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The process of spray painting plays a pivotal role in various industries, from automotive manufacturing to artistic endeavours. Achieving optimal parameters for spray painting is essential to ensure consistent and high-quality results. This research explores the complex world of parameter optimization in spray painting techniques. The material consumption and dry film thickness are the two basic ways to gauge the effectiveness of the spray-coating process. With in right spray conditions, a given dry film thickness might be achieved substantially minimal material use. Through a comprehensive exploration of variables such as air pressure, nozzle size, spray distance and preparation techniques. This research aims to uncover the ideal combination of parameters that yield superior paint application, reduced material consumption, the better Dry film thickness to be obtain and improved efficiency. By employing a systematic approach that involves experimental trials and data analysis, this study contributes to enhancing our understanding of the interplay between these parameters and their impact on paint deposition, finish quality, and overall process effectiveness. The research's conclusions are anticipated to offer insightful explanations for industries and artisans alike, guiding them towards a more refined and effective method for approaching spray painting. As the need for precise and efficient painting processes keeps expanding, the outcomes of this research offer an important enhancement to the advancement in the spray-painting techniques across various applications. [ABSTRACT FROM AUTHOR]

This This work presents a new highly automated machine vision inspection (MVI) tool for real-time defect detection and classification on circular knitting machines based on the combination of the tool (software+hardware) is directly attached to a circular knitting machine and the inspection is performed on-line. The automatic inspection allows the detection and classification of the most frequently occurring types of defects on knitted fabrics, which are significant for purposes of quality control and fabric grading. Identify and categorize defects such as dropped stitches, irregular patterns, or yarn inconsistencies. This objective ensures early detection, allowing for timely interventions to minimize the production of faulty fabrics. Continuous monitoring ensures immediate identification of issues, allowing for on-the-fly adjustments. It aims to optimize the knitting process, improve overall efficiency, and reduce the likelihood of defects by addressing issues as they arise. Implement an automated inspection process to assess the final product's quality. It also contributes to reducing manual inspection efforts, ensuring consistent product quality, and facilitating the sorting of fabrics based on predefined criteria. Enable the knitting machine to accurately reproduce complex designs and patterns. It supports the production of customized and high-value textiles, meeting the demands of diverse markets, including fashion and specialized industries. [ABSTRACT FROM AUTHOR]