Efficacy comparison of two methods for determining the position of the rebate edge (formed after MDF machining) during automatic monitoring of workpiece delamination
{"title":"Efficacy comparison of two methods for determining the position of the rebate edge (formed after MDF machining) during automatic monitoring of workpiece delamination","authors":"Katarzyna Śmietańska, Jarosław Górski","doi":"10.5604/01.3001.0053.9813","DOIUrl":null,"url":null,"abstract":": Efficacy comparison of two methods for determining the position of the rebate edge (formed after machining) during automatic monitoring of workpiece delamination. Delamination is one of the most common defects in the processing of wood-based materials. It has a huge impact on the quality of the final product. In order to determine the delamination indicators in a simple and reliable way, the automatic image processing method can be used (Śmietańska et al. 2020). Bator and Śmietańska (2019) proposed the special algorithm to estimate the straight line representing a milling edge. However, this algorithm is quite complicated. The aim of this article is to check whether the aforementioned (complicated) algorithmic way can be replaced by a much simpler idea – the precise manual positioning of the scanned sample on the scanner (using very simple device installed on the scanner). The special experimental research was carried out to compare the effectiveness of the two different methods. The straight line which represents the rebate edge identified by Bator and Śmietańska (2019) algorithm was usually accurate to 1 pixel (0.02 mm). The analogue line based on the assumption that the scanned samples were perfectly positioned on the scanner only sometimes fit just as well. At worst, the distance between these lines is 0.2 mm. Usually the distance did not exceed 0.16 mm but was significant and quite random. There was no statistically significant correlation between this parameter (Dmax) and tool condition (VB). It means that sample were not perfect positioned. They were placed more or less in the same position because of imperfect stiffness of the frame installed on the scanner and human errors.","PeriodicalId":8205,"journal":{"name":"Annals of Warsaw University of Life Sciences - SGGW. Forestry and Wood Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Warsaw University of Life Sciences - SGGW. Forestry and Wood Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5604/01.3001.0053.9813","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
: Efficacy comparison of two methods for determining the position of the rebate edge (formed after machining) during automatic monitoring of workpiece delamination. Delamination is one of the most common defects in the processing of wood-based materials. It has a huge impact on the quality of the final product. In order to determine the delamination indicators in a simple and reliable way, the automatic image processing method can be used (Śmietańska et al. 2020). Bator and Śmietańska (2019) proposed the special algorithm to estimate the straight line representing a milling edge. However, this algorithm is quite complicated. The aim of this article is to check whether the aforementioned (complicated) algorithmic way can be replaced by a much simpler idea – the precise manual positioning of the scanned sample on the scanner (using very simple device installed on the scanner). The special experimental research was carried out to compare the effectiveness of the two different methods. The straight line which represents the rebate edge identified by Bator and Śmietańska (2019) algorithm was usually accurate to 1 pixel (0.02 mm). The analogue line based on the assumption that the scanned samples were perfectly positioned on the scanner only sometimes fit just as well. At worst, the distance between these lines is 0.2 mm. Usually the distance did not exceed 0.16 mm but was significant and quite random. There was no statistically significant correlation between this parameter (Dmax) and tool condition (VB). It means that sample were not perfect positioned. They were placed more or less in the same position because of imperfect stiffness of the frame installed on the scanner and human errors.
:工件分层自动监控中两种确定返利边(加工后形成)位置方法的效果比较。脱层是木基材料加工中最常见的缺陷之一。它对最终产品的质量有很大的影响。为了简单可靠地确定分层指标,可以采用自动图像处理方法(Śmietańska et al. 2020)。Bator和Śmietańska(2019)提出了一种特殊的算法来估计代表铣削边缘的直线。然而,这个算法相当复杂。本文的目的是检查上述(复杂的)算法方法是否可以被一个更简单的想法所取代-扫描样品在扫描仪上的精确手动定位(使用安装在扫描仪上的非常简单的设备)。为比较两种方法的有效性,进行了专门的实验研究。Bator和Śmietańska(2019)算法识别的代表返利边缘的直线通常精确到1像素(0.02毫米)。基于扫描样品完全放置在扫描仪上的假设的模拟线只是有时也适合。在最坏的情况下,这些线之间的距离为0.2毫米。通常距离不超过0.16 mm,但具有显著性和随机性。该参数(Dmax)与刀具状态(VB)之间无统计学意义的相关性。说明样品定位不完美。由于安装在扫描仪上的框架的刚度不完美和人为错误,它们被放置在或多或少相同的位置。