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The study also addresses reduced execution time through Python implementation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\nSoumen Basak, Saritha P.C, Alenrex Maity, Kanai Chandra Paul  \nDepartment of Printing Engineering, Jadavpur University, Salt Lake Campus, B-73-80, Plot No. 8, Salt Lake By-Pass, LB Block, Sector-III, Bidhannagar,Kolkata – 700106, West Bengal, India.  \nCorrespondence: soumen.basak@jadavpuruniversity  \nAbstract:  \nThe computational learning theory and dynamic programming approach of machine learning techniques made it astutely applicable to the various bids such as developing algorithms, understanding patterns, studying observations, forecasting data etc. The algorithms have the features to construct a framework of trained input dataset to deliver sensible and dynamic data driven predictions and judgements. These contrasting peculiarities of the machine learning techniques can be made useful with the various fields of printing technology in a revolutionized manner. Considering these aspects, this work is focussed on to integrating the machine learning possibilities with flexographic print quality evaluation. This paper established a machine learning algorithm in Python to evaluate the flexographic print quality as dot gain as the output response.  \nThe data analysis and performance evaluation are done using the supervised machine learning approaches such as linear regression, decision tree and random forest regression algorithms. The results show comparatively a better accuracy with random forest regressor technique with 93.02% . Moreover, the strategy to reduce execution time is put forth that involves using Python to implement the machine learning algorithms in this work.  \nKeywords:  \nFlexography, Dot Gain, Machine Learning, Python, Linear Regression, Decision Tree, Random Forest Regressor.  \n1. Introduction  \n1.1 Objective of the study  \nThe machine learning is something that is encountered in our everyday lives. This paper examines the application of machine learning technique in the estimation and prediction of flexographic print quality. This aims to implement supervised machine learning techniques such as: linear regression, decision tree and random forest regression algorithms  \nin the most efficient way possible using the Python tool. The experimental background comprises the flexographic printing on 114 tonal gradations on both coated and uncoated paper under three different anilox roller ruling.  \n1.2 The Flexographic Printing The direct printing method known as flexographic printing that uses a rotary relief  \nimage carrier that is sufficiently flexible to transfer printing ink to a broad range of substrates, including paper, paperboards, plastic films, foils, etc. [1, 2]. A small amount of pressure is sufficient in flexography to move ink from the plate to the substrate atthe printing nip [3, 4] . Figure 1 represents the schematic diagram of flexographic printing process.  \nFigure 1: Schematic diagram of aflexographic  \nprinting unit  \nIn flexographic halftone printing, it is clear that the anilox frequency at the final print affects both the lower and higher screen rules of the plate image. The finer anilox roll is needed to maintain a cell aperture smaller than the minimum reproducible plate dot. The dot becomes steadier as it gets deeper, which reduces the tone value increase [5, 6] .  \nThe amount of ink to be applied to the substrate was greatly influenced by the specifications for the anilox roller cell engraving, which in turn had an impact on the optical density and halftone reproduction of the finished print. The anilox cell capacity was the main factor that determined print quality, but other factors, such the cell’s shape, were also discovered to have an impact [7, 8] .  \nA rise in the substrate surface roughness can lead to a sudden decrease in print density while also increasing print density through smoother paper. In addition, the features of the anilox roller cell in flexography are  \ncrucial for ensuring a","cbCainjzg97666Jb","https://ap.wps.com/l/cbCainjzg97666Jb","pdf",911657,"English","# 1. Introduction\n## 1.1 Objective of the study\n## 1.2 The Flexographic Printing\n## 1.3 Machine Learning\n## 1.3.1 Types of machine learning methods","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To estimate and predict flexographic print quality using supervised machine learning methods, with dot gain as the output response.\"},{\"question\":\"Which supervised learning algorithms are used for performance evaluation?\",\"answer\":\"The study evaluates linear regression, decision tree, and random forest regression approaches.\"},{\"question\":\"How does the random forest model perform compared with the other methods?\",\"answer\":\"Results show higher accuracy with the random forest regressor, reported as 93.02%.\"}]","Machine learning approach to predict the dot gain of flexographic prints | PDF",25]