Real-time filtering on parallel SIMD architectures for automated quality inspection

Abstract

Surface metrology in automated quality inspection is a field, among many others, affected by noise and thus requiring filtering. In surface metrology, filtering is required to remove undesired information from data in order to extract surface features and relevant properties necessary for quality control. Moreover, filtering requires immediate results, while the product is being manufactured. This way, quick correcting actions can be directly applied to solve possible manufacturing issues. This work proposes different strategies to filter height maps in real-time acquired using laser profilers, the most widely used inspection method in industrial applications. Different models to apply the filtering operations are considered, particularly assessing different alternatives to store previous samples in memory, which are required for data filtering. FIFO, double FIFO, circular and double circular buffers are evaluated. Furthermore, CPU parallelism, SIMD instructions and cache-line friendly data structures are analyzed. The proposed methods are extremely efficient, capable of filtering laser profiles at extremely high acquisition rates. The proposed methods are designed for real-time surface metrology, but they are very likely to find potential applications in different areas. The filters are compared in terms of accuracy and speed, including other well-known filters such as the spline filter. Tests analyze execution time, including cache efficiency and filtering accuracy. Results with synthetic data and real data obtained from steel strips show excellent performance, providing accurate results at very high speeds.