Nonterrestrial utilization of materials: Automated space manufacturing facility

Four areas related to the nonterrestrial use of materials are included: (1) material resources needed for feedstock in an orbital manufacturing facility, (2) required initial components of a nonterrestrial manufacturing facility, (3) growth and productive capability of such a facility, and (4) automation and robotics requirements of the facility

Artificial intelligence for advanced manufacturing quality

100 p.This Thesis addresses the challenge of AI-based image quality control systems applied to manufacturing industry, aiming to improve this field through the use of advanced techniques for data acquisition and processing, in order to obtain robust, reliable and optimal systems. This Thesis presents contributions onthe use of complex data acquisition techniques, the application and design of specialised neural networks for the defect detection, and the integration and validation of these systems in production processes. It has been developed in the context of several applied research projects that provided a practical feedback of the usefulness of the proposed computational advances as well as real life data for experimental validation

Composite reinforced propellant tanks

Design studies involving weight and cost were carried out for several structural concepts applicable to space shuttle disposable tankage. An effective design, a honeycomb stabilized pressure vessel, was chosen. A test model was designed and fabricated

Quality Control system for a hot-rolled metal surface

The modern ideas about of quality of products are based on the principle of the absolute satisfaction of requirements of recommendations of the buyer. A presence of surface defects of steel-smelting and rolling origin is peculiar to the production of hot-rolling mill. The automatic surface inspection system (ASIS) includes two digital line video cameras for the filming of the upper and lower surfaces of the flat bar, block of illumination of the upper and lower surfaces of the flat bar, computer equipment. A system that secures 100 % control of the surface of rolled metal (of the upper and lower side) detects automatically and classifies the sheet defects in the real time mode was mounted in the domestic practice in the first time in 2003 on hot rolling mill 2000 JSC «Novolipetsk Iron & Steel Corporation» (NISC). The whole assortment of the mill 2000 was divided for the five groups by the outward appearance of the surface. The works on the identification of defects of hot-rolled metal and widening of data base of knowledge of ASIS were continued after the carrying out of guarantee tests. More than 10 thousand images of defects were added to the data base during the year

Surface Defect Detection And Polishing Parameter Optimization Using Image Processing For G3141 Cold Rolled Steel

Traditionally the surface quality inspection especially for metal polishing purpose is perform by human inspectors. Defect detection is a method of nondestructive testing of material and products to detect defects. This study consists of two parts where the first part is applying vision system to detect and measure surface defects that have been characterized to some level of surface roughness. Specimen of G3141 cold rolled steel is used in this research as it represent the actual material applied in local automotive manufacturer. Gray image of scratch defect on metal surface is detected and information about mean gray pixel value (Ga) is interpreted and converted to surface roughness (Ra) measurement. In this study a new technique is developed where the Ga only read on the specific scratch line without considering the whole image. To realize this, automatic cropping algorithm is developed to detect the region of interest and interpret the Ga value. This techniques will enables the polishing to be done at specific scratch defect area without necessary to develop polishing path throughout the whole surface which is time consuming. Second part is to obtain the optimum polishing parameter by using artificial intelligence technique which is able to predict the grit size, polishing time and polishing force parameter to remove the scratch by polishing process. For the purpose of this study, multiple ANFIS or MANFIS have been selected to predict optimum parameter for polishing parameters. Polishing parameter data can be generated by using MANFIS to predict optimum polishing parameters such as grit size, polishing time and polishing force in order to perform polishing process. However due to lack of study done in the field of flat and dry polishing, the polishing parameter data have to be developed. The polishing parameter data for flat and dry polishing is performed by using robotic polishing arm and the experiment runs design by using full factorial design. Results show that the defect detection algorithm able to detect defect only on the scratch area and able to read the Ga value at detected scratch line and transform it to surface roughness measurement at considerably good level of accuracy compared with manual method. Results from MANFIS have shown that the system is able to predict up to 95% accuracy which is considerably high. The overall results from both parts of this research would inspire further advancements to achieve robust machine vision based surface measurement systems for industrial robotic processes specifically in polishing process

Surface Defect Detection Using YOLO Network

Detecting defects on surfaces such as steel can be a challenging task because defects have complex and unique features. These defects happen in many production lines and differ between each one of these production lines. In order to detect these defects, the You Only Look Once (YOLO) detector which uses a Convolutional Neural Network (CNN), is used and received only minor modifications. YOLO is trained and tested on a dataset containing six kinds of defects to achieve accurate detection and classification. The network can also obtain the coordinates of the detected bounding boxes, giving the size and location of the detected defects. Since manual defect detection is expensive, labor-intensive and inefficient, this paper contributes to the sophistication and improvement of manufacturing processes. This system can be installed on chipsets and deployed to a factory line to greatly improve quality control and be part of smart internet of things (IoT) based factories in the future. YOLO achieves a respectable 70.66% mean average precision (mAP) despite the small dataset and minor modifications to the network

Possibilities of Evaluating the Dimensional Acceptability of Workpieces Using Computer Vision

This paper discusses the possibilities of an automated solution for determining dimensionally accurate and defective products using a computer vision system. In a real industrial environment, research was conducted on a prototype of a quality control machine, i.e. a machine that, based on product images, evaluates whether the product is accurate or defective using computer vision. Various geometric features are extracted from the obtained images of products, on the basis of which a fuzzy inference system based on Fuzzy C-means clustering features is created. The extracted geometric features represent the input variables, and the output variable has two values - true and false. The root mean square error in the evaluation of the accuracy and defectiveness of products ranges between 0.07 and 0.16. Through this research, valuable findings and conclusions were reached for the future research, since this topic is poorly examined in the most renowned databases

NDE: An effective approach to improved reliability and safety. A technology survey

Technical abstracts are presented for about 100 significant documents relating to nondestructive testing of aircraft structures or related structural testing and the reliability of the more commonly used evaluation methods. Particular attention is directed toward acoustic emission; liquid penetrant; magnetic particle; ultrasonics; eddy current; and radiography. The introduction of the report includes an overview of the state-of-the-art represented in the documents that have been abstracted