{"id":2635,"date":"2024-10-09T10:15:34","date_gmt":"2024-10-09T08:15:34","guid":{"rendered":"https:\/\/clooma.ai\/?post_type=guide-dutilisateur&#038;p=2635"},"modified":"2024-10-21T15:19:00","modified_gmt":"2024-10-21T13:19:00","slug":"03-scanning","status":"publish","type":"guide-dutilisateur","link":"https:\/\/clooma.ai\/en\/users-guide\/03-scanning\/","title":{"rendered":"Sweeping plan"},"content":{"rendered":"<p><strong>A sweeping plan<\/strong> also known as clearing designs, generally refers to experimental design methods used to explore the effects of different variables on a response. These designs are particularly useful for gaining an overview of the behavior of a system or process. The initial aim of these experimental designs is to evaluate the effects of several factors X on a response Y, and they are particularly useful in the case of a large number of factors X.<\/p>\n\n\n\n<p>Ellistat Data Analysis offers scan plans for factors that can have between 2 and 3 levels, such as :<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>L12 Orthogonal Arrays (Taguchi Designs) are part of the\u00a0<strong>orthogonal planes<\/strong>\u00a0developed by Japanese statistician\u00a0<strong>Genichi Taguchi<\/strong>. They are widely used in experimental design to test the effects of several factors with a minimum number of experimental trials. The\u00a0<strong>L12 Orthogonal Array (Taguchi Design)<\/strong>\u00a0is a type of\u00a0<strong>orthogonal plane<\/strong>\u00a0Taguchi, which is designed to handle up to 11 factors.<\/li>\n\n\n\n<li>Visit\u00a0<strong>experimental designs L20<\/strong>\u00a0is one of the orthogonal designs used for process optimization and investigating factor effects in multi-factor experiments. L20 designs allow up to 19 factors to be tested, each at two levels, while maintaining the orthogonality of the tests.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Example: Experimental design - Scanning design<\/h2>\n\n\n\n<p><strong>ELLIMETAL<\/strong>&nbsp;manufactures complex metal parts, such as engine and transmission components, which require high precision and optimum surface finish. The milling machine used in production plays a crucial role in determining the final characteristics of the parts.<\/p>\n\n\n\n<p>Let's imagine you're responsible for optimizing a milling machine used to manufacture metal parts. You want to determine the impact of various machine and processing parameters on the dimensional accuracy and surface finish of the parts.<\/p>\n\n\n\n<p>The output data to be optimized is Roughness Ra (\u00b5m).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Factors to consider<\/h3>\n\n\n\n<p>The 9 factors you want to study could include:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Spindle speed (RPM)<\/strong>&nbsp;Machine spindle speed. [1000 ; 2000]<\/li>\n\n\n\n<li><strong>Tool feed (mm\/min)<\/strong>&nbsp;Speed at which the tool feeds into the workpiece. [50 ; 100]<\/li>\n\n\n\n<li><strong>Cutting depth (mm)<\/strong>&nbsp;Depth of cut in the material. [1 ; 2]<\/li>\n\n\n\n<li><strong>Cutting tool type<\/strong>&nbsp;Tool material and shape. [A; B]<\/li>\n\n\n\n<li><strong>Type of lubricant<\/strong>&nbsp;Type of lubricant used for milling. [Lx; Ly]<\/li>\n\n\n\n<li><strong>Room temperature (\u00b0C)<\/strong>&nbsp;Workpiece temperature during milling. [20 ; 30]<\/li>\n\n\n\n<li><strong>Cooling pressure (bar)<\/strong>&nbsp;Applied coolant pressure. [5 ; 10]<\/li>\n\n\n\n<li><strong>Cutting angle (\u00b0)<\/strong>&nbsp;Angle of cutting tool in relation to workpiece surface. [15 ; 30]<\/li>\n\n\n\n<li><strong>Clamping conditions<\/strong>&nbsp;: Method of mounting the part on the machine. [M1; M2]<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\">Strategy<\/h3>\n\n\n\n<p>L12 generates 12 experimental combinations from these factors and levels, as illustrated above. Each combination represents a unique milling machine setting.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Test matrix generation with Ellistat - Scanning plan<\/h2>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/clooma.ai\/wp-content\/uploads\/Untitled-108-1024x521.png\" alt=\"\" class=\"wp-image-2636\"\/><\/figure><\/div>\n\n\n<ul class=\"wp-block-list\">\n<li>Click on the \"<strong>DOE<\/strong>\"then click on the map \"<strong>Sweeping plan<\/strong>&nbsp;<strong>\"<\/strong>.<\/li>\n\n\n\n<li>In the&nbsp;<strong>zone 1<\/strong>You can set the tab name and change the number of levels. \ud83d\udcdd: Set the tab name to \"ELLIMETAL\", this will create a new tab in the grid page.<\/li>\n\n\n\n<li>In&nbsp;<strong>zone 2,<\/strong>&nbsp;we can put the number of factors, name them then put the value of the min and max \ud83d\udcdd: Put 9 factors :\n<ul class=\"wp-block-list\">\n<li><strong>Spindle speed (RPM)<\/strong>&nbsp;[1000 ; 2000]<\/li>\n\n\n\n<li><strong>Tool feed (mm\/min)<\/strong>&nbsp;[50 ; 100]<\/li>\n\n\n\n<li><strong>Cutting depth (mm)<\/strong>&nbsp;[1 ; 2]<\/li>\n\n\n\n<li><strong>Cutting tool type<\/strong>&nbsp;[A; B]<\/li>\n\n\n\n<li><strong>Type of lubricant<\/strong>&nbsp;[Lx; Ly]<\/li>\n\n\n\n<li><strong>Room temperature (\u00b0C)<\/strong>&nbsp;[20 ; 30]<\/li>\n\n\n\n<li><strong>Cooling pressure (bar)<\/strong>&nbsp;[5 ; 10]<\/li>\n\n\n\n<li><strong>Cutting angle (\u00b0)<\/strong>&nbsp;[15 ; 30]<\/li>\n\n\n\n<li><strong>Clamping conditions<\/strong>&nbsp;[ M1; M2]<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>In&nbsp;<strong>zone 3,<\/strong>&nbsp;you'll find a preview of the test matrix, which you can create in the create tab in&nbsp;<strong>zone 1.<\/strong>&nbsp;You can also create a D-optimal design, which is a type of experimental design that aims to maximize the statistical efficiency of an experiment while minimizing the number of trials required. \ud83d\udcdd: Click on \"Create DOE\".<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/clooma.ai\/wp-content\/uploads\/Untitled-109-1024x425.png\" alt=\"\" class=\"wp-image-2637\"\/><\/figure><\/div>\n\n\n<ul class=\"wp-block-list\">\n<li>In&nbsp;<strong>the \"ELLIMETAL\" grid<\/strong>In this case, we find the test matrix with the 8 tests forming an orthogonal matrix.<\/li>\n\n\n\n<li>In the next column you can manually add the trial run results as shown in the following figure: <a href=\"https:\/\/www.notion.so\/Donn-e-DOE-BALAYAGE-ab8bf9d9abd5410dbbec44225021c2b0?pvs=21\">DOE BALAYAGE data<\/a><\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img decoding=\"async\" src=\"https:\/\/clooma.ai\/wp-content\/uploads\/Untitled-110-1024x316.png\" alt=\"\" class=\"wp-image-2638\"\/><\/figure><\/div>","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"menu-guide-dutilisateur":[24],"class_list":["post-2635","guide-dutilisateur","type-guide-dutilisateur","status-publish","hentry","menu-guide-dutilisateur-6-doe-plans-dexperiences"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Plan de balayage - Clooma<\/title>\n<meta name=\"description\" content=\"Comment r\u00e9aliser un plan de balayage avec Ellistat Data Analysis ? 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