Prediction and Optimization of Tool Life in Micromilling AISI D2 (∼62 HRC) Hardened Steel

Saedon, Juri; Soo, Sein Leung; Aspinwall, D.K.; Barnacle, A.; Saad, Nor Hayati

doi:10.1016/j.proeng.2012.07.367

Cited by 30 publications

(19 citation statements)

References 9 publications

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“…For micro and macro milling operations different modeling techniques have been applied for tool wear or part quality estimation and they include response surfaces [8,[22][23][24][25], and Artificial Intelligence (AI) models such as Artificial Neural Networks (ANN) [8, 11-13, 15, 23-27], Adaptive Neuro Fuzzy Inference Systems (ANFIS) [28,29], Fuzzy Systems [12,30,31], Hidden Markov Models (HMM) [24], Bayesian Networks (BN) [32,33] and Least Squares Support Vector Machines (LS-SVM) [34][35][36]. Comprehensive reviews about modeling techniques applied in machining can be found in [19,37,38].…”

Section: Estimation Modulementioning

confidence: 99%

“…Micro channels were machined until the geometric error exceeded specifications. In Saedon et al [22] the channel width was used as an indirect measurement of tool wear and a reduction of 30 µm in width was defined as a criterion for tool life end. A similar practice is followed in this research, and the reach of a 6% of width and a 10% of form errors were used as tool life end criteria, i.e, as cutting-tool replacement policy.…”

Section: Figure 9 Design Of Experiments Used For Modeling Width and mentioning

confidence: 99%

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Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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Section: Estimation Modulementioning

confidence: 99%

Section: Figure 9 Design Of Experiments Used For Modeling Width and mentioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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show abstract

“…Studies related to the proper selection of the parameters and monitoring of machining forces based on the temperature can be conducted with the goal of maximizing the material removal rate. Similarly, the analysis of cutting forces allows an overview of the machining process in order to evaluate the level of tool wear and resulting surface finish Milling of hardened steel components provides substantial benefits in terms of reduced manufacturing cost and production time when compared to traditional route consisting of machining in the annealed condition, heat treatment, electrical discharge machining, grinding and manual polishing 2,3 . A large number of studies related to the machining of hardness steels mainlydeals with the manufacture a wide range of molds and dies.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of AISI D2 tool steel, its machinability is approximately 30-40% of AISI B1112 free cutting steel 19 . Using FEM simulation, Seadon et al, 2 asserted that tool life criterion is one of the main aspects used to evaluate the performance of cutting tools in material removal operations. The authors used the analysis of variance and concluded that cutting speed is the predominant factor followed by feed rate and depth of cut.…”

mentioning

confidence: 99%

Machinability Evaluation in Hard Milling of AISI D2 Steel

Gaitonde¹,

Karnik²,

Maciel

et al. 2016

Mat. Res.

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Milling of hardened steel components provides considerable benefits in terms of reduced manufacturing cost and time compared to traditional machining. Temperature variation in milling is an important factor affecting the wear of cutting tools. The poor selection of milling parameters may cause excessive tool wear and increased work surface roughness. Hence, there is a need to study the machinability aspects during milling of hardened steel components. In the present work, influence of cutting speed, feed rate and radial depth of cut on milling temperature, surface roughness and cutting force during milling of AISI D2 steel has been investigated using response surface methodology (RSM) based models. From the parametric analysis, it is revealed that temperature increases linearly, whereas surface roughness increases non-linearly with cutting speed. However, for higher values of feed rate, an increased cutting speed considerably reduces the cutting force for specified depth of cut range. The present work also reveals that the selection of best cutting conditions is useful at the CAPP stage in the milling process particularly with tight tolerances.

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“…Saedon et al [21] used a Response Surface Methodology to define the optimum input parameters to predict the tool wear, and tool life. According to the authors, the RSM was a good tool to estimate the region of maximum tool life.…”

Section: Introductionmentioning

confidence: 99%

Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

et al. 2016

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Prediction and Optimization of Tool Life in Micromilling AISI D2 (∼62 HRC) Hardened Steel

Cited by 30 publications

References 9 publications

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Machinability Evaluation in Hard Milling of AISI D2 Steel

Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

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