The manufacturing of a printed circuit board in the SMT assembly line goes through multiple phases of automatic handling. To ensure the quality of the board and reduce the number of defects, inspection tasks such as solder paste inspection and automatic optical inspection are conducted. The inspection tasks are carried out at various phases of the assembly line. The paper aims to answer the questions of how machine learning technology can contribute for better PCB fault detection in the assembly line and at which parts of the assembly line this technology has been applied. The paper discusses the PCB defect detection by using machine learning and other approaches. The current research shows that PCB defect detection using machine learning are miniscule. Early detection is still unexplored and experimented in the industry.
In this study, using operational research techniques, a model has been presented to assess battlefield threat, to prioritise aggressive targets, to evaluate the capability of own sites and the risks of the conflict with the targets, to define conflict scenarios and finally to select the best scenario using an assignment model. The above proceedings were added as an intermediate phase of target-site assignment, called 'deciding the best conflict scenario', to the 'threat assessment' and 'weapon-target assignment' in the naval combat management system. For each of the own site, the data collected from the environment together with the panels of experts are shown in a two-dimensional matrix, in which the four areas of the matrix represent the conflict scenarios. Considering that the study was done in a simulated environment, the expert's verification and the convergence of the results in Monte Carlo method were used to validate the research. The proposed model can offer optimised decision to the operational commander through predicting the battlefield and managing the site's capacity and the interaction in between during the combat.
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