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In order to enhance the fitness of the product and in order to improve productivity in turning operations, greater amount of challenges have been faced. In this paper, we have made a comparative analysis of HSS and carbide coated HSS drills while machining with H13 steel plates. For the drilling operation, process parameters were analysed using the Taguchi design of experiments. The response performance characteristics of surface roughness of H13 die steel plates for the drilling settings, cutting speed (rpm), and feed rate (mm/min) is optimized. The design of the experiment was conducted using the Taguchi technique for the L18 orthogonal array, and an analysis of variance was observed. The effect of drilling settings on the quality of drilled holes is examined; variation in surface roughness for various levels of speed and feed and the different combinations of these levels will form an L18 orthogonal array design of experiment by Taguchi analysis. A total of 36 cutting tests were performed with two different drill bits; here three different cutting speeds of 300, 600, and 900 rpm were taken with a feed rate of 0.02, 0.04, and 0.06 mm/rev combinations. The response of SN ratio for surface roughness of HSS and carbide tool has been found out for different levels of speed and feed. From this Taguchi analysis, it is identified that the optimal parameter. As a result, the factors are analysed, and optimized parameters have been concluded for H13 material using HSS, and carbide tools were examined both statistically and experimentally. The carbide coated drill bit gives 60% better surface roughness value based on experimental data obtained. The surface roughness value based on experimentation for HSS tool was found to be 34.16% and carbide coated drill bit was 23.40%. [ABSTRACT FROM AUTHOR]

Machine vision is a computer vision system that enables a computer to work on image-based inspection and analysis for different applications. In this computer vision, a camera and sensor were used to view an image for its analysis with the help of some sort of algorithms, then processed to infer the image-based data. Machine vision systems along with Python programs can be used for many interdisciplinary applications like weld inspection, online monitoring in manufacturing auto components etc. In this study, the "Edge detection python algorithm" was developed and run through "Google Colab" notebook to inspect the edges and the boundaries of samples like faying surface-modified friction welded dissimilar joints and a binder clip (paper clamp) to check any defects or cracks and straightness etc. With the help of this Python algorithm, the edge detection was done by Sobel, Scharr, and Prewit operators. An input image of the weld joint and the binder clip were converted into Otsu's binary threshold image. The matrix vision camera and the CMOS sensor were used in the machine vision set-up to take the images. This written algorithm is helpful to trace the edges of any kind of solids components. The edges of the binder clips and the weld joint/zone were detected. The binder clips were inspected under two different cases namely the clip in folding condition (Case I) and the binder clip in unfolding condition (Case II). The results showed a defect that was identified in the weld zone and no bending was in the binder clips. This kind of study is useful in manufacturing industries for quality inspection purposes with a new machine vision set up for online inspection of fabricated components like nuts and bolts etc. [ABSTRACT FROM AUTHOR]

Autonomous transportation is a new paradigm of an Industry 5.0 cyber-physical system that provides a lot of opportunities in smart logistics applications. The safety and reliability of deep learning-driven systems are still a question under research. The safety of an autonomous guided vehicle is dependent on the proper selection of sensors and the transmission of reflex data. Several academics worked on sensor-based difficulties by developing a sensor correction system and fine-tuning algorithms to regulate the system's efficiency and precision. In this paper, the introduction of vision sensor and its scene terrain classification using a deep learning algorithm is performed with proposed datasets during sensor failure conditions. The proposed classification technique is to identify the mobile robot obstacle and obstacle-free path for smart logistic vehicle application. To analyze the information from the acquired image datasets, the proposed classification algorithm employs segmentation techniques. The analysis of proposed dataset is validated with U-shaped convolutional network (U-Net) architecture and region-based convolutional neural network (Mask R-CNN) architecture model. Based on the results, the selection of 1400 raw image datasets is trained and validated using semantic segmentation classifier models. For various terrain dataset clusters, the Mask R-CNN classifier model method has the highest model accuracy of 93%, that is, 23% higher than the U-Net classifier model algorithm, which has the lowest model accuracy nearly 70%. As a result, the suggested Mask R-CNN technique has a significant potential of being used in autonomous vehicle applications. [ABSTRACT FROM AUTHOR]