89,159 research outputs found
Machine Learning Based AFP Inspection: A Tool for Characterization and Integration
Automated Fiber Placement (AFP) has become a standard manufacturing technique in the creation of large scale composite structures due to its high production rates. However, the associated rapid layup that accompanies AFP manufacturing has a tendency to induce defects. We forward an inspection system that utilizes machine learning (ML) algorithms to locate and characterize defects from profilometry scans coupled with a data storage system and a user interface (UI) that allows for informed manufacturing. A Keyence LJ-7080 blue light profilometer is used for fast 2D height profiling. After scans are collected, they are process by ML algorithms, displayed to an operator through the UI, and stored in a database. The overall goal of the inspection system is to add an additional tool for AFP manufacturing. Traditional AFP inspection is done manually adding to manufacturing time and being subject to inspector errors or fatigue. For large parts, the inspection process can be cumbersome. The proposed inspection system has the capability of accelerating this process while still keeping a human inspector integrated and in control. This allows for the rapid capability of the automated inspection software and the robustness of a human checking for defects that the system either missed or misclassified
Tackling the X-ray cargo inspection challenge using machine learning
The current infrastructure for non-intrusive inspection of cargo containers cannot accommodate exploding com-merce volumes and increasingly stringent regulations. There is a pressing need to develop methods to automate parts of the inspection workflow, enabling expert operators to focus on a manageable number of high-risk images. To tackle this challenge, we developed a modular framework for automated X-ray cargo image inspection. Employing state-of-the-art machine learning approaches, including deep learning, we demonstrate high performance for empty container verification and specific threat detection. This work constitutes a significant step towards the partial automation of X-ray cargo image inspection
GRANTING TRACEABILITY OF ASSEMBLIES BY ENSURING VISUAL ACCESSIBILITY OF ASSEMBLY PARTS LABELS THROUGH A VIRTUAL VISION SYSTEM IMPLEMENTED AT BUILD PREPARATION STAGE
The ability to produce already assembled parts is a remarkable advantage of additive manufacturing
technology over traditional manufacturing. We define an assembly as a collection of parts that are
physically linked and share a common functional goal. Traceability of produced assemblies/parts is a must
for some key manufacturing verticals such as automotive, aerospace and medical. Meaning that all parts
must have proper and reliable identification system, thus enabling production and quality management
systems. Traceability of assemblies using part labels is challenging due to several factors. For instance,
some assembly part labels may not be visually accessible, thus blocking automated/manual quality
inspection or categorizations in later stages of the manufacturing line. Here, we propose an inspection
mechanism composed of virtual vision system to validate that an assembly labelling had been correctly
located prior production. Additionally, once the inspection on the virtual model finished, produced
information about the label and the labelling location can be communicated to automated quality inspection
machine vision systems, located at the end of the 3DP digital manufacturing workflow, to check that
physical assembly is consistent with the digital one. The method proposes alternatives in case identified
labels does not satisfy traceability criteria and notifies users accordingly. Since the mechanism is
implemented in the build preparation stage allows customers to save time and resources by avoiding the
production of non-operative produced parts
Optimising the Machine Vision Unit used in an Automated Component Handling System
Published ArticleIn most manufacturing areas there is concern with quality assurance and developing cost-effective automated inspection systems that can provide feedback necessary for closed-loop manufacturing.
A Machine Vision system can perform quick, repetitive inspection on large numbers of products and operate 24 hours a day. It can identify materials and components, locate and orient parts and verify proper assembly. The measurement, alignment or verification results can be logged on a database for later reference, e.g. when there is a customer complaint or fabrication fault.
Certain parameters will restrict the capability of a Machine Vision System in some or other way. The goal is to establish all parameters that will influence the capabilities of the Machine Vision system and to minimize the restrictions they cause
Detection of concealed cars in complex cargo X-ray imagery using Deep Learning
BACKGROUND: Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators.
OBJECTIVE: Development and evaluation of a novel method for the automated detection of cars in complex X-ray cargo imagery.
METHODS: X-ray cargo images from a stream-of-commerce dataset were classified using a window-based scheme. The limited number of car images was addressed by using an oversampling scheme. Different Convolutional Neural Network (CNN) architectures were compared with well-established bag of words approaches. In addition, robustness to concealment was evaluated by projection of objects into car images.
RESULTS: CNN approaches outperformed all other methods evaluated, achieving 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected.
CONCLUSIONS: We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data
Prediction of sedimentation and bank erosion due to the construction of Kahang Dam
River impoundments continue to cause changes to the hydrological regimes of its host river. Thus,
assessment and development of tools for better understanding of the sediment dynamics and
riverbank erosion downstream the dam will be of great benefit to researchers and policymakers.
The present research employs the use of field techniques and estimation models to improve the (i)
prediction of suspended sediment concentration, (ii) monitoring riverbank erosion, and (iii)
development of Riverbank Erosion Index (RbEI) for downstream Kahang Dam. This research used
the Artificial Neural Network (ANN) and ANN with Autoregressive (AR) (NNETAR) in
predicting suspended sediment concentration using sediment concentration, discharge and water
level as inputs. Similarly, erosion pins were installed on four transects to monitor the riverbank
for thirteen months. The results obtained for sediment concentration prediction clearly show that
the R2 for NNETAR (0.885) have better value compared to ANN (0.695) even though the
relationship between discharge and sediment concentration was weak, it outperforms the ANN.
While based on the sediment rating curve (SRC) results, the same pattern was exhibited where the
R2 for NNETAR show a greater value than ANN and SRC with R2 values of 0.695 and 0.451,
respectively. Based on the observed results of quantified riverbank erosion, the most active
transect eroded 1.747 mm/yr- while 0.657 mm/yr- is the least eroded. furthermore, the result
reveals the maximum and minimum sediment contribution to the fluvial system from riverbank
eroded to be 0.00743 tonnes/yr and 0.00148 tonnes/yr respectively. Lastly, by using discharge and
percentage soil composition (sand and clay), a RbEI was developed by the adopted Equation 4.7
to estimate the status of riverbank erosion of River Kahang. Moreover, five classifications of
erosion status were proposed, which can be used to describe the status and severity of the riverbank
erosion. In conclusion, the estimates by the RbEI is expected to serve as basis for analysing and
adopting river stabilisation and restoration design, which will be of importance to dam operators
in making informed decisions regarding early warnings on the riverbank stability. Also, reliable
sediment concentration estimation will assist in the development of catchment sediment budget
which will give an insight into the effect of situating a dam on a river in terms of sediment supply
and riverbank erosio
Automatic Color Inspection for Colored Wires in Electric Cables
In this paper, an automatic optical inspection system for checking the sequence of colored wires in electric cable is presented. The system is able to inspect cables with flat connectors differing in the type and number of wires. This variability is managed in an automatic way by means of a self-learning subsystem and does not require manual input from the operator or loading new data to the machine. The system is coupled to a connector crimping machine and once the model of a correct cable is learned, it can automatically inspect each cable assembled by the machine. The main contributions of this paper are: (i) the self-learning system; (ii) a robust segmentation algorithm for extracting wires from images even if they are strongly bent and partially overlapped; (iii) a color recognition algorithm able to cope with highlights and different finishing of the wire insulation. We report the system evaluation over a period of several months during the actual production of large batches of different cables; tests demonstrated a high level of accuracy and the absence of false negatives, which is a key point in order to guarantee defect-free productions
On Machine Capacitance Dimensional and Surface Profile Measurement System
A program was awarded under the Air Force Machine Tool Sensor Improvements Program Research and Development Announcement to develop and demonstrate the use of a Capacitance Sensor System including Capacitive Non-Contact Analog Probe and a Capacitive Array Dimensional Measurement System to check the dimensions of complex shapes and contours on a machine tool or in an automated inspection cell. The manufacturing of complex shapes and contours and the subsequent verification of those manufactured shapes is fundamental and widespread throughout industry. The critical profile of a gear tooth; the overall shape of a graphite EDM electrode; the contour of a turbine blade in a jet engine; and countless other components in varied applications possess complex shapes that require detailed and complex inspection procedures. Current inspection methods for complex shapes and contours are expensive, time-consuming, and labor intensive
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