Sparsely captured free tool central point total focusing method for unknown curved components under automatic immersion ultrasonic inspection

In intelligent manufacturing, the application of total focusing method (TFM) has expanded from manual ultrasonic testing to automatic immersion ultrasonic inspection (AIUI). However, the conventional TFM designed for manual does not account for improvements in data acquisition and imaging for AIUI, making it difficult to apply efficiently and accurately to large-sized components with unknown curved surfaces. In this paper, a sparsely captured free tool central point total focusing method (SCFTCP-TFM) based on the surface estimation algorithm in pitch-catch mode (SEAPCM) is proposed to achieve high-precision interface reconstruction and defect detection under AIUI. The workpiece cross-section is imaged with the proposed method based on FMC data acquired at different tool central points, extending the application scope of TFM to automated inspection. Rapid interface reconstruction is achieved using sparse FMC data while reducing the computational load. The estimated surfaces for two blade-type components indicate a maximum error of 0.074 mm, and the positions of curved surfaces and Φ0.8 mm flat bottom holes (FBHs) are correctly presented in the cross-sections of components. SCFTCP-TFM requires only one-third of the coumputational cost of previous methods, with almost no degradation in inspection quality, and is expected to meet the practical AIUI applications.