Optimization of surface roughness in an end-milling operation using nested experimental design

Patel, Kandarp; Batish, Ajay; Bhattacharya, Anirban

doi:10.1007/s11740-009-0177-x

Cited by 10 publications

(6 citation statements)

References 14 publications

(13 reference statements)

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“…Zuperl et al [8], Tandon et al [14] 2002, Mounayri et al [20] Have predicted force using ANN and optimize MRR Prakasvudhisarn et al [45] Developed a surface roughness model using SVMs and optimized.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…Mounayri et al [20] developed force model using ANN and extended the PSO technique to 3D and 3(1/2) D optimization with 36% reduction in machining time. Prakasvudhisarn et al [45] developed a machine learning support vector machines (SVMs) for estimation of surface roughness and optimized the machining parameters using PSO technique for minimum surface roughness. Tandon et al [14] minimized unit time by 35% by optimization of machining parameter using PSO technique.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…After development of regression model for cutting forces, the abnormal data was filtered using ANN and more accurate results haveT beenT obtained.T AykutT etT al.T[32]T developedT cuttingT forceT modelT usingT DOET andT ANNT andT providedT effectT ofT machinabilityT onT chipT removalT cuttingT parametersT forT faceT milling.T WhereasT theT OnwuboluT etT al.T[39]T developT theT modelT forT toolT wearT andT foundT moreT realisticT resultsT usingT enhanced-groupT methodT ofT dataT handling (GMDH) learning network and DOE. Prakasvudhisarn et al[45] developed a machine learning support vector machines (SVMs) for estimation of surface roughness. Yazdi et al[54] developed surface finish model using ANN & RSMT toT predictT theT surfaceT finishT andT foundT itT significantT withT feedT perT toothT andT speed.T Whereas,T MRRT foundT significantT withT depthT ofT cutT andT feedT perT tooth.…”

mentioning

confidence: 99%

See 2 more Smart Citations

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

Kumar¹,

Gupta²,

Chandna³

2019

IJEAT

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Optimization of machining parameters becomes more important; when high capital cost NC machines are employed for high precision and efficient machining. Minimizations of unit cost and time along with minimum tool and workpiece deflection, improved surface finish & tool life under certain boundary conditions are key objectives of the optimization problem. Optimization methods for milling include in-process parameters relationship with machining objectives and determination of optimal cutting conditions. Development of costeffective mathematical models is still a challenging task. However, there has been a considerable improvement in the techniques of modeling and optimization during the last two decades. In this paper, several modeling and optimization techniques reported for the milling operations have been reviewed and are for milling, classified for different criteria. Issues related to performance of several evolutionary algorithms, machining parameters, objectives and constraints have also been identified. From the survey of optimization techniques during milling operations it has been found that search techniques perform better than experimental approaches for optimization of process parameters. However, the experimental techniques play a vital role in prediction models for different machining objectives

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

Kumar¹,

Gupta²,

Chandna³

2019

IJEAT

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“…Because of recent developments in milling technology, surfaces have become more accurate, which is mainly caused by the application of high speed cutting (HSC) milling processes [22]. That leads to the fact that milled surfaces are widely employed while assembling them with other parts [15].…”

Section: Generation Of Technology Bundlesmentioning

confidence: 99%

Integrative technology chain design for small scale manufacturers

Klocke

Arntz

Heeschen

2014

Prod. Eng. Res. Devel.

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The global manufacturing industry has changed over the years. Individual customer requirements result in multi-variant products characterized by small lot sizes. Up to now, the manufacturing industry has not found an adequate solution for producing small lot sizes in an economic way. Due to this fact, the industry could not adapt the methods defined by Taylor, Ford or even Toyota which are commonly named by industrialization. Although the tool machine industry provides flexible machine centers, this is just a small step towards an industrialized small scale manufacturing. Furthermore the manufacturing industry is facing an increasing international competition due to globalized markets. This leads to an increasing cost pressure. Small scale manufacturers have to transform their workshops into industrialized production facilities characterized by standardized production flows as well as balanced production lines. This paper introduces a multiple-criteria approach for the development of standardized technology chains as a prerequisite for standard production lines and flows for small scale manufacturers using a high number of different manufacturing technologies and consequently characterized by low production flow standardization. Based on a comprehensive, structured workpiece analysis an integrated technology-oriented method was developed for the design of standard technology chains as a prerequisite for standardized production flows. The method was already applied in the industry and has shown a significant increase of production efficiency in terms of lead time and overall product costs

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“…Furthermore, variations of the flank twist and the tip relief have been investigated [3]. Influences of surface roughness on the gear noise as shown in [4] and [5] have not been considered in the designing process. Focusing on two helical gear variants the results of the investigation will be presented.…”

Section: Gear Setsmentioning

confidence: 99%

Dynamic transmission error measurements of a drive train

Brecher

Gorgels

Hesse

et al. 2011

Prod. Eng. Res. Devel.

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Due to generally reducing the noise level of machines and automotive applications gear noise optimization becomes more important. To optimize the excitation of the gearing in the development process, tooth contact analysis software is used. The transmission error is most common to characterize the noise excitation of gear sets. Usually in automotive applications gear noise is influenced by the entire drive train system with all its components. Therefore, it is necessary to asses if the transmission error of the gear set correlates to the noise of the drive train. Aim of a project funded by the German Research Foundation (DFG) was to investigate the possibility to measure the transmission error for every step of assembly. The transmission error was measured for a gear set, the transmission and the drive train. The research object was the drive train of a light truck. This report presents the results of the completed project.

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Optimization of surface roughness in an end-milling operation using nested experimental design

Cited by 10 publications

References 14 publications

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

State of Art Optimization Techniques for Machining Parameters Optimization during Milling

Integrative technology chain design for small scale manufacturers

Dynamic transmission error measurements of a drive train

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