Since the early 1980s, the automotive industry has used hydraulically actuated (servo-hydraulic) test systems to simulate operating speeds and road conditions for testing OEM components and fully assembled vehicles.

Artificial intelligence (AI) is gaining momentum across a vast array of fields. Nearly everyone has tried or actively uses a form of AI, whether for personal or professional purposes.

You are the only one who can protect your time and your interests. You have to establish the “line” you’re not willing to cross or allow others to cross.

Every time you squeeze toothpaste onto your toothbrush, spray perfume on your skin, or swallow a pill, you’re using the result of a carefully crafted recipe made in a lab. These are called formulations.

In last month’s article, “ANOVA and the Process Behavior Chart,” we saw how both techniques use the same basic comparison to answer completely d

Understanding the inner workings of your industrial artificial intelligence (IAI) system is crucial if you want it to add measurable value to your manufacturing operations. Here, we’ll dig into one important aspect of every AI: the inputs, aka your data.

In global manufacturing, two key goals—intertwined yet distinct—heavily dominate the industry’s agenda: addressing the growing demand for environmentally sustainable practices, and optimizing operational efficiency.

Many marketing professionals are drawn to their field because they consider themselves more right-brained (creative) than left-brained (analytical).

Scientists at the National Institute of Standards and Technology (NIST) have created a new thermometer using atoms boosted to such high energy levels that they are 1,000 times larger than normal.

In the ever-changing landscape of business management, the concept of quality has undergone significant transformations.