Development and application of an ultra-precision lathe

Kim, Jeong‐Du; Kim, Joo Young

doi:10.1007/bf01305868

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…The ratio of data sets' number is determined by r parameter. [6] According to problem condition, simplified relation of r is given in (4) where W is the number of free parameters of network, that is the number of synaptic weights plus the number of biases. By calculating r, 92 percent of data is used in training set, 4 percent in testing, and 4 percent in cross-validation set.…”

Section: Methodsmentioning

confidence: 99%

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Thermal Errors Modeling of a CNC Machine’s Axis Using Neural Network and Fuzzy Logic

Eskandari

Arezoo

Abdullah

2011

AMM

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Thermal errors of CNC machines have significant effects on precision of a workpiece. One of the approaches to reduce these errors is modeling and on-line compensating them. In this study, thermal errors of an axis of the machine are modeled by means of artificial neural networks along with fuzzy logic. Models are created using experimental data. In neural networks modeling, MLP type which has 2 hidden layers is chosen and it is trained by backpropagation algorithm. Finally, the model is validated with the aid of calculating mean squared error and correlation coefficients between outputs of the model and a checking data set. On the other hand, an adaptive neuro-fuzzy inference system is utilized in fuzzy modeling which uses neural network to develop membership functions as fuzzifiers and defuzzifiers. This network is trained by hybrid algorithm. At the end, model validation is done by mean squared error like previous method. The results show that the errors of both modeling techniques are acceptable and models can predict thermal errors reliably.

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Section: Methodsmentioning

confidence: 99%

“…[3] One of the methods to solve this problem is using materials like concrete or reinforced plastic in the structure of machines. [4] Although these methods are effective in reducing the thermal errors, they could be very expensive in precise machining. One of the economical ways is to compensate the error.…”

Section: Introductionmentioning

confidence: 99%

Thermal Errors Modeling of a CNC Machine’s Axis Using Neural Network and Fuzzy Logic

Eskandari

Arezoo

Abdullah

2011

AMM

View full text Add to dashboard Cite

show abstract

“…These considerations suggest to use particular structural materials (i.e. cement concrete, fibre reinforced plastics) able to reduce the deformation due to temperature changes, or to study advanced techniques that permit to compensate thermal errors analysing temperatures of machine critical points [15][16][17][18].…”

Section: Main Issues In Uhp Machine Tool Designingmentioning

confidence: 99%