<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Binder-Jetting on 3D Print Titanium</title><link>https://3dprinttitanium.com/tags/binder-jetting/</link><description>Recent content in Binder-Jetting on 3D Print Titanium</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 28 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://3dprinttitanium.com/tags/binder-jetting/index.xml" rel="self" type="application/rss+xml"/><item><title>JetAI: AI-Driven Optimization of Binder Jetting for Titanium Alloys</title><link>https://3dprinttitanium.com/articles/jetai-titanium-binder-jetting-optimization-2026/</link><pubDate>Sat, 28 Mar 2026 00:00:00 +0000</pubDate><guid>https://3dprinttitanium.com/articles/jetai-titanium-binder-jetting-optimization-2026/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>The optimization of metal binder jetting for titanium alloy aerospace components is essential, given the industry&amp;rsquo;s shift towards additive manufacturing for its advantages in strength-to-weight ratio and corrosion resistance. Current processes face challenges in achieving optimal isotropy and mechanical properties. We introduce JetAI, an AI-driven framework that dynamically adjusts jetting parameters to optimize component quality. Utilizing a hybrid approach that combines quantum-inspired algorithms and machine learning, JetAI enhances isotropy by 36.7% and improves tensile strength to 21.54 MPa over traditional methods. These results suggest that JetAI not only benefits titanium alloys but also holds potential for broader material applications, setting a new standard in aerospace manufacturing.&lt;/p></description></item></channel></rss>