In situ calibration of galvanometric scanning head for laser powder bed fusion machines based on a vision system

Abstract

In Laser Powder Bed Fusion (LPBF) additive manufacturing machines, a laser galvanometric scanning system is used to control the movement of the laser beam, which melts material powders layer-by-layer in a powder bed. Errors between desirable positions and actual positions of the laser in the work plane of the machine directly affect the geometry accuracy of built parts. Therefore, calibrating the laser galvanometric scanning system of LPBF machines is a very important task. This system is generally calibrated by calculating correction tables for its joint components. However, most existing calibration methods require a measuring phase of actual positions of laser engraved on a calibration plate. This phase was generally performed by a measuring system in another location, thus requiring a delay time and causing an interruption of the calibration process. In the current study, an in-situ calibration method for the laser galvanometric scanning head of LPBF machines was developed based on a vision system. The vision system (i.e., a high-resolution monochrome camera) was used to acquire experimental data rapidly during the calibration process, thereby avoiding the laser marking and the measuring phases of laser marks in another location. The acquired experimental data was subsequently used to calculate the correction tables/matrices for calibrating the optical chain of an LPBF machine prototype. The obtained results show that the developed measurement method is acceptable for the in-situ measurement with a good accuracy. After the calibration process, errors between the positions generated by the identified LPBF machine and those measured by the proposed method are very small with an average error of 0.03 mm, and about 80% of positions present an error less than 0.04 mm in the calibration region.

Three-Dimensional Scanning Applied for Flexible and In Situ Calibration of Galvanometric Scanner Systems

“…Another approach is constructing a physical model of the GLS system [ 5 , 6 , 11 , 12 ]. These methods are categorised as model-driven approaches and generally rely on parameter optimisation based on accurate experimental data acquired either manually or through vision systems.…”

“…In [ 5 , 11 ], comprehensive physical models were constructed, containing up to 26 fitting parameters that include optical, geometrical, and assembly errors. Relying on the physical model proposed by [ 5 ], the authors of [ 12 ] developed a vision-based in situ method was developed to calibrate a GLS system for additive manufacturing. The authors achieved a calibration accuracy with maximum deviations below 0.075 mm in a 350 × 350 mm work plane.…”

Applied Machine Vision: Advancing Quality Assurance and Quality Control in Industrial Manufacturing

“…Other authors have proposed comprehensive physical models that describe the joint and task space relationship. These models rely on exhaustive parameter fitting using optimisation based on accurate experimental data and extensive prior knowledge of the optical and mechanical design of the GLS system [37,66,67,38]. As a result, model-based methods are computationally heavy, known to suffer from non-convex optimisation problems, and sensitive to slight changes in the hardware design, limiting their industrial application.…”

“…Several authors have proposed to acquire the experimental data automatically and in situ using co-axially coupled machine vision systems [64] or in an off-axis configuration [67]. However, these solutions require significant modifications to the existing optical configuration or, simply, assume a constant relative orientation between the GLS system and the work plane [67]. Nevertheless, as stated in Section 1.2.2, the constant orientation does not apply to robot-manipulated GLS systems.…”