural dynamic analysis of the machine tool. A set of experiments is designed according to the Taguchi technique. The average Ra is measured by a Mitutoyo Surftest SJ-400, and then analysis of variance (ANOVA) is performed to determine the influences of cutting parameters on the given Ra. Quadratic mathematical modeling is introduced for prediction of the Ra during the hard milling process. The predicted values are in reasonable agreement with the observation of experiments. In an effort to obtain the minimizing Ra, a single objective optimization is employed based on the desirability function. The result shows that the percentage error between measured and predicted values of Ra is 3.2%, which is found to be insignificant. Eventually, the milled surface roughness under the optimized machining conditions is 0.122 µm. This finding shows that grinding may be replaced by finish hard milling in the mold and die manufacturing field.
“…In this sense, molds and dies industry present important impact in the competitiveness of the forming processes. In a mold manufacturing it is estimated that 65% of the costs are due to finishing and semi-finishing processes by machining [2,3]. Between these processes, hole-making spends from 25% to 50% of the cycle time and 33% of the total number of operations, requesting reliability due to the high added value to the part being processed [4,5].…”
Helical milling is a hole-making process which has been applied in hardened materials. Due to the difficulties on achieving high-quality boreholes in these materials, the influence of noise factors, and multi-quality performance outcomes, this work aims the multi-objective robust design of hole quality on AISI H13 hardened steel. Experiments were carried out through a central composite design considering process and noise factors. The process factors were the axial and tangential feed per tooth of the helix, and the cutting velocity. The noise factors considered were the tool overhang length, the material hardness and the borehole height of measurement. Response models were obtained through response surface methodology for roughness and roundness outcomes. The models presented good explanation of data variability and good prediction capability. Mean and variance models were derived through robust parameter design for all responses. Similarity analysis through cluster analysis was realised, and average surface roughness and total roundness were selected to multi-objective optimisation. Mean square error optimisation was performed to achieve bias and variance minimization. Multi-objective optimisation through normalized normal constraint was performed to achieve a robust Pareto set for the hole quality outcomes. The normalized normal constraint optimisation results outperformed the results of other methods in terms of evenness of the Pareto solutions and number of Pareto optimal solutions. The most compromise solution was selected considering the lowest Euclidian distance to the utopia point in the normalized space. Individual and moving range control charts were used to confirm the robustness achievement with regard to noise factors in the most compromise Pareto optimal solution. The methodology applied for robust modelling and optimisation of helical milling of AISI H13 hardened steel was confirmed and may be applied to other manufacturing processes.
KeywordsHelical milling; AISI H13 hardened steel; Multi-objective robust optimization; Robust parameter design; Normalized normal constraint method.
R 2 coefficient of determinationRadj 2 adjusted coefficient of determination Radj 2 prediction coefficient of determination
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