The present study features analytical and experimental results of optimizing resistance spot welding performed using a pneumatic force system (PFS). The optimization was performed to join SECC-AF (JIS G 3313) galvanized steel material with SPCC-SD low carbon steel. The SECC-AF is an SPCC-SD (JIS G 3141) sheet plate coated with zinc (Zn) with a thickness of about 2.5 microns. The zinc coating on the metal surface causes its weldability to decrease. This study aims to obtain the highest tensile-shear strength test results from the combination of the specified resistance spot welding parameters. The research method used the Taguchi method using four variables and a combination of experimental levels. The experimental levels are 2-levels for the first parameter and 3-levels for other parameters. The Taguchi optimization experimental results achieved the highest tensile-shear strength at 5049.64 N. It properly worked at 22 squeeze time cycles, 25 kA of welding current, and 0.6-second welding time and 12 holding-time cycles. The S/N ratio analysis found that the welding current had the most significant effect, followed by welding time, squeeze time, and holding time. The delta S/N ratio values were 1.05, 0.67, 0.57 and 0.29, respectively.

In this decade, Internet of Things (IoT) technologies are motivating nations for digital transformation. This transformation is part of Fourth industrial revolution (Industry 4.0). Several challenges are obstacle in the digitalization, one of them is talent in this field. There are not many available automation or control labs equipped with advance automation technologies in the educational institutions. To produce more force for IoT, engineering intuitions need to improve their curriculum and engineering lab facilities. In this paper, a technology-based learning system is proposed for learning IoT. The design of this system purposely developed for control lab for undergraduates and postgraduate students. This system offers a low-cost development using industrial standard controller, which is suitable for industrial and enterprise applications prototyping. Three modules are prepared to train the students; 1)

There are many different varieties of clouds, each with a unique set of properties. As a result of this variability, it is difficult to discern these sorts of clouds. A database’s objects must be categorized using data categorization in order to be organized into multiple categories. This study made use of the Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which falls under the low cloud category and includes photos of Cumulus (182 images), and Cumulonimbus (242 photographs), and Stratus (242 images) (202 images). A fast R-CNN detector with feature extraction = Resnet50 was used to create a system for classifying cloud kinds. A significant amount of training time is saved by the quicker R-CNN due to its lack of a selective search algorithm. Training loss values for cloud images had an average of 0.9030 from the first epoch through the last one. Using the Faster R-CNN object detection method with the Resnet50 architecture, cloud photos were added and the accuracy was 94.12 and the average precision was 0.76. - Faster R-advantages CNN affect the architecture utilized and are marginally influenced by the algorithm choice, however CNN with Resnet50 is superior overall where these advantages are held.

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