[PROJECT][20 JUN 2023]

ULTRASONIC IMAGING & 3D RECONSTRUCTION PLATFORM

A precision motion-controlled ultrasonic imaging platform for phased array radar, featuring real-time signal processing and 3D digital twin reconstruction of industrial parts.

PYTHONMATLABPHASED ARRAY RADARMOTION CONTROLSIGNAL PROCESSING
PYTHONMATLABSIGNAL PROCESSING3D RECONSTRUCTION

Overview

An industrial non-destructive testing platform developed at Zhejiang University's Ultrasonic Imaging Laboratory. The system uses precision motion control to position a phased array radar probe, acquires ultrasonic echo signals, and reconstructs 3D digital twin models of inspected parts through a signal processing pipeline.

Technical Architecture

Precision Motion Control

The motion control subsystem drives a three-axis precision gantry, positioning the radar probe at each sampling point along a predefined scan path. Real-time control software written in Python achieves millisecond-level positioning accuracy.

python
class ScanController:
    def execute_scan(self, scan_path: ScanPath):
        for point in scan_path.waypoints:
            self.motion.move_to(point.x, point.y, point.z)
            self.motion.wait_settled(timeout_ms=5)
            # Trigger phased array acquisition
            frame = self.radar.acquire_frame(
                num_elements=64,
                focus_depth=point.focus_mm
            )
            self.buffer.append(frame, point.position)

Signal Processing Pipeline

Ultrasonic echo signals are processed through a MATLAB pipeline performing beamforming, time gain compensation, and envelope detection to produce B-scan cross-section images. Multiple cross-sections are then voxelized and interpolated to reconstruct a complete 3D model.

matlab
% Beamforming and envelope detection
function bscan = process_frame(rf_data, params)
    % Delay-and-sum beamforming
    focused = beamform_das(rf_data, params.delays);
    % Time gain compensation
    compensated = apply_tgc(focused, params.tgc_curve);
    % Hilbert transform envelope detection
    bscan = abs(hilbert(compensated));
end

3D Digital Twin Reconstruction

B-scan data from layer-by-layer scanning is stacked into a 3D volume, processed with anisotropic diffusion denoising and Marching Cubes isosurface extraction to reconstruct a high-precision 3D surface model of the inspected part.

Key Metrics

Metric Value
Positioning accuracy ±0.05mm
Scan rate 200 points/sec
Array elements 64
3D resolution 0.2mm

Significance

The platform provides a complete digital solution for industrial non-destructive testing, capable of detecting sub-millimeter internal defects with broad applications in aerospace and automotive manufacturing.