Computer Numerical Control (CNC) is a manufacturing concept where machine tools are automated to perform some prede, ned functions based on the instructions fed to them. CNC turning processes have found wide-ranging applications in modernday manufacturing industries due to their capabilities to produce low-cost high-quality parts/components with very close dimensional tolerances. In order to exploit the fullest potential of a CNC turning process, its di, erent input parameters should always be set to the optimal level for operation. In this paper, two classi, cation tree algorithms, i. e., Classi, cation And Regression Tree (CART) and CHi-squared Automatic Interaction Detection (CHAID) are applied to study the e, ects of various turning parameters on the responses and identify the best machining conditions for a CNC process. It is perceived that the obtained settings almost match with the observations of the earlier researchers. The CART algorithm outperforms CHAID with respect to higher overall classi, cation accuracy and lower prediction risk.

Various methods for PARAMETRIC INTERPOLATION of NURBS curves have been proposed in the past. However, the errors caused by the approximate nature of the NURBS interpolator were rarely taken into account. This paper proposes an integrated look-ahead algorithm for PARAMETRIC INTERPOLATION along NURBS curves. The algorithm interpolates the sharp corners on the curve with the Pythagorean-hodograph (PH) INTERPOLATION. This will minimize the geometric and interpolator approximation errors simultaneously. The algorithm consists of four different modules: a sharp corner detection module, a PH construction module, a feedrate planning module, and a dynamics module. Simulations are performed to show correctness of the proposed algorithm. Experiments on an X-Y table confirm that the developed method improves contour accuracy significantly compared to previously proposed adaptive-feedrate and curvature-feedrate algorithms.

In this paper we present a new method for tool-path generation and adaptive INTERPOLATION for a three-axis CNC milling machine. This method satisfied the force and / or the error conditions by using a variable-direction variable-feedrate strategy based on Maximum Feedrate Map. The proposed adaptive interpolator modifies the cutting path and the moving direction according to the actual instantaneous position error in real time. It also adaptively slows down the speed of the cutting tool if the position error exceeds a permissible threshold so as to decrease the error. As the cutting points on the spline at each cycle are not calculated beforehand, memory requirement for the interpolator drastically reduces. The significance of the proposed method on the reduction of the cutting error in the presence of disturbance is demonstrated via computer simulations.

The aim of this study was to investigate the factors affecting surface quality of paulownia wood during milling by advanced computer numerical control (CNC) machine. For this aim wood logs converted to proper sizes (2.5x10x15 cm) and then air dried. The variable of this study were spindle speed of machine (10000 and 18000 rpm), feeding rate (6 and 12 m/min), cutting depth (1and 5 mm), cutting method (down and up-milling) and cutting pattern (tangential and radial). Roughness of cut specimens edge were evaluated by profilometer method according to ISO 13565 standard. For evaluation of surface quality Abbott group parameters (Rpk, Rk, Rvk) were used. Degree of effective of the parameters was evaluated by fractional factorial design as completely random design at confidence level of 95%. The result showed that spindle speed, cutting method and feed rate are influencing factors on surface quality of milled specimens and their effect was significant. With increasing spindle speed and decreasing feeding rate the roughness decreased and surface quality improved. In up-milling cutting method, degree of roughness was higher and consequently surface quality was inferior. Cutting method in comparison to other factors had the high influence on surface quality. The rest variable did not have independent influence on surface quality at 95% confidence level.

Cellulose nanocrystals (CNCs) are charged nanoparticles with a high aspect ratio derived from the most common biological polymer, cellulose. Acid hydrolysis is one of the most common methods for CNC production. Whatman #1 filter paper was hydrolyzed by sulfuric acid and characterized by AFM in this research to examine the morphology and size distribution of CNCs. One drop of CNC suspension was air-dried on microscope cover glass to be analyzed by AFM with non-contact mode. The CNC dimensions were determined by measuring individual and isolated particles (n=88) via Nanosurf Easyscan2 software. The measurement data was analyzed by Excel statistically. Synthesized CNCs were ellipsoidal with the length, height, and aspect ratio of 219.87 ± 42.12, 6.25 ± 2.27, and 41.17 ± 21.70 nm, respectively. Although the length and height of the produced CNCs were in acceptable range, but the width of CNCs was an overestimation and it was not reliable, mostly due to AFM tip broadening effect. Particle size measurement of CNCs is a challenging process because of their rapid aggregation and rod shape. Although hydrolysis parameters are influential on the final shape and size of CNCs, but it is necessary to optimize and maximize the quality of sample preparation and AFM adjustment to obtain the size of CNCs with the most accuracy and reliability. The CNC dimensions were determined by AFM are slightly different in the literature but height (thickness) is the most reliable one based on this experiment. Further studies are required to standardize CNC size measurement by AFM.

In this paper, an optimal Electrical Cam (Ecam) profile is obtained by identifying the best breakpoint positions for piecewise polynomials using the cubic spline INTERPOLATION method. To achieve a curve that best tracks the reference Ecam curve, the breakpoint positions are determined using particle swarm optimization with random inertia weight (RNW-PSO). The previous programmable logic controller (PLC) used in the sanding mechanism was the DELTA DVP40ES2, utilizing the Ecam capability of DELTA ASD-A2 servo motors. To implement the Ecam function independently of the servo motor type, it has been integrated into a PLC, specifically the SIEMENS SIMATIC CPU 1215C. The optimized Ecam curve is then applied to a computer numerical control (CNC) sanding machine. Practical results demonstrate the effectiveness of the proposed method, showing improved sanding quality and better compliance with the reference curve.