There have been substantial improvements in measurement systems in order to meetfluctuating market demands. This rapid change and development in measurement technologyhas 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 fulfillingcustomer demands for more than a decade. Though, current CMMs can meet needs of rapid andincreased demands of customers to a greater extent but still there is lot of scope forimprovement and development in CMMs. The globalization of manufacturing has resulted indevelopment 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 complexitiesof parts and micro & nano range products has to be investigated. In this paper,comprehensive review concerning CMMs with capabilities to measure micro/nano features hasbeen presented. This work has also discussed different methods to estimate measurementuncertainty, as well as performance evaluation of CMMs. Moreover, novel concepts such asintelligent CMM, multi-sensor CMM, virtual CMM have been presented.

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

The Total Inertial Steering approach proposed in this paper can perform an optimumcorrection of the geometric deviations of the manufactured part with respect to itsdigital model, from the measured points on its surfaces. In the case of production bymachine tool numerical control, there exist a link between the tool offsets and deviationsof measured points. An incidence matrix which represents this link is obtained. In mostcases, this matrix is not square and therefore not invertible, because there are moremeasured points as correctors to adjust. The Gauss pseudo-inverse is used to calculate thevalues of corrections to be made to compensate for measured deviations. Tolerancesassociated 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, theresulting solution favors the one with the highest number of points, at the expense of theother surface which can remain not conform towards its tolerance. This paper proposes astrategy to rebalance the correction surfaces, and this regardless of the number of pointsand tolerance of each surfaces. A relatively simple tutorial example is given in the paperto enable tracking calculations.

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

Plasma etching process plays a critical role in semiconductor manufacturing. Becausephysical and chemical mechanisms involved in plasma etching are extremely complicated,models supporting process control are difficult to construct. This paper uses a 35-runD-optimal design to efficiently collect data under well planned conditions for importantcontrollable variables such as power, pressure, electrode gap and gas flows ofCl2 and He andthe response, etching rate, for building an empirical underlying model. Since therelationship between the control and response variables could be highly nonlinear, ageneralized regression neural network is used to select important model variables andtheir combination effects and to fit the model. Compared with the response surfacemethodology, the proposed method has better prediction performance in training and testingsamples. A success application of the model to control the plasma etching processdemonstrates the effectiveness of the methods.