Application of automation for in-line quality inspection, a zero-defect manufacturing approach

Image processing is an omnipresent topic in current embedded industrial and consumer applications. Therefore, it is important to investigate processing architectures to extract design guidelines for developing efficient image processors. While SIMD (single instruction, multiple data) processor arrays were often proposed to accelerate image processing tasks, the internal architecture of processor elements (PEs) has not been optimized. Nevertheless, it is necessary to evaluate the optimal complexity of PEs to trade off performance and architectural overhead caused by complex processor architectures. Hence, the goal of this paper is to present a deep evaluation of finding the right architectural complexity of PEs in a processor field to meet given performance and logic area constraints. In order to determine the optimal complexity, the ADL (architecture description language) based FAUPU framework for image preprocessing architectures is utilized and after evaluation extended with pipelining support. The newly introduced pipelining features enable resource-efficient performance optimizations and are a significant improvement to the FAUPU ADL. Due to the fine-grained configurability of the FAUPU architecture, several design variants can be easily generated and it is possible to evaluate the effects of instruction set architecture (ISA) complexity and pipelining on design properties and how these features are best combined. Consequently, the FAUPU framework can be used to address the question, whether it is better to use many lightweight cores or do less but more complex cores yield a greater performance to area ratio? The results show that lightweight cores are best suited to achieve a targeted frame rate with the least resources. However, more complex cores on the other hand yield better performance to area ratios.

Dataflow optimization for programmable embedded image preprocessing accelerators

Piezoelectric sensor array for detecting gas pipeline crack

Quality assurance is the systematic monitoring and evaluation of the various aspects of a product to maximize the probability that minimum standards of quality are being attained by the production process. Deviations from the normal quality that impair the operating characteristics of a metal or product and lead to a reduction in grade or to rejection of products should be considered as defects. To achieve zero defects (“Zero ppm”) output cost-effectively, manufacturers are making the commitment to move to online, automated Non Destructive Testing (NDT) methods. One of the major difficulties with real-time image acquisition is redundancy due to continuous scanning of scenes. The proposed NDT method is a interface predictive coding & is one of the most powerful image coding techniques that can control the redundancy and blur and can identify microscopic casting defects and cracks automatically, which may be even internal fault in nature and measure them by intelligent object detection and feature extraction tools. The paper introduces the Four Step Search matching based automated visual quality inspection and NDT employed using intelligent object detection which have many advantages over existing methods and feature extraction in ultrasonic ToFD scan images as a tool in the automated visual inspection and NDT.

N. Samson

Papers

Advanced 4S image correlation for real-time quality inspection of ceramic products using ultrasonic ToFD images