A literature review on welding certificate for robotized welding
The higher production of more and more complex products challenges the accurately for manual welding, and increase the time it takes to programme automatic welding systems. The main objective for the article is to see if current regulations and standards are able to accommodate the shift from automatic weld to autonomous welding systems. To do this, the most current and applicable standards have been analysed. Where there is a need for human input I have tried to find research in new systems that is able to assist computer systems in obtaining that information. The findings are that most of the current standards have room to accommodate autonomous systems, given that the correct safety precautions are taken.
Vision Based Metal Spectral Analysis Using Multi-label Classification
Industrial equipments that employ element identification tend to be expensive as they utilize built-in spectroscopes and computers for post processing. In this paper we present an in situ fully automatic method for detecting constituent elements in a sample specimen using computer vision and machine learning techniques on Laser Induced Breakdown Spectroscopy (LIBS) spectra. This enables the development of a compact and portable spectrometer on a high resolution video camera.In the traditional classification problem, classes are mutually exclusive by definition. However, in spectral analysis a spectrum could contain emissions from multiple elements such that the disjointness of the labels is no longer valid. We cast the metal detection problem as a multi-label classification and enable detection of elemental composition of the specimen. Here, we apply both Support Vector Machine (SVM) and Artificial Neural Networks (ANN) to multiple metal classification and compare the performance with a simple template matching technique. Both machine learning approaches yield correct identification of metals to an accuracy of 99%. Our method is useful in instances where accurate elemental analysis is not required but rather a qualitative analysis. Experiments on the simulation data show that our method is suitable for LIBS metal detection.
Experimental study of water fluxes in a residential area: 2. Road infiltration, runoff and evaporation
Lack of accurate data has led some hydrologists and city planners to assume that urban infiltration is zero and runoff is 100% of the rainfall. These assumptions lead to an over estimation of road runoff volume and an underestimation of direct recharge to groundwater, which is already rising under some UK cities. This study investigates infiltration and runoff processes and quantifies the percentage of rainfall that contributes to storm drainage, and that which infiltrates through different types of road surface. Access tubes were installed for measuring soil water content using a neutron probe in three car parks, a road and a grass site at the Centre for Ecology and Hydrology, Crowmarsh Gifford, Wallingford. Storm drainage was recorded at the exit of the Thamesmead Estate in Crowmarsh Gifford, just before the drain joins the River Thames at Wallingford. Rainfall and water table depth were also recorded. Weekly measurements of soil moisture content indicated that the top 40 cm layer is not influenced by water‐table fluctuations and, therefore, positive changes in soil moisture could be attributed to infiltration of rainfall through the surface. Depending on the nature of the surface, subsurface layers, level of traffic, etc., between 6 and 9% of rainfall was found to infiltrate through the road surfaces studied. The storm drainage generated by road runoff revealed a flow pattern similar to that of the receiving watercourse (River Thames) and increased with the increase of infiltration and soil water content below the road surface. The ratio of runoff to rainfall was 0·7, 0·9 and 0·5 for annual, winter (October–March) and summer (April–September) respectively. As the results of the infiltration indicated that 6 to 9% of annual rainfall infiltrates through the road surface, this means that evaporation represents, 21–24% of annual rainfall, with more evaporation taking place during summer than winter. Copyright © 2003 John Wiley & Sons, Ltd.