Machine learning (ML) and computer vision (CV) technologies are vital branches of artificial intelligence (AI) that help automate tasks and increase efficiency across industries. Experts predict that ...
For metallic alloys in aerospace, machine learning can make the development of additive manufacturing (AM) processes both faster and cheaper. In collaboration with the University of Sheffield Advanced ...
Manufacturing technologies have been the first domain to experience this transformation. The review documents how artificial ...
In a new era of industrial revolution, intelligence is key. Knowing how product design can affect manufacturability or how production processes can affect finished quality helps manufacturers make ...
Researchers at Korea University have developed a machine learning model for predicting sheet resistance in phosphorus oxychloride (POCl3) doping processes in solar cell manufacturing. “Our study aims ...
Machine learning is a rapidly growing field with endless potential applications. In the next few years, we will see machine learning transform many industries, including manufacturing, retail and ...
In recent years, significant advancements in ML have influenced several fields beyond computer science, including autonomous driving, structural color design, medicine, and face recognition. The ...
Machine Learning in Manufacturing market includes supply and demand analysis, concentration rate of raw materials, manufacturing process analysis, competitive landscape, application and specification ...
For several decades, machine vision technologies have helped manufacturers — from automotive to semiconductor and electronics — automate processes, improve productivity and efficiency, and drive ...
In pop culture, the combination of business interests and artificial intelligence is something to be feared. It brings to mind Skynet, the malevolent neural network from the Terminator movies that ...
More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...
Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...
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