Automated planning to minimise uncertainty of machine tool calibration

Abstract

When calibrating a machine tool, multiple measurement tasks will be performed, each of which has an associated uncertainty of measurement. International Standards and best-practice guides are available to aid with estimating uncertainty of measurement for individual tasks, but there is little consideration for the temporal influence on the uncertainty when considering interrelated measurements. Additionally, there is an absence of any intelligent method capable of optimising (reducing) the estimated uncertainty of the calibration plan as a whole. In this work, the uncertainty of measurement reduction problem is described and modelled in a suitable language to allow state-of-the-art artificial intelligence planning tools to produce optimal calibration plans. The paper describes how the continuous, non-linear temperature aspects are discretized and modelled to make them easier for the planner to solve. In addition, detail is provided as how the complex uncertainty equations are modelled in a restrictive language where its syntax heavily influences the encoding. An example is shown for a three-axis machine, where the produced plan exhibits intelligent behaviour in terms of scheduling measurements against temperature deviation and the propagation of error uncertainties. In this example, a reduction of 58% in the estimated uncertainty of measurement due to intelligently scheduling a calibration plan is observed. This reduction in the estimated uncertainty of measurement will result an increased conformance zone, thus reducing false acceptance and rejection of work-pieces. u DEV ICE SQR Uncertainty due to the mechanical square in micrometers (µm) u E,DEV ICE P OST Uncertainty due to thermal expansion of the mounting post in micrometers (µm)

Effective grounding for hybrid planning problems represented in PDDL+

“…Automated planning is exploited in many real-world applications as it is a common capability requirement for intelligent autonomous agents . Example application domains include drilling (Fox et al, 2018), urban traffic control , smart grid (Thiébaux et al, 2013), UAV control (Ramírez et al, 2018;Kiam et al, 2020), e-learning (Garrido et al, 2012), machine tool calibration (Parkinson et al, 2014), human-robot interaction (Petrick & Foster, 2013), and mining (Lipovetzky et al, 2014).…”

Moveable Factories for Leapfrog Manufacturing in an Industrial Economy

“…There are already some examples of radical reductions in the sizes of machine tools: for example, from more than 20 cubic metres (e.g., 3 × 3 × 2.5 m) to less than two cubic metres [39,40]. Important topics include reducing the size and weight of hybrid additive and subtractive machine tools [41] and reducing uncertainty of machine tool calibration [42]. Other opportunities for increasing the range of sophisticated goods that can be manufactured locally with moveable factories may arise from technological advances, such as the Internet of Things (IoT), which can better enable Cyber-Physical Systems (CPS) that entwine digital control systems with physical operations.…”

Analysis of Laser Tracker-Based Volumetric Error Mapping Strategies for Large Machine Tools

“…To estimate the uncertainty or accuracy level of a whole calibration plan is not straightforward, and several methodologies have recently been proposed [18]. To make an adequate comparison between different design alternatives, the calibration procedure is presented as an uncertainty propagation calculus, where the geometric errors represent the main input quantities (X M ) along with the measurement uncertainties of LT (X LT ).…”