{"id":22514,"date":"2026-07-16T13:30:10","date_gmt":"2026-07-16T11:30:10","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/use-cases\/quality-improvement\/"},"modified":"2026-07-16T13:47:02","modified_gmt":"2026-07-16T11:47:02","slug":"quality-improvement","status":"publish","type":"page","link":"https:\/\/www.neuraldesigner.com\/use-cases\/quality-improvement\/","title":{"rendered":"Product quality improvement using machine learning"},"content":{"rendered":"<p>Quality improvement investigates the relationship between the features of a product and how well it complies with or conforms to its design.<\/p>\n<p>This article explains how machine learning and\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/\">Neural Designer<\/a>\u00a0can be used to model engineering systems and optimize industrial processes.<\/p>\n<p>This approach can be tested with Neural Designer&#8217;s trial version.<\/p>\n<section>\n<h3>Contents<\/h3>\n<ol>\n<li><a href=\"#DataSet\">Data set<\/a>.<\/li>\n<li><a href=\"#MathematicalModel\">Mathematical model<\/a>.<\/li>\n<li><a href=\"#ResponseOptimization\">Response optimization<\/a>.<\/li>\n<li><a href=\"#Conclusions\">Conclusions<\/a>.<\/li>\n<\/ol>\n<\/section>\n<p><!-- Definition --><\/p>\n<section id=\"DataSet\">On the other hand, the quality index can be objective (a physical or chemical test) or subjective (the evaluation by a person).The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">data set<\/a> contains measurements of our product.<\/p>\n<p>It comprises the design and quality\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Variables\">variables<\/a>.<\/p>\n<p><b>Design variables<\/b> are those\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#InputVariables\">inputs<\/a>\u00a0that determine the quality of the product.<\/p>\n<p>Some examples of design variables are:<\/p>\n<ul>\n<li>Proportions of product components.<\/li>\n<li>Physical or chemical measures of the product.<\/li>\n<li>Etc.<\/li>\n<\/ul>\n<p><b>Quality variables<\/b> are the <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#TargetVariables\">outputs<\/a>\u00a0of the product and depend on the design variables.<\/p>\n<p>Product quality optimization can consider different targets:<\/p>\n<p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/icon-device-hub.svg\" \/><\/p>\n<h3>PRODUCT FEATURES<\/h3>\n<p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/icon-thumbs.svg\" \/><\/p>\n<h3>HUMAN PREFERENCES<\/h3>\n<p>Usually, we will have a single quality variable, although there may be several.<\/p>\n<\/section>\n<section id=\"MathematicalModel\">\n<h2>Mathematical model<\/h2>\n<p>The model of the quality of a product is a mathematical description that adequately predicts the quality of the product from the design variables.<\/p>\n<p>More specifically, it relates the quality variables with the design variables.<\/p>\n<p style=\"text-align: center;\"><strong><em>quality_variables = function(design_variables)<\/em><\/strong><\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/learning\/neural-networks-tutorial\">Neural networks<\/a>\u00a0are algorithms used to fit multi-dimensional and non-linear functions from data sets.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/performance-optimization-neural-network.webp\" \/><\/p>\n<p>The inputs to the neural network include the design variables.<br \/>\nThe outputs from the neural network are the predicted quality of the product.<\/p>\n<\/section>\n<section id=\"ResponseOptimization\">\n<h2>Response optimization<\/h2>\n<p>The objective of the response optimization algorithm is to exploit the mathematical model to look for optimal design variables.<\/p>\n<p>Indeed, the predictive model allows us to simulate different possible products and adjust the design variables to improve quality.<\/p>\n<p>More specifically, product quality optimization can be formulated as follows:<\/p>\n<p style=\"border: 1px solid; text-align: center;\"><b><br \/>\nDetermine the design variables that maximize the quality variables.<br \/>\n<\/b><\/p>\n<p>The next figure illustrates the response optimization process.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/response-optimization.svg\" \/><\/p>\n<p>As we can see, the design values, <b>(x1*,x2*)<\/b>, maximize the quality value.<\/p>\n<p>A quality optimization problem might also be specified by a set of constraints on the product&#8217;s inputs and outputs.<\/p>\n<p>An example is to maximize the compressive strength of concrete while maintaining the amount of cement at the desired value.<\/p>\n<\/section>\n<p><!-- Conclusions --><\/p>\n<section id=\"Conclusions\">\n<h2>Conclusions<\/h2>\n<p>Machine learning can predict the quality of a product based on its design.<\/p>\n<p>That allows us to simulate different products and adjust the design to improve quality.<\/p>\n<p>Some examples are to\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/wine-quality-improvement\">model wine preferences from physicochemical properties<\/a>\u00a0or to\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/examples\/concrete-properties-assessment\">model the compressive strength of high performance concretes<\/a>.<\/p>\n<p><a href=\"https:\/\/www.neuraldesigner.com\/downloads\/\">Neural Designer<\/a> uses neural networks to model the quality of products.<br \/>\nIt also contains response optimization algorithms to fine-tune the design variables and optimize quality.<\/p>\n<\/section>\n<section>\n<h2>Related posts<\/h2>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Quality improvement investigates the relationship between the features of a product and how well it complies with or conforms to its design. This article explains how machine learning and\u00a0Neural Designer\u00a0can be used to model engineering systems and optimize industrial processes. This approach can be tested with Neural Designer&#8217;s trial version. Contents Data set. Mathematical model. [&hellip;]<\/p>\n","protected":false},"author":152,"featured_media":1653,"parent":22498,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-22514","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Product quality improvement using machine learning - Neural Designer<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.neuraldesigner.com\/use-cases\/quality-improvement\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Product quality improvement using machine learning - Neural Designer\" \/>\n<meta property=\"og:description\" content=\"Quality improvement investigates the relationship between the features of a product and how well it complies with or conforms to its design. 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