There have been substantial improvements in measurement systems in order to meet fluctuating market demands. This rapid change and development in measurement technology has primarily been governed by demands of accuracy and precision from aerospace, automotive and other manufacturing industries. Coordinate measuring machines (CMMs) available with different technologies and configurations has efficiently been fulfilling customer demands for more than a decade. Though, current CMMs can meet needs of rapid and increased demands of customers to a greater extent but still there is lot of scope for improvement and development in CMMs. The globalization of manufacturing has resulted in development of variety of complex products and miniaturization of mechanical components. The technology of micro/nano-scale 3D measurement is still a bottleneck for industries. Therefore, precise and accurate system which is flexible enough to deal with complexities of parts and micro & nano range products has to be investigated. In this paper, comprehensive review concerning CMMs with capabilities to measure micro/nano features has been presented. This work has also discussed different methods to estimate measurement uncertainty, as well as performance evaluation of CMMs. Moreover, novel concepts such as intelligent CMM, multi-sensor CMM, virtual CMM have been presented.

Mean residual life and failure rate functions are ubiquitously employed in reliability analysis. The term of useful period of lifetime distributions of bathtub-shaped failure rate functions is referred to the flat rigion of this function and has attracted authors and researchers in reliability, actuary, and survival analysis. In recent years, considering the change points of mean residual life and failure rate functions has been extensively utelized in determining the optimum burn-in time. In this paper we investigate the difference between the change points of failure rate and mean residual life functions of some generalized gamma type distributions due to the capability of these distributions in modeling various bathtub-shaped failure rate functions.

The Total Inertial Steering approach proposed in this paper can perform an optimum correction of the geometric deviations of the manufactured part with respect to its digital model, from the measured points on its surfaces. In the case of production by machine tool numerical control, there exist a link between the tool offsets and deviations of measured points. An incidence matrix which represents this link is obtained. In most cases, this matrix is not square and therefore not invertible, because there are more measured points as correctors to adjust. The Gauss pseudo-inverse is used to calculate the values of corrections to be made to compensate for measured deviations. Tolerances associated with the surfaces must also be taken into account in the incidence matrix. However, when the same cutting tool machine two surfaces with different point values, the resulting solution favors the one with the highest number of points, at the expense of the other surface which can remain not conform towards its tolerance. This paper proposes a strategy to rebalance the correction surfaces, and this regardless of the number of points and tolerance of each surfaces. A relatively simple tutorial example is given in the paper to enable tracking calculations.

Today a wide range of instruments are available for the rapid roughness quantification of optical surfaces but especially for three dimensional measurement methods no standardized process is established. This leads to different results, even if the same specimen is tested with similar measurement devices. In order to solve this problem an exemplary process development is described in this paper. To do this firstly the term of roughness is defined as a surface deviation and then the functionality and importance of filters in roughness measurement as well as the used measurement devices are described. The following chapter defines the used materials and methods which were used during the measurement process. In the last part of this paper the results are discussed and a process assignment is suggested.

Plasma etching process plays a critical role in semiconductor manufacturing. Because physical and chemical mechanisms involved in plasma etching are extremely complicated, models supporting process control are difficult to construct. This paper uses a 35-run D-optimal design to efficiently collect data under well planned conditions for important controllable variables such as power, pressure, electrode gap and gas flows of Cl2 and He and the response, etching rate, for building an empirical underlying model. Since the relationship between the control and response variables could be highly nonlinear, a generalized regression neural network is used to select important model variables and their combination effects and to fit the model. Compared with the response surface methodology, the proposed method has better prediction performance in training and testing samples. A success application of the model to control the plasma etching process demonstrates the effectiveness of the methods.