The use of job aids for visual inspection in manufacturing and maintenance

Visual inspection is a task regularly seen in manufacturing applications and is still primarily carried out by human operators. This study explored the use of job aids (anything used to assist the operator with the task, such as lists, check sheets or pictures) to assist with visual inspection within a manufacturing facility that inspects used parts. Job aids in the form of inspection manuals were used regularly during the inspection process, and how accurately they were followed was dependent on a number of factors such as size of part, experience of the operator, and accuracy of the inspection manuals. If the job aids were well structured, well written and accessible, then the inspectors were seen to follow them, however for certain jobs inspectors were seen to change the inspection order making inspection more efficient. The findings of the study suggest that prior experience can help in designing efficient, easy to use job aids and that a collaborative approach to design as well as using pictorial examples for comparison purposes would improve the inspection process

Laser measuring system for incremental assemblies

Wire-wrapped frame assemblies used in spark chambers and the like can be measured using a system which utilizes a laser, an interferometer, and a retroreflector to precisely measure distance. A light source and a photodetector are located adjacent the incremental assembly and mounted on a movable carriage. The interferometer is also mounted on the movable carriage, while the laser and retroreflector are positioned at either end of the carriage track. The carriage is moved along one edge of the incremental assembly between the retroreflector and the laser, and as the carriage is moved, the light from the light source to the photodetector is interrupted. This produces a trigger command to a control unit which in turn causes a distance measurement to be made. A printout is provided for each sampling trigger command to list such items as ideal position, actual position and amount of error

Statistical Process Control automation in the final inspection process: an industrial case study

This case study arises from the need to make more robust and effective quality assurance procedures of the products by automating the final inspection process. The case study explains how the automation of the inspection process was performed in a company from the automotive sector. Knowledge, involvement and commitment of operators and respective managers should not be neglected because their reaction against the change procedures influence the success of any automation performed. The successful introduction of automation contributed to a more efficient process and from the pilot station to the remaining stations problem solving and continuous improvement was evidenced.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Surface analysis and fingerprint recognition from multi-light imaging collections

Multi-light imaging captures a scene from a fixed viewpoint through multiple photographs, each of which are illuminated from a different direction. Every image reveals information about the surface, with the intensity reflected from each point being measured for all lighting directions. The images captured are known as multi-light image collections (MLICs), for which a variety of techniques have been developed over recent decades to acquire information from the images. These techniques include shape from shading, photometric stereo and reflectance transformation imaging (RTI). Pixel coordinates from one image in a MLIC will correspond to exactly the same position on the surface across all images in the MLIC since the camera does not move. We assess the relevant literature to the methods presented in this thesis in chapter 1 and describe different types of reflections and surface types, as well as explaining the multi-light imaging process. In chapter 2 we present a novel automated RTI method which requires no calibration equipment (i.e. shiny reference spheres or 3D printed structures as other methods require) and automatically computes the lighting direction and compensates for non-uniform illumination. Then in chapter 3 we describe our novel MLIC method termed Remote Extraction of Latent Fingerprints (RELF) which segments each multi-light imaging photograph into superpixels (small groups of pixels) and uses a neural network classifier to determine whether or not the superpixel contains fingerprint. The RELF algorithm then mosaics these superpixels which are classified as fingerprint together in order to obtain a complete latent print image, entirely contactlessly. In chapter 4 we detail our work with the Metropolitan Police Service (MPS) UK, who described to us with their needs and requirements which helped us to create a prototype RELF imaging device which is now being tested by MPS officers who are validating the quality of the latent prints extracted using our technique. In chapter 5 we then further developed our multi-light imaging latent fingerprint technique to extract latent prints from curved surfaces and automatically correct for surface curvature distortions. We have a patent pending for this method

Automated Analysis of Flow Cytometry Data to Reduce Inter-Lab Variation in the Detection of Major Histocompatibility Complex Multimer-Binding T Cells

Manual analysis of flow cytometry data and subjective gate-border decisions taken by individuals continue to be a source of variation in the assessment of antigen-specific T cells when comparing data across laboratories, and also over time in individual labs. Therefore, strategies to provide automated analysis of major histocompatibility complex (MHC) multimer-binding T cells represent an attractive solution to decrease subjectivity and technical variation. The challenge of using an automated analysis approach is that MHC multimer-binding T cell populations are often rare and therefore difficult to detect. We used a highly heterogeneous dataset from a recent MHC multimer proficiency panel to assess if MHC multimer-binding CD8+ T cells could be analyzed with computational solutions currently available, and if such analyses would reduce the technical variation across different laboratories. We used three different methods, FLOw Clustering without K (FLOCK), Scalable Weighted Iterative Flow-clustering Technique (SWIFT), and ReFlow to analyze flow cytometry data files from 28 laboratories. Each laboratory screened for antigen-responsive T cell populations with frequency ranging from 0.01 to 1.5% of lymphocytes within samples from two donors. Experience from this analysis shows that all three programs can be used for the identification of high to intermediate frequency of MHC multimer-binding T cell populations, with results very similar to that of manual gating. For the less frequent populations (<0.1% of live, single lymphocytes), SWIFT outperformed the other tools. As used in this study, none of the algorithms offered a completely automated pipeline for identification of MHC multimer populations, as varying degrees of human interventions were needed to complete the analysis. In this study, we demonstrate the feasibility of using automated analysis pipelines for assessing and identifying even rare populations of antigen-responsive T cells and discuss the main properties, differences, and advantages of the different methods tested

A NOVEL APPROACH FOR DETECTION FAULT IN THE AIRCRAFT EXTERIOR BODY USING IMAGE PROCESSING

The primary objective of this thesis is to develop innovative techniques for the inspection and maintenance of aircraft structures. We aim to streamline the entire process by utilizing images to detect potential defects in the aircraft body and comparing them to properly functioning images of the aircraft. This enables us to determine whether a specific section of the aircraft is faulty or not. We achieve this by employing image processing to train a model capable of identifying faulty images. The image processing methodology we use involves the use of images of both defective and operational parts of the aircraft\u27s exterior. These images undergo a preprocessing phase that preserves valuable details. During the training period, a new image of the same section of the aircraft is used to validate the model. After processing, the algorithm grades the image as faulty or normal. To facilitate our study, we rely on the Convolutional Neural Network (CNN) approach. This technique collects distinguishing features from a single patch created by the frame segmentation of a CNN kernel. Furthermore, we use various filters to process the images using the image processing toolbox available in Python. In our initial trials, we observed that the CNN model struggled with the overfitting of the faulty class. To address this, we applied image augmentation by converting a small dataset of 87 images to an augmented dataset of 4000 images. After passing the data through multiple convolutional layers and executing multiple epochs, our proposed model achieved an impressive training accuracy of 98.28%. In addition, we designed a GUI-based interface that allows users to input an image and view the results in terms of faulty or normal. Finally, we propose that the application of this research in the field of robotics would be an ideal area for future work

COMPUTER SYSTEM OF COMPREHENSIVE ASSESSMENT OF TECHNICAL CONDITION OF FUNCTIONING OF MECHANISMS

The computer system proposed in this work is aimed at solving the problem of automating a comprehensive assessment of the technical functioning of mechanisms. The system’s computational equipment have the minimum necessary computing requirements. No additional paid software is required for installation. Unlike existing systems, the proposed one has a moderate cost. For the majority of industrial enterprises, this factor is crucial when choosing the most beneficial computer system. In addition, the developed system is simple and comfortable to use. Thus, the system has an intuitive and intelligible interface for the operator, which allows the operator to quickly familiarize themselves with it and put it to use immediately; the system monitors the correctness entries in the electronic history - it corrects basic fields that are not properly indicated (repair data, repair requests, part price, etc.). The system has the ability to add individual templates for a specific unit. Unlike existing systems, the proposed system is multifunctional.The computer system proposed in this work is aimed at solving the problem of automating a comprehensive assessment of the technical functioning of mechanisms. The system’s computational equipment have the minimum necessary computing requirements. No additional paid software is required for installation. Unlike existing systems, the proposed one has a moderate cost. For the majority of industrial enterprises, this factor is crucial when choosing the most beneficial computer system. In addition, the developed system is simple and comfortable to use. Thus, the system has an intuitive and intelligible interface for the operator, which allows the operator to quickly familiarize themselves with it and put it to use immediately; the system monitors the correctness entries in the electronic history - it corrects basic fields that are not properly indicated (repair data, repair requests, part price, etc.). The system has the ability to add individual templates for a specific unit. Unlike existing systems, the proposed system is multifunctional