Selection of an optimum sample size for flatness error estimation while using coordinate measuring machine

In the present day manufacturing environments it is becoming increasingly important to be able to deliver quality products at the right time to the market at competitive costs. The quality, cost and time to market depend not only on the design and manufacturing but also on the inspection process adopted. Design specifications rely on extensive usage of form tolerances to ensure that the functionality of surfaces and features of the product are maximized. The use of the coordinate measuring machines (CMM) has greatly improved the efficacy of form tolerance measurement and is also used as the key device in this work. The focus of this work is to deal with the method and strategies for measurement of flatness error so as to be able to predict the flatness error accurately at reduced sample sizes in batch and mass production setups. Accurate evaluation of flatness will require large sample sizes which increase the cost and time of inspection and hence a need to reduce the sample sizes without compromising on the accuracy. In the absence of robust models that can predict the errors due to manufacturing processes, an alternative technique has been devised to arrive at a reduced sample size. The procedure involves using large sample data inspected on the first component as the basis for evolving smaller sample sizes for subsequent components. Experimental verification of the developed algorithm shows that flatness error can be predicted with sufficient accuracy at small sample sizes.