Reducing the latency between machining and measurement using FEA to predict thermal transient effects on CMM measurement

Coordinate Measuring Machines (CMM) are widely used to validate the dimensional conformance of machined components. Since time frames are critical in ensuring cost effective production, it is important to know when a machined component is ready for dimensional inspection. A common approach is to delay the inspection until the component has reached thermal stabilization such as 20 degrees C as standard, however, this latency incurs an uncertainty of getting either under or overestimated which can result in a considerable waste of time and resources lowering cost effectiveness. Conversely, premature measurement could also increase the measurement uncertainty if significant temperature gradients exist. This paper presents an approach to predict the required latency at which the machined component is within critical thermal boundary conditions and ready for dimensional checks. The approach is comprised of experimental tests required to obtain key boundary conditions including convective and Thermal Contact Conductance (TCC) values. FEA was then used to simulate and validate the latency based on anticipated workpiece conditions. The method was validated on an industrial part, namely a Venturi vessel, in two typical scenarios i. e. placed on the (1) Chuck and (2) CMM to predict the latency required for thermal stabilization using a pre-selected part tolerance level of 1.5 mm. The validation revealed 8.3 min for (1) and 7.6 min for (2) which is a considerable reduction of latency in comparison to waiting for longer periods. This technique was also compared with a typical CMM measurement regime of using a single temperature sensor for compensation. A simple comparison yielded an over or under compensation of 7 mm depending on the position of temperature sensor on the vessel surface. The validations have allowed the formation of a platform for the FEA software to be effectively used to predict the required latency to improve the management of CMM machine usage while ensuring low uncertainty and a cost effective solution. (C) 2019 The Authors. Published by Elsevier Ltd.