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An algorithm for generating an optimum CMM inspection path is developed to improve the throughput of CMMs. In this algorithm, a modified 3-D ray tracing technique is applied to an octree database of a CMM configuration space to detect obstacles between any two target points. After an obstacle is detected, collision-free silhouette contour vertices of the object are generated, from a selection criterion, as potential points of a vertex path. As the ray advances, a sequential-decision-making technique is used to derive the suboptimum vertex path from possible collision-free vertex paths. After the suboptimum vertex path is generated, a selection strategy is employed to ensure a correct edge path sequence for deriving an optimum edge point path. A 3-D simulation shows that the proposed global algorithm eliminates the dynamically undesirable characteristics of octree based algorithms and saves searching time in congested work spaces by finding paths around colliding objects. Actual measurement of a test part indicates that the proposed method can reduce the inspection time to less than half as compared to the interactive graphic method.

A sensitivity analysis of the discrete-to-continuous transformation used in the “indirect” modeling of continuous-time systems from sampled experimental data is presented. For the transformation of poles it is shown that small errors in the discrete-time domain may yield large errors in corresponding continuous-time parameters, such as time constants, natural frequencies, and damping factors, if very fast sampling is used. An important consequence of this phenomenon is the introduction of large errors in the modal parameter estimates of the lower frequency modes of multiple degree-of-freedom systems. In order to alleviate this problem appropriate sensitivity specifications, leading to a lower bound for the allowable values of the sampling period, are introduced. The transformation of residues by the step approximation method is also examined, and, the results of the analysis are finally used for the development of guidelines for the appropriate selection of the sampling period so that the transformation sensitivity be confined within prespecified design limits.

The data used by Tee and Wu [5] for identification of the transfer function and the noise of a Fourdrinier paper machine are reanalyzed taking into account the feedback of the skilled operator. The identification procedure consists of fitting a bivariate time series to the input and output observations. The transfer function and the noise of the paper machine as well as those of the skilled operator are obtained by simple manipulations of the fitted bivariate model. The resulting model is physically interpreted and an attempt is made to locate the primary source of disturbance.

Normal operating record data of a continuous digester pulping process are used to study the possibilities of improving process regulator control with stochastic discrete time series techniques. It is illustrated that inclusion of a secondary input that is uncorrelated with the main input in a univariate process model enables more efficient forecasting of disturbances to be made, resulting in better control for minimum mean squared output. The resulting control scheme is a form of feedforward control which calls for adjustments in only one main process input.

A new method is described to simultaneously model the dynamics and disturbance for a paper-making process from a set of on-line step input response data. The response is decomposed into two parts — a deterministic part represented by the response of an nth order linear system, and a stochastic part characterized by an Autoregressive Moving Average time series model. The differential equations representing the dynamics of the noise are also obtained and discussed. A general modeling strategy and estimation procedure for arbitrary inputs and unkown deal-time is outlined.