Structured light measurement-driven adaptive machining for low-pressure turbine blades with powder metallurgy γ-TiAl

Powder metallurgy is a promising method for gamma titanium aluminides (γ-TiAl) low-pressure turbine blade manufacturing as it generates better mechanical properties. However, the powder metallurgy γ-TiAl has an uneven deformation during the pressing process, making it difficult to align the workpiece to the right position during the machining process. To solve this problem, a structured light measurement-driven adaptive machining method is proposed in this paper for the low-pressure turbine blades with powder metallurgy γ-TiAl. The point cloud of the powder metallurgy workpiece is firstly obtained with structured light measurement. Then, the feature point matching method is proposed for coarse registration of the point cloud of the semi-product with the blade design model. Afterwards, a weighted iterative closest point (ICP) algorithm is applied for fine registration of the position of the point cloud to distribute the machining allowance evenly for better machining quality and efficiency. The experiments show that the proposed method can effectively improve the allocation accuracy and allocation results.