Evaluate Machine Learning Model to Better Understand Cutting in Wood

Wood cutting properties for the chains of chainsaw is measured in the lab by analyzing the force, torque, consumed power and other aspects of the chain as it cuts through the wood log. One of the essential properties of the chains is the cutting efficiency which is the measured cutting surface per t...

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Main Author: Anam, Md Tahseen
Format: Others
Language:English
Published: Uppsala universitet, Institutionen för informationsteknologi 2021
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448713
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spelling ndltd-UPSALLA1-oai-DiVA.org-uu-4487132021-08-12T05:24:11ZEvaluate Machine Learning Model to Better Understand Cutting in WoodengAnam, Md TahseenUppsala universitet, Institutionen för informationsteknologi2021Machine LearningMask R-CNNImage AnalysisWood ImagePith DetectionKnotsCracksCutting EfficiencyEngineering and TechnologyTeknik och teknologierWood cutting properties for the chains of chainsaw is measured in the lab by analyzing the force, torque, consumed power and other aspects of the chain as it cuts through the wood log. One of the essential properties of the chains is the cutting efficiency which is the measured cutting surface per the power used for cutting per the time unit. These data are not available beforehand and therefore, cutting efficiency cannot be measured before performing the cut. Cutting efficiency is related to the relativehardness of the wood which means that it is affected by the existence of knots (hardstructure areas) and cracks (no material areas). The actual situation is that all the cuts with knots and cracks are eliminated and just the clean cuts are used, therefore estimating the relative wood hardness by identifying the knots and cracks beforehand can significantly help to automate the process of testing the chain properties, saving time and material and give a better understanding of cutting wood logs to improve chains quality.Many studies have been done to develop methods to analyze and measure different features of an end face. This thesis work is carried out to evaluate a machinelearning model to detect knots and cracks on end faces and to understand their impact on the average cutting efficiency. Mask R-CNN is widely used for instance segmentation and in this thesis work, Mask R-CNN is evaluated to detect and segment knots and cracks on an end face. Methods are also developed to estimatepith’s vertical position from the wood image and generate average cutting efficiency graph based on knot’s and crack’s percentage at each vertical position of wood image. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448713IT ; 21 044application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Machine Learning
Mask R-CNN
Image Analysis
Wood Image
Pith Detection
Knots
Cracks
Cutting Efficiency
Engineering and Technology
Teknik och teknologier
spellingShingle Machine Learning
Mask R-CNN
Image Analysis
Wood Image
Pith Detection
Knots
Cracks
Cutting Efficiency
Engineering and Technology
Teknik och teknologier
Anam, Md Tahseen
Evaluate Machine Learning Model to Better Understand Cutting in Wood
description Wood cutting properties for the chains of chainsaw is measured in the lab by analyzing the force, torque, consumed power and other aspects of the chain as it cuts through the wood log. One of the essential properties of the chains is the cutting efficiency which is the measured cutting surface per the power used for cutting per the time unit. These data are not available beforehand and therefore, cutting efficiency cannot be measured before performing the cut. Cutting efficiency is related to the relativehardness of the wood which means that it is affected by the existence of knots (hardstructure areas) and cracks (no material areas). The actual situation is that all the cuts with knots and cracks are eliminated and just the clean cuts are used, therefore estimating the relative wood hardness by identifying the knots and cracks beforehand can significantly help to automate the process of testing the chain properties, saving time and material and give a better understanding of cutting wood logs to improve chains quality.Many studies have been done to develop methods to analyze and measure different features of an end face. This thesis work is carried out to evaluate a machinelearning model to detect knots and cracks on end faces and to understand their impact on the average cutting efficiency. Mask R-CNN is widely used for instance segmentation and in this thesis work, Mask R-CNN is evaluated to detect and segment knots and cracks on an end face. Methods are also developed to estimatepith’s vertical position from the wood image and generate average cutting efficiency graph based on knot’s and crack’s percentage at each vertical position of wood image.
author Anam, Md Tahseen
author_facet Anam, Md Tahseen
author_sort Anam, Md Tahseen
title Evaluate Machine Learning Model to Better Understand Cutting in Wood
title_short Evaluate Machine Learning Model to Better Understand Cutting in Wood
title_full Evaluate Machine Learning Model to Better Understand Cutting in Wood
title_fullStr Evaluate Machine Learning Model to Better Understand Cutting in Wood
title_full_unstemmed Evaluate Machine Learning Model to Better Understand Cutting in Wood
title_sort evaluate machine learning model to better understand cutting in wood
publisher Uppsala universitet, Institutionen för informationsteknologi
publishDate 2021
url http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-448713
work_keys_str_mv AT anammdtahseen evaluatemachinelearningmodeltobetterunderstandcuttinginwood
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