Fast and cost-effective in-process defect inspection for printed electronics based on coherent optical processing

Rapid imaging and product screening with low-cost line-field Fourier domain optical coherence tomography

Fourier domain optical coherence tomography (FD-OCT) is a well-established imaging technique that provides high-resolution internal structure images of an object at a fast speed. Modern FD-OCT systems typically operate at speeds of 40,000-100,000 A-scans/s, but are priced at least tens of thousands of pounds. In this study, we demonstrate a line-field FD-OCT (LF-FD-OCT) system that achieves an OCT imaging speed of 100,000 A-scan/s at a hardware cost of thousands of pounds. We demonstrate the potential of LF-FD-OCT for biomedical and industrial imaging applications such as corneas, 3D printed electronics, and printed circuit boards.

Robust constrained tension control for high-precision roll-to-roll processes

Tension control is critical for maintaining good product quality in most roll-to-roll (R2R) production systems. Previous work has primarily focused on improving the disturbance rejection performance of tension controllers. Here, a robust linear parameter-varying model predictive control (LPV-MPC) scheme is designed to enhance the tension tracking performance of a pilot R2R system for deposition of materials used in flexible thin film applications. The performance of a tension controller may degrade due to disturbances associated with model uncertainties and the slowly-changing dynamics in R2R systems. We introduce a method that separately treats these two sources of disturbance. The controller utilizes an incremental model to eliminate the errors caused by the mismatch between the nominal model and the actual system. A tube-based MPC formulation combined with scheduled parameters adequately updates models and corrects for the time-varying dynamics. Constraints on the rated motor torque are incorporated in the MPC to maintain the controller reliability and avoid machine failures. We illustrate the operation of our control algorithm through simulation of an actual R2R system. The controller outperforms the benchmarks in terms of fast transient response and offset-free tension tracking. It also demonstrates immunity from variations due to parametric uncertainties.

Quality Mapping of Offset Lithographic Printed Antenna Substrates and Electrodes by Millimeter-Wave Imaging

Offset lithographic printed flexible antenna substrate boards and electrodes have attracted much attention recently due to the boost of flexible electronics. Unmanned quality inspection of these printed substrate boards and electrodes demands high-speed, large-scale and nondestructive methods, which is highly desired for manufacturing industries. The work here demonstrates two kinds of millimeter (mm)-wave imaging technologies for the quality (surface uniformity and functionality parameters) inspection of printed silver substrates and electrodes on paper and thin polyethylene film, respectively. One technology is a mm-wave line scanner system and the other is a terahertz-time domain spectroscopy-based charge-coupled device (CCD) imaging system. The former shows the ability of detecting transmitted mm-wave amplitude signals only; its detection is fast in a second time scale and the system shows great potential for the inspection of large-area printed surface uniformity. The latter technology achieves high spatial resolution images of up to hundreds of micrometers at the cost of increased inspection time, in a time scale of tens of seconds. With the exception of absorption rate information, the latter technology offers additional phase information, which can be used to work out 2D permittivity distribution. Moreover, its uniformity is vital for the antenna performance. Additionally, the results demonstrate that compression rolling treatment significantly improves the uniformity of printed silver surfaces and enhances the substrate’s permittivity values.

Autonomous Robot-Guided Inspection System Based on Offline Programming and RGB-D Model

Automatic optical inspection (AOI) is a control process for precisely evaluating the completeness and quality of manufactured products with the help of visual information. Automatic optical inspection systems include cameras, light sources, and objects; AOI requires expert operators and time-consuming setup processes. In this study, a novel autonomous industrial robot-guided inspection system was hypothesized and developed to expedite and ease inspection process development. The developed platform is an intuitive and interactive system that does not require a physical object to test or an industrial robot; this allows nonexpert operators to perform object inspection planning by only using scanned data. The proposed system comprises an offline programming (OLP) platform and three-dimensional/two-dimensional (3D/2D) vision module. A robot program generated from the OLP platform is mapped to an industrial manipulator to scan a 3D point-cloud model of an object by using a laser triangulation sensor. After a reconstructed 3D model is aligned with a computer-aided design model on a common coordinate system, the OLP platform allows users to efficiently fine-tune the required inspection positions on the basis of the rendered images. The arranged inspection positions can be directed to an industrial manipulator on a production line to capture real images by using the corresponding 2D camera/lens setup for AOI tasks. This innovative system can be implemented in smart factories, which are easily manageable from multiple locations. Workers can save scanned data when new inspection positions are included based on cloud data. The present system provides a new direction to cloud-based manufacturing industries and maximizes the flexibility and efficiency of the AOI setup process to increase productivity.