Automatic planning for machine tool calibration: A case study

Machine tool owners require knowledge of their machine's capabilities, and the emphasis increases with areas of high accuracy manufacturing. An aspect of a machine's capability is its geometric accuracy. International Standards and best-practice guides are available to aid understanding of the required measurements and to advise on how to perform them. However, there is an absence of any intelligent method capable of optimising the duration of a calibration plan, minimising machine down-time. In this work, artificial intelligence in the form of automated planning is applied to the problem of machine tool pseudo-static geometric error calibration. No prior knowledge of Artificial Intelligence (AI) planning is required throughout this paper. The authors have written this paper for calibration engineers to see the benefits that automated planning can provide. Two models are proposed; the first produces a sequential calibration plan capable of finding the optimal calibration plan. The second model has the additional possibility of planning for concurrent measurements, adding the possibility of further reducing machine down-time. Both models take input regarding a machine's configuration and available instrumentation. The efficacy of both models is evaluated by performing a case study of a five-axis gantry machine, whereby calibration plans are produced and compared against both an academic and industrial expert. From this, the effectiveness of this novel method for producing optimal calibration plan is evaluated, stimulating potential for future work.