This study introduces the improvement of mathematical and predictive models of surface roughness parameter (Ra) in milling AA6061 alloy using carbide cutting tools coated with CVD-TiCN in dry condition. An experimental model has been improved for estimating the surface roughness using artificial neural networks (ANN) and response surface methodology (RSM). For these models, cutting speed, depth of cut, and feed rate were evaluated as input parameters for experimental design. For the ANN modelling, the standard backpropagation algorithm was established to be the optimum selection for training the model. In the forming of the network construction, five different learning algorithms were used: the conjugate gradient backpropagation, Levenberg–Marquardt, scaled conjugate gradient, quasi-Newton backpropagation, and resilient backpropagation. The best consequent with single hidden layers for the surface roughness was obtained by 3-8-1 network structures. The statistical analysis was performed with RSM-based second-order mathematics model. The influences of the cutting parameters on surface roughness were defined by using analysis of variance (ANOVA). The ANOVA results show that the depth of cut is the most effective parameter on surface roughness. Prediction models developed using ANN and RSM were compared in terms of prediction accuracy R2, MEP, and RMSE. The data estimated from ANN and RSM were realized to be very close to the data acquired from experimental studies. The value R2 of RSM model was higher than the values of the ANN model which demonstrated the stability and sturdiness of the RSM method.

This study focuses on the examination of the effect of cutting parameters on surface roughness when drilling medium-density fiberboard (MDF) with a parallel robot. Taguchi technique was applied to find the optimum drilling parameters and, later, the drilling processing. Experimental layout was established using the Taguchi L18 orthogonal array, and experimental data were examined via a statistical analysis of variance (ANOVA). Experimental results were performed by multiple regression analysis (linear and quadratic). Correlation coefficient (R2) was found 99.46% for surface roughness with the quadratic regression model. By the Taguchi analysis, the optimum values for the surface roughness were found to be a point angle of 118°, a cutting speed of 47.1 m/min, and a feed rate of 0.01 mm/rev. The optimization outcomes presented that the Taguchi technique had been successfully performed to decide the optimal surface roughness of the MDF in the drilling.

A 3-axis parallel kinematic machine tool and advanced control system with programming in G-code for the milling of wood material are described in detail. This parallel kinematic machine is based on a 3-PSS (prismatic link, spherical link, and spherical link) parallel mechanism. A programming system and control based on a real-time PC windows platform and Mach3 software system was implemented for this tool. Finally, a model application of a programming system developed for a three-degree-of-freedom linear delta parallel machine was presented, and the workability for milling wood material (medium-density fibreboard) was shown.

Paralel kinematik mekanizmalar ailesinden olan lineer delta robot, 3 adet lineer aktüatör ile hareket ettirilen, bir sabit ve bir hareketli platformdan oluşan bir robottur. Bu çalışmada, lineer delta robotun prototipi yapılarak doğruluk ve tekrarlanabilirlik performansı gerçekleştirilmiştir. Lineer delta robot için deney tasarımı düzeneği kurulmuş ve ISO 9283 standardına göre ortalama doğruluk ve tekrarlanabilirlik parametreleri Microscribe G2X koordinat ölçme cihazı kullanılarak robotun performansı bulunmuştur. Yapılan testlerde robotun ortalama doğruluğu 1,25 mm ve ortalama tekrarlanabilirliği ise 1,45 mm bulunmaktadır. Sonuç olarak yapılan testler açık çevrim kontrol metoduyla yapıldığı için literatürdeki çalışmalara benzerlik göstermektedir.