A comparative study of methods for defect detection in textile fabrics

Published ArticleFabric defect detection methods have been broadly classified into three categories; statistical methods, spectral methods and model-based methods. The performance of each method relies on the discriminative ability of texture features it uses. Each of the three categories has its own advantages and disadvantages and some researchers have recommended their combination for improved performance. In this paper, we compare the performance of three fabric defect detection methods, one from each of the three categories. The three methods are based on the grey-level co-occurrence matrices (GLCM), the undecimated discrete wavelet transform (UDWT) and the Gaussian Markov Random field models (GMRF) respectively from the statistical, spectral and model-based categories. The tests were done using the textile images from the TILDA dataset. To ensure classifier independence on the outcome of the comparison, the Euclidean distance and feed forward neural network classifiers were used for defect detection using the features obtained from each of the three methods. The results show that GLCM features allowed better defect detection than wavelet features and that wavelet features allowed better detection than GMRF features

Towards the discrimination of milk (origin) applied in cheddar cheese manufacturing through the application of an artificial neural network approach on Lactococcus lactis profiles

Published ArticleAn artificial neural network (ANN) that is able to distinguish between Cheddar cheese produced with milk from mixed and single breed sources was designed. Samples of each batch (4 pure Ayrshire/4 mixed with no Ayrshire milk) were ripened for 92 days and analysed every 14 days. A novel ANN was designed and applied which, based only on Lactococcus lactis counts, provided an acceptable classification of the cheeses. The ANN consisted of a multi-layered network with supervised training arranged in an ordered hierarchy of layers, in which connections were allowed only between nodes in immediately adjacent layers

Monitoring of Laser Powder Bed Fusion by Acoustic Emission: Investigation of Single Tracks and Layers

Quality concerns in laser powder bed fusion (L-PBF) include porosity, residual stresses and deformations during processing. Single tracks are the fundamental building blocks in L-PBF and their shape and geometry influence subsequent porosity in 3D L-PBF parts. The morphology of single tracks depends primarily on process parameters. The purpose of this paper is to demonstrate an approach to acoustic emission (AE) online monitoring of the L-PBF process for indirect defect analysis. This is demonstrated through the monitoring of single tracks without powder, with powder and in layers. Gas-borne AE signals in the frequency range of 2–20 kHz were sampled using a microphone placed inside the build chamber of a L-PBF machine. The single track geometry and shape at different powder thickness values and laser powers were studied together with the corresponding acoustic signals. Analysis of the acoustic signals allowed for the identification of characteristic amplitudes and frequencies, with promising results that support its use as a complementary method for in-situ monitoring and real-time defect detection in L-PBF. This work proves the capability to directly detect the balling effect that strongly affects the formation of porosity in L-PBF parts by AE monitoring

Acoustic Emission Technique for Online Detection of Fusion Defects for Single Tracks During Metal Laser Powder Bed Fusion

One of the main drawbacks of laser based powder bed fusion, is lack of fusion between tracks due to non-optimal input process parameters, scanning and building strategies and/or inhomogeneity in the delivered powder layer. Unstable geometrical characteristics of single tracks and high roughness of the powder layer can cause porosity in 3 dimensional printed parts. In this study a non-destructive online monitoring technique, using acoustic emission was utilized to determine lack of fusion and balling effect of single tracks. This phenomenon was simulated by using an increased powder layer thickness. Short Time Fourier Transform was used as a tool for analysis of the acoustic behaviour of the system and it was compared with the acoustic emission (AE) recorded during processing of single tracks.Mechanical Engineerin

A review of robotics research in South Africa

CITATION: Boje, E., et al. 2019. A review of robotics research in South Africa. R & D Journal of the South African Institution of Mechanical Engineering, 35:75-97, doi:10.17159/2309-8988/2019/v35a9.The original publication is available at https://www.saimeche.org.zaRobots are increasingly being used in the industry. Businesses that use robots can produce products and provide services at lower costs and with higher quality. Some industries, like automotive manufacturing, have become dependent on robots. The impact of robots on society and the greater economy is not clear. Robots threaten the jobs of lowskilled workers and even middle-skilled workers. While researchers and governments are trying to understand the impact of robots on the economy, it is commonly accepted that robots will be used more widely across all industries. With this in mind, it is useful to consider the current research in robotics at South African research institutions. This paper is such a review. It is not exhaustive, but it provides a sense of the robotics research being done in South African research institutions. It appears that research institutions do not work on common themes, yet many research groups relate their work to Industry 4.0. The review suggests that each research group is working on topics of interest to them. The implication of this is that a wide variety of robotic themes are being researched in South Africa.https://www.saimeche.org.za/page/RD_2019Publisher's versio