Determination of cutting parameters in end milling operation based on the optical surface roughness measurement

Taga, Özer; Kıral, Zeki; Yaman, Kemal

doi:10.1007/s12541-016-0070-4

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…The problems faced by researchers on this subject are varied. Thus, we find systems that perform roughness measurements with an insufficient measurement range, i.e., with a narrow range of the obtained data [21,22]. Moreover, the inaccessibility of the measurement poses a very large problem.…”

Section: Of 12mentioning

confidence: 99%

Effective Detection of the Machinability of Stainless Steel from the Aspect of the Roughness of the Machined Surface

et al. 2023

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Reliable measurement of surface roughness (Ra) is extremely important for quality control of production processes. The cost of the equipment and the duration of the measurement process are very high. The aim of this work is to develop a device for non-destructive measurement of specific roughness levels on stainless steel using computer vision. The device should be structurally simple, affordable, accurate, and safe for practical use. The purpose of the device is to effectively detect the level of roughness of the treated surface obtained by the water jet cutting process. On the basis of the obtained results, it is possible to adjust the parameters during the cutting process. The principle of operation of the device is based on measuring the intensity of the visible spectrum of the light reflected from the surface of the sample to be measured and correlating these values with the values of the measured roughness. After testing several variants of the device, the so-called vertical measurement variant was developed using the following equipment: violet light LED, optical filter and light splitter, USB 2.0 web camera, Arduino microcontroller, personal computer, and LabView programming interface.

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Section: Of 12mentioning

confidence: 99%

Effective Detection of the Machinability of Stainless Steel from the Aspect of the Roughness of the Machined Surface

et al. 2023

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“…The limit value of Ra specified in the product design documentation must be attained, nevertheless tool wear may hamper these efforts at certain stages of production. In the event of such an occurrence, constant monitoring of the state of machined surfaces [3,4,5] enables spotting the defect and applying the most sensible solutioncutting tool change. However, this will inevitably lead to halting the production process and generate delay of machining time of a given lot of machined products.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of milling parameters using neural network

Lipski

Zaleski

2017

ITM Web Conf.

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Abstract. The purpose of this study was to design and test an intelligent computer software developed with the purpose of increasing average productivity of milling not compromising the design features of the final product. The developed system generates optimal milling parameters based on the extent of tool wear. The introduced optimisation algorithm employs a multilayer model of a milling process developed in the artificial neural network. The input parameters for model training are the following: cutting speed vc, feed per tooth fz and the degree of tool wear measured by means of localised flank wear (VB3). The output parameter is the surface roughness of a machined surface Ra. Since the model in the neural network exhibits good approximation of functional relationships, it was applied to determine optimal milling parameters in changeable tool wear conditions (VB3) and stabilisation of surface roughness parameter Ra. Our solution enables constant control over surface roughness parameters and productivity of milling process after each assessment of tool condition. The recommended parameters, i.e. those which applied in milling ensure desired surface roughness and maximal productivity, are selected from all the parameters generated by the model. The developed software may constitute an expert system supporting a milling machine operator. In addition, the application may be installed on a mobile device (smartphone), connected to a tool wear diagnostics instrument and the machine tool controller in order to supply updated optimal parameters of milling. The presented solution facilitates tool life optimisation and decreasing tool change costs, particularly during prolonged operation.

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Investigation into the machining characteristics of AISI 1045 steel and Inconel 718 for an ellipsoidal shape using laser-assisted contouring and ramping machining

Kim¹,

Lee

2017

Int. J. Precis. Eng. Manuf.

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Determination of cutting parameters in end milling operation based on the optical surface roughness measurement

Cited by 8 publications

References 20 publications

Effective Detection of the Machinability of Stainless Steel from the Aspect of the Roughness of the Machined Surface

Effective Detection of the Machinability of Stainless Steel from the Aspect of the Roughness of the Machined Surface

Optimisation of milling parameters using neural network

Investigation into the machining characteristics of AISI 1045 steel and Inconel 718 for an ellipsoidal shape using laser-assisted contouring and ramping machining

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