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At least 55 records · Page 3

Defect Detection Model Development for Large Scale Thermoplastic Printing

Large-format additive manufacturing (LFAM) offers several advantages, including high throughput, cost-effective pellet-fed extrusion, and the capability to produce large-scale structures. The main pain points of LFAM include start and stops during the printing process, warpage, long layer times that lead to bead freezing, and bead separation due to shrinkage. These issues can lead to overfill, underfill and buildup of material in different sections of a print. This can lead to hidden defects embedded within the printed layers, or even ultimate failure of the printed structure. This ensures these defects can only be identified through nondestructive testing (NDT) inspection methods after printing, which can be timely and costly. Aligned Vision work specializes in 2D projectors with visual inspection systems and machine learning. Traditionally system is used for composite layup and layup inspections. In this work we used the LFAM system at Oak Ridge National Laboratory to create defect rich samples. The Aligned Vision inspection system then performed in-situ monitoring of the print process after each part was printed. This in-situ vision inspection system was used to develop a layer-by-layer inspection model that looks for overfill, underfill, and the buildup of defects using only a camera-based vision system. This leads to the assurance of high-quality production components.

36 MATERIALS SCIENCE

A software defect detection methodology

This paper identifies baseline procedures for verifying software for individual, small team, and large team development efforts for mission-critical and non-mission-critical software.

software engineering software verification validat

Defects Detection and Characterization Using Leaky Lamb Wave (LLW) Dispersion Data

Composite materials are being used at a significant level of usage for flaw critical structures and they are taking a growing percentage of the makeup of aircraft and spacecraft. Composite structues are now reaching service duration, for which the issue of aging is requiring adquate attention.

Leaky Lamb Wave LLW Polar Backscattering Composite

An Automated Classification Technique for Detecting Defects in Battery Cells

Battery cell defect classification is primarily done manually by a human conducting a visual inspection to determine if the battery cell is acceptable for a particular use or device. Human visual inspection is a time consuming task when compared to an inspection process conducted by a machine vision system. Human inspection is also subject to human error and fatigue over time. We present a machine vision technique that can be used to automatically identify defective sections of battery cells via a morphological feature-based classifier using an adaptive two-dimensional fast Fourier transformation technique. The initial area of interest is automatically classified as either an anode or cathode cell view as well as classified as an acceptable or a defective battery cell. Each battery cell is labeled and cataloged for comparison and analysis. The result is the implementation of an automated machine vision technique that provides a highly repeatable and reproducible method of identifying and quantifying defects in battery cells.

McDowell, Mark

3D X-ray CT for BGA/CGA Workmanship Defect Detection

Commercial-off-the-shelf (COTS) advanced microelectronic technologies in high-reliability versions are now being considered for use in a number of National Aeronautics and Space Administration (NASA) electronic systems. One of the key drawbacks of advanced electronic packages with hidden solder joint interconnections, such as the column grid array (CGA), is that inspection can be challenging—whether visually or using X-rays. In general, inspection for solder joint integrity is poor, except for identifying shorts. The new, advanced X-ray systems, especially the 3D computer tomography version, may be able to provide the three-dimensional features that are extremely difficult to resolve under the 2D systems.This report presents both 2D and 3D X-ray images along with their representative optical photomicrographs for a number of advanced electronics package assemblies including 3D stack and CGA assemblies before and after thermal cycling.

Ghaffarian, Reza

Towards Qualification and Certification of Laser Powder Bed Fusion Ti-6Al-4V with In-Situ Process Monitoring and Automated Defect Detection

Qualification and certification of laser powder bed fusion (LPBF) parts are two challenges that must be answered to ensure suitability for critical applications. In-situ monitoring using high frame rate thermal and conventional optical imaging sensors is applied to the LPBF build process. Currently, the large volume of data from such sensors becomes untenable for manual inspection in production environments. This presentation serves to address this in-situ monitoring deficiency in two ways. First, a framework for managing data streams from LPBF process monitoring sensors is described. Second, two candidate image analysis techniques are presented: one is a set of heuristics that are easily interpretable, and the other is an uninterpretable convolutional neural network. These strategies are compared in terms of performance, computational expense, and speed. These methodologies represent platforms for connecting processing conditions to process modeling efforts aligned with the qualification and certification mission for LPBF Ti-6Al-4V components.

Qualification

An experiment to assess the cost-benefits of code inspections in large scale software development

This experiment (currently in progress) is designed to measure costs and benefits of different code inspection methods. It is being performed with a real development team writing software for a commercial product. The dependent variables for each code unit's inspection are the elapsed time and the number of defects detected. We manipulate the method of inspection by randomly assigning reviewers, varying the number of reviewers and the number of teams, and, when using more than one team, randomly assigning author repair and non-repair of detected defects between code inspections. After collecting and analyzing the first 17 percent of the data, we have discovered several interesting facts about reviewers, about the defects recorded during reviewer preparation and during the inspection collection meeting, and about the repairs that are eventually made. (1) Only 17 percent of the defects that reviewers record in their preparations are true defects that are later repaired. (2) Defects recorded at the inspection meetings fall into three categories: 18 percent false positives requiring no author repair, 57 percent soft maintenance where the author makes changes only for readability or code standard enforcement, and 25 percent true defects requiring repair. (3) The median elapsed calendar time for code inspections is 10 working days - 8 working days before the collection meeting and 2 after. (4) In the collection meetings, 31 percent of the defects discovered by reviewers during preparation are suppressed. (5) Finally, 33 percent of the true defects recorded are discovered at the collection meetings and not during any reviewer's preparation. The results to date suggest that inspections with two sessions (two different teams) of two reviewers per session (2sX2p) are the most effective. These two-session inspections may be performed with author repair or with no author repair between the two sessions. We are finding that the two-session, two-person with repair (2sX2pR) inspections are the most expensive, taking 15 working days of calendar time from the time the code is ready for review until author repair is complete, whereas two-session, two-person with no repair (2sX2pN) inspections take only 10 working days, but find about 10 percent fewer defects.

Porter, A.

Detection of CFRP Composite Manufacturing Defects Using a Guided Wave Approach

NASA Langley Research Center is investigating a guided-wave based defect detection technique for as-fabricated carbon fiber reinforced polymer (CFRP) composites. This technique will be extended to perform in-process cure monitoring, defect detection and size determination, and ultimately a closed-loop process control to maximize composite part quality and consistency. The overall objective of this work is to determine the capability and limitations of the proposed defect detection technique, as well as the number and types of sensors needed to identify the size, type, and location of the predominant types of manufacturing defects associated with laminate layup and cure. This includes, porosity, gaps, overlaps, through-the-thickness fiber waviness, and in-plane fiber waviness. The present study focuses on detection of the porosity formed from variations in the matrix curing process, and on local overlaps intentionally introduced during layup of the prepreg. By terminating the cycle prematurely, three 24-ply unidirectional composite panels were manufactured such that each subsequent panel had a higher final degree of cure, and lower level of porosity. It was demonstrated that the group velocity, normal to the fiber direction, of a guided wave mode increased by 5.52 percent from the first panel to the second panel and 1.26 percent from the second panel to the third panel. Therefore, group velocity was utilized as a metric for degree of cure and porosity measurements. A fully non-contact guided wave hybrid system composed of an air-coupled transducer and a laser Doppler vibrometer (LDV) was used for the detection and size determination of an overlap By transforming the plate response from the time-space domain to the frequency-wavenumber domain, the total wavefield was then separated into the incident and backscatter waves. The overlap region was accurately imaged by using a zero-lag cross-correlation (ZLCC) imaging condition, implying the incident and backscattered waves are in phase over the overlap boundaries.

Hudson, Tyler B.

Nonlinear ultrasonic scanning to detect material defects

A method and system are provided to detect defects in a material. Waves of known frequency(ies) are mixed at an interaction zone in the material. As a result, at least one of a difference wave and a sum wave are generated in the interaction zone. The difference wave occurs at a difference frequency and the sum wave occurs at a sum frequency. The amplitude of at least one nonlinear signal based on the sum and/or difference waves is then measured. The nonlinear signal is defined as the amplitude of one of the difference wave and sum wave relative to the product of the amplitude of the surface waves. The amplitude of the nonlinear signal is an indication of defects (e.g., dislocation dipole density) in the interaction zone.

Yost, William T.