{"id":3582,"date":"2025-11-26T11:13:19","date_gmt":"2025-11-26T10:13:19","guid":{"rendered":"https:\/\/neuraldesigner.com\/solution\/quality-improvement\/"},"modified":"2026-02-09T11:48:18","modified_gmt":"2026-02-09T10:48:18","slug":"quality-improvement","status":"publish","type":"solution","link":"https:\/\/www.neuraldesigner.com\/solutions\/quality-improvement\/","title":{"rendered":"Product quality improvement using machine learning"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3582\" class=\"elementor elementor-3582\" data-elementor-post-type=\"solution\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-53fa0e55 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"53fa0e55\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3e405f17\" data-id=\"3e405f17\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t<div class=\"elementor-element elementor-element-34198a9 e-flex e-con-boxed e-con e-parent\" data-id=\"34198a9\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-324d1a3 elementor-widget elementor-widget-text-editor\" data-id=\"324d1a3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p class=\"post\">Quality improvement investigates the relationship between the features of a product and how well it complies with or conforms to its design.<\/p><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><p>This approach can be tested with Neural Designer&#8217;s trial version.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e3b1e50 elementor-widget elementor-widget-text-editor\" data-id=\"5e3b1e50\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><script><![CDATA[ function myFunction() { var x = document.getElementById(\"myTopnav\"); if (x.className === \"topnav\") { x.className += \" responsive\"; } else { x.className = \"topnav\"; } } ]]><\/script><\/p><p><script type=\"text\/x-mathjax-config\"><![CDATA[ MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['(',')']]}}); ]]><\/script><\/p><section><h3>Contents<\/h3><ol><li><a href=\"#DataSet\">Data set<\/a>.<\/li><li><a href=\"#MathematicalModel\">Mathematical model<\/a>.<\/li><li><a href=\"#ResponseOptimization\">Response optimization<\/a>.<\/li><li><a href=\"#Conclusions\">Conclusions<\/a>.<\/li><\/ol><\/section><p><!-- Definition --><\/p><section id=\"DataSet\"><p>On the other hand, the quality index can be objective (a physical or chemical test) or subjective (the evaluation by a person).<\/p><p>The <a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set\">data set<\/a> contains measurements of our product.<\/p><p>It comprises the design and quality\u00a0<a href=\"https:\/\/www.neuraldesigner.com\/learning\/tutorials\/data-set#Variables\">variables<\/a>.<\/p><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><p>Some examples of design variables are:<\/p><ul><li>Proportions of product components.<\/li><li>Physical or chemical measures of the product.<\/li><li>Etc.<\/li><\/ul><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><p>Product quality optimization can consider different targets:<\/p><div style=\"text-align: center;\"><div style=\"display: inline-block; padding: 1%;\"><p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/icon-device-hub.svg\" \/><\/p><h3>PRODUCT FEATURES<\/h3><\/div><div style=\"display: inline-block; padding: 1%;\"><p><img decoding=\"async\" style=\"width: 50px;\" src=\"https:\/\/www.neuraldesigner.com\/images\/icon-thumbs.svg\" \/><\/p><h3>HUMAN PREFERENCES<\/h3><\/div><\/div><p>Usually, we will have a single quality variable, although there may be several.<\/p><\/section><section id=\"MathematicalModel\"><h2>Mathematical model<\/h2><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><p>More specifically, it relates the quality variables with the design variables.<\/p><p style=\"text-align: center;\"><strong><em>quality_variables = function(design_variables)<\/em><\/strong><\/p><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><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/performance-optimization-neural-network.webp\" \/><\/p><p>The inputs to the neural network include the design variables.<br \/>The outputs from the neural network are the predicted quality of the product.<\/p><\/section><section id=\"ResponseOptimization\"><h2>Response optimization<\/h2><p>The objective of the response optimization algorithm is to exploit the mathematical model to look for optimal design variables.<\/p><p>Indeed, the predictive model allows us to simulate different possible products and adjust the design variables to improve quality.<\/p><p>More specifically, product quality optimization can be formulated as follows:<\/p><p style=\"border: 1px solid; text-align: center;\"><b><br \/>Determine the design variables that maximize the quality variables.<br \/><\/b><\/p><p>The next figure illustrates the response optimization process.<\/p><p><img decoding=\"async\" src=\"https:\/\/www.neuraldesigner.com\/images\/response-optimization.svg\" \/><\/p><p>As we can see, the design values, <b>(x1*,x2*)<\/b>, maximize the quality value.<\/p><p>A quality optimization problem might also be specified by a set of constraints on the product&#8217;s inputs and outputs.<\/p><p>An example is to maximize the compressive strength of concrete while maintaining the amount of cement at the desired value.<\/p><\/section><p><!-- Conclusions --><\/p><section id=\"Conclusions\"><h2>Conclusions<\/h2><p>Machine learning can predict the quality of a product based on its design.<\/p><p>That allows us to simulate different products and adjust the design to improve quality.<\/p><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><p><a href=\"https:\/\/www.neuraldesigner.com\/free-trial\">Neural Designer<\/a> uses neural networks to model the quality of products.<br \/>It also contains response optimization algorithms to fine-tune the design variables and optimize quality.<\/p><\/section><section><h2>Related posts<\/h2><\/section>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t<div class=\"elementor-element elementor-element-31ab03f e-flex e-con-boxed e-con e-parent\" data-id=\"31ab03f\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7036382 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7036382\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"author":13,"featured_media":1653,"template":"","categories":[],"tags":[43],"class_list":["post-3582","solution","type-solution","status-publish","has-post-thumbnail","hentry","tag-industry"],"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<\/title>\n<meta name=\"description\" content=\"Learn how quality 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