Non-contact optical three-dimensional measuring, scanning and digitising are increasingly present in quality assurance systems. Simple scanning procedures, high density of data acquired in a single scan, and the possibility of integrated reverse engineering and inspection, are all advantages of optical scanning compared to conventional measuring methods. Due to the three-dimensional acquisition of measuring data, an optical scanner is often considered to be an alternative possibility for coordinate measuring machines. However, the accuracy of the measured data acquired by optical scanning (even with a high-end system) is still far below the level achieved by high-level coordinate measuring machines. This paper examines the possibilities of using a three-dimensional scanner for workpiece inspection. The first part presents a special field of workpiece inspection in which, even with currently achievable accuracy, optical scanning is a viable solution for the inspection of manufactured parts. In addition, the achievable dimensional accuracy of an optical scanner is tested by scanning several gauge blocks. In conclusion, a head to head comparison with a coordinate measuring machine is made by scanning and verifying a sphere.

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In this research study proposed are a response surface methodology (RSM), genetic algorithm (GA) and a grey wolf optimizer (GWO) algorithm for prediction of surface roughness in ball-end milling of hardened steel. The RSM is a conventional predicting approach, GA is an evolutionary algorithm and GWO is a new swarm intelligence-based algorithm. Spindle speed, feed per tooth, axial depth and radial depth of cut were selected as input parameters. Experiments were performed on a CNC milling center and experimental data were collected based on a four-factor-five-level central composite design (CCD). RSM was applied for establishing the basic relationship between input parameters and surface roughness. After that analysis of variance (ANOVA) was conducted for the evaluation of the proposed mathematical model. A predefined reduced quadratic model was used as a reference model for a build-up of predictive models using GA and GWO algorithm. Predicted values of RSM, GA and GWO models are compared with experimental results. In the comparison of model performance for all the three models it was found that GWO model is the best solution. The model accuracy was found to be at 91.80 % and 89.58 % for training and testing data, respectively, which showed the effectiveness of the GWO algorithm for modeling machining processes. References[1] Van Luttervelt, C.A., Childs, T.H.C., Jawahir, I.S., Klocke, F., Venuvinod, P.K., Altintas, Y., Armarego, E., Dornfeld, D., Grabec, I., Leopold, J., Lindstrom, B., Lucca, D.,Obikawa, T., Shirakashi, Sato, H. (1998). Present situation and future trends in modelling of machining operations progress report of the CIRP working group 'Modelling of machining operations',

This paper describes a methodology for uncertainty assessment for Coordinate Measuring Machine measurement of complex real work pieces from industry. The study applied two approaches (in scanning mode only) for estimating the measurement uncertainty with the support of Taguchi plan in the experiment containing five factors: scanning speed, sample density, probe configuration, scanning direction, and position of measuring object. In the first approach the uncertainty was estimated by measuring the basic geometric objects (primitives like sphere and torus) representing the decomposition of complex surfaces and in the second one a complex surface was treated as an unknown quantity. Calculated uncertainty Type A for both measurement tasks was in the range from 0.65 µm to 6.47 µm. Evaluation of the uncertainty Type B covered specifications of the machine and standard uncertainties derived from temperature effects. Total u B component was found to be in order of 0.4 µm. Future research will be directed towards the development and application of simulation methods