Machine Vision: Optical Online Quality Inspection of Cutting knife in a WoodChipper
Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Machine vision (MV) is a broad field that is growing very rapidly and being used in every automation control system. It uses computer vi-sion in the context of manufacturing. Machine vision is the analysis of images/pictures to extract data of interest for controlling a process or activity. Thesis focus with in the field of Machine Vision that is used for optical online quality inspection of the cutting knifes in a wood chipper that is also the title of the thesis.The work is focused on measuring the quality of the cutting knifes that are moving with the speed of 45 m/s in a real time wood chipper. On the basis of that quality measurement it is decided that knifes in a wood chipper should be replaced or not. While measuring the quality of knifes the effect of blurring effect due to very fast motion of knifes is also investigated for different exposure times. Different types of illumination sources are discussed and described that which illumination source is suitable for what type of environment and application.The quality of cutting knifes is investigated by comparing the obtained values with predicted values in the least square sense. Different image processing algorithms and filtering techniques are applied to find the region of interest i.e. edges of knifes from the image. Decision is made on the basis of accumulated mean square error.The work presented in the thesis, very clearly describes the hardware components that are used in machine vision systems to acquire images. It forms the basis for future research and development activities within this application area. Analysis of results aim at being important input to the definition of problems faced in machine vision applications. Three important disciplines i.e. image processing, hardware and software design are utilized in this thesis.
Place, publisher, year, edition, pages
2011. , 60 p.
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:miun:diva-14584OAI: oai:DiVA.org:miun-14584DiVA: diva2:446310