Performance evaluation of chip breaker utilizing neural network

Kim, Hong-Gyoo; Sim, Jaesung; Kweon, Hyeog-Jun

doi:10.1016/j.jmatprotec.2008.02.064

Cited by 33 publications

(15 citation statements)

References 7 publications

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“…Metal cutting processes can be subdivided into three phases [5,6]: (i) chip formation phase (material layer, which is to be removed, separates from the workpiece); (ii) chip flow phase (material becomes chip and flows parallel to tool rake face); and (iii) chip curl phase (chip looses contact to tool and curls away). Under certain circumstances, chip breakage may occur in a fourth phase.…”

Section: Empirical Models Of Chip Breakagementioning

confidence: 99%

“…Despite its high relevance for metal cutting, the design of chip breakage properties of cutting tools is still dominated by cost intensive and inflexible empirical approaches based on the "trial and error" principle [5]. In this context, predictive methodologies help to create remedy by reducing the required amount of cutting experiments in the course of cutting tool design.…”

Section: Introductionmentioning

confidence: 99%

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3D FEM simulation of chip breakage in metal cutting

Buchkremer

Klocke

Veselovac

2015

Int J Adv Manuf Technol

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Section: Empirical Models Of Chip Breakagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D FEM simulation of chip breakage in metal cutting

Buchkremer

Klocke

Veselovac

2015

Int J Adv Manuf Technol

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“…Long chips curl around the tool and can pose serious hazards to the workpiece surface, the operator and the machine-tool operations. The situation becomes more critical under the environment of automated machine loading, unloading and in process inspection of the machined parts [46,47]. To overcome this difficulty, a number of researchers have investigated effective control of chip flow and chip breaking.…”

Section: Grooved Tools (Restricted Contact Tools)mentioning

confidence: 99%

“…As a result, the performance evaluation method has been developed and applied to commercial tools, which resulted in excellent performance. Further author suggested that if the training data in the neural network is collected with greater consideration given to the effect of cutting conditions and the performance of chip breakers, it can be used for the design of chip breakers in the future [47]. On the basis of Lee and Shaffer's model, author presents an analytical slip-line approach to investigate how the negative tool rake angle and the cutting speed affect the tool-chip friction, and how the tool-chip friction further affects machining performances, such as the ratio of the cutting force to the thrust force, the chip thickness ratio, the geometry of the shear zone, and the geometry of the stagnation zone of material flow adjacent to the tool rake face.…”

Section: Modeling Techniques With Tool Geometrymentioning

confidence: 99%

Untitled

2011

JESTR

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The effect of cutting tool geometry has long been an issue in understanding mechanics of turning. Tool geometry has significant influence on chip formation, heat generation, tool wear, surface finish and surface integrity during turning. This article presents a survey on variation in tool geometry i.e. tool nose radius, rake angle, groove on the rake face, variable edge geometry, wiper geometry and curvilinear edge tools and their effect on tool wear, surface roughness and surface integrity of the machined surface. Further modeling and simulation approaches on tool geometry including one approach developed in a recent study, on variable micro-geometry tools, is discussed in brief.Keywords: Curvilinear edge, rake angle, grooved tools, nose radius, variable geometry. Stringent control on the quality of machined surface and sub-surface during turning is most important consideration a part from considering the tool life. In order to attain sufficiently high production rates at minimum cost, optimization of cutting tool geometry is necessary [1], [2]. The design of cutting edge geometry and its influence on machining performance have been a research topic in metal cutting for a long time. Edge preparation has a critical effect on the tool life. A tool with poor edge preparation may chip and fail quickly [3]. Users expect to have more and more productivity in their machining processes (high removal rate of work-material) and low wear of their cutting tools (long tool life). These demands require major improvements in the design of cutting tools: new substrates, new coatings, cutting tool geometry and materials etc. According to tool manufacturers, the manufacturing procedures of their cutting tools, especially the micro-geometry preparation (cutting edge etc.), have a major influence on their performance and on their reliability [4]. Edge preparation must be carefully selected for a given application because it affects the surface integrity of the machined workpiece (white layer, residual stresses etc.) [5]. Heat generated during hard turning is also affected by edge preparation due to change in work material flow around the cutting edge. For example, a chamfered face provides excessive negative angle to the cutting action and results in high heat generation. PCBN tools rapidly wear out during hard turning at high cutting speeds mainly due to attained temperatures [6].It is important to consider the tool-edge effect in order to better understand the chip formation mechanism and accurately predict machining performances, such as cutting forces, cutting temperatures, tool wear, surface finish and the machined surface integrity. The methods commonly employed include experimental, analytical, and numerical methods [7]. The tool geometry effects on turning performance parameters are mentioned in Figure1.It is demonstrated that the cutting tool edge geometry significantly influences many fundamental aspects such as cutting forces, chip formation, cutting temperature, tool wear, tool-life and characteristics like sur...

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“…Compared with traditional computing methods, the artificial neural networks have been applied as an effective and an alternative method for the experimental studies that the mathematical model cannot be formed in recently. In the field of cutting, the model of the chip break [3], surface roughness [4] and tool wear [5] based on the neural network have been successfully established. ANNs-based models seem to obtain improved and acceptable performance in cutting operation forecasting issue, however, the conventional ANNs still suffer from several weaknesses such as the need for a large number of controlling parameters, the difficulty in obtaining stable solutions, the danger of over-fitting and thus the lack of generalization capability.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Cutting Temperature Prediction Using Support Vector Regression and Ant Colony Optimization

Zeng

Liu

Jiang

et al. 2012

AEF

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Abstract:In this paper, support vector regression with ant colony optimization is presented for the prediction of tool-chip interface temperature depends on cutting parameters in machining. Ant colony (ACO) optimization was developed to optimize three parameters of SVR, including penalty parameter C, insensitive loss function ε and kernel function σ. SVR constructs hyperplane in high dimension space and fits the data in non-linear form. Normalized mean square error (NMSE) of fitting result is used as target of ant colony optimization. ACO finds the best parameters which correspond to the NMSE. The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model tool-chip interface temperature with good predictive accuracy.

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Performance evaluation of chip breaker utilizing neural network

Cited by 33 publications

References 7 publications

3D FEM simulation of chip breakage in metal cutting

3D FEM simulation of chip breakage in metal cutting

Untitled

A New Approach to Cutting Temperature Prediction Using Support Vector Regression and Ant Colony Optimization

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