Manufacturers often have a love-hate relationship with technology, particularly artificial intelligence (AI) and other solutions that have the potential to affect jobs. On one side, companies need every tool available to help bolster efficiency and cost-effectiveness. On the other, the workforce fears loss of jobs. Predictions for lights-out factories, run by robots, may even make jobs in manufacturing seem destined for obsolescence, scaring away new recruits.

A more logical view, though, sees advanced technologies as a workforce power tool equipping the enterprise with enhanced insights and speed. Freed from manual, tedious tasks, humans can focus on their unique abilities: innovating, problem-solving, and relating to customers.

Recently, I got the chance to travel to Youngstown, Ohio. As I came into town, it struck me that Youngstown was like many other cities across America, including my hometown of Buffalo, New York. In its heyday, Youngstown was a center of manufacturing and steel production—industries that employed thousands of people and formed the backbone of the community. However, this area took it particularly hard when the economy changed and traditional factories closed, and it is still fighting to transform. 

Put in the terms of this article’s title, most of us would run a mile, whatever the proposition. But the popularity of online reviews, and the trust we place in persons unknown when making major decisions about where to stay, what to eat, and how to get the most from a trip, tells a different story.

Online communities have always been a place where people connect with peers: people like us, sharing something in common. Accessible anywhere and generally free to participate, it’s little wonder that news groups, forums, and chat rooms flourished from the beginning of the internet and prepared the ground for the late 2000’s social media explosion.

It’s hard to imagine a world without these connections. They’ve become part of the fabric of our daily lives. They’ve changed not only the way we socialize and define our friends, but also our relationship to information and how we form, and express, our opinions. They’ve also influenced the way we make our vacationing decisions; many of us now move from idea through research to booking entirely on screen.

As a cucumber plant grows, it sprouts tightly coiled tendrils that seek out supports to pull the plant upward. This ensures the plant receives as much sunlight exposure as possible. Now, researchers at MIT have found a way to imitate this coiling-and-pulling mechanism to produce contracting fibers that could be used as artificial muscles for robots, prosthetic limbs, or other mechanical and biomedical applications.

Although many different approaches have been used for creating artificial muscles, including hydraulic systems, servo motors, shape-memory metals, and polymers that respond to stimuli, they all have limitations, including high weight or slow response times. The new fiber-based system, by contrast, is extremely lightweight and can respond very quickly, the researchers say. The findings have been reported in the journal Science.

The new fibers were developed by MIT postdoc Mehmet Kanik and MIT graduate student Sirma Örgüç, working with professors Polina Anikeeva, Yoel Fink, Anantha Chandrakasan, C. Cem Taşan, and five others. The group used a fiber-drawing technique to combine two dissimilar polymers into a single strand of fiber.

There is a lot of buzz these days in the manufacturing sector about robots—and how they can help manufacturers address some of the challenges they face in today’s market, such as increased productivity and the scarcity of skilled workers.

But what exactly do analysts and automation experts mean when they use the word “robot?” How can different types of robots improve an actual manufacturing operation? If you are a smaller manufacturer who is curious about robots but has never worked with them, it may be difficult to envision how robots might fit in to your facility. Here’s an overview of four types of industrial robots that every manufacturer should know.

1. Articulated robots

An articulated robot is the type of robot that comes to mind when most people think about robots. Much like CNC mills, articulated robots are classified by the number of points of rotation, or axes, they have. The most common is the six-axis articulated robot. There are also four- and seven-axis units on the market.

The majority of educational technology is designed for student use. And it’s almost always designed by adults, few of whom consult with kids before they start mass-producing their products and selling them to schools. The disconnect is not lost on Brandon Goon.

Goon, now 20, dropped out of his high-performing New Jersey high school as a junior. He was bored, and he found himself learning far more interesting things outside of school than inside it. As a student, he saw a lot of products aimed at supporting student-centered learning, but created by adults who only thought they knew what kids liked.

“They don’t really understand that I don’t want to use Instagram for the classroom,” Goon says. “Some of those things can be more engaging than your traditional, boring dashboard, but at the end of the day, I want to do my Instagram on Instagram.”

Those of us old enough to remember the “good old days” recall that grade school focused on learning the three R’s: readin’, ’ritin’, and ’rithmetic. In the world of sensors, there are also three Rs: repeatability, resolution, and response. Despite how important these sensor parameters are, there is often confusion in the minds of users about exactly what they mean, and in what ways they tend to interact with each other. This article explains these three Rs for position sensors to dispel any confusion that exists.

The National Association of Manufacturers (NAM) Manufacturers’ Outlook Survey: First Quarter 2019, found that 71.3 percent of the U.S. manufacturers surveyed cited the inability to attract and retain skilled workers as their top concern for the sixth consecutive survey. Analysts have called it a “full-blown workforce crisis.”

I recently had the privilege of attending a meeting hosted by Catalyst Connection, part of the Pennsylvania MEP and the MEP National Network, on opportunities to expand diversity and inclusion in manufacturing while simultaneously addressing the lack of skilled workers. Catalyst Connection, inspired by its move to the neighborhood of Hazelwood in Pittsburgh, brought together some of Southwest Pennsylvania’s economic development, workforce development, and educational powerhouses along with local advanced manufacturers to discuss the pockets of poverty that persist despite the area’s booming manufacturing industry.

In the Iron Man movies, Tony Stark uses a holographic computer to project 3D data into thin air, manipulate them with his hands, and find fixes to his superhero troubles. In the same vein, researchers from MIT and Brown University have now developed a system for interactive data analytics that runs on touchscreens and lets everyone—not just billionaire tech geniuses—tackle real-world issues.

For years, the researchers have been developing an interactive data-science system called Northstar, which runs in the cloud but has an interface that supports any touchscreen device, including smartphones and large interactive whiteboards. Users feed the system datasets, and manipulate, combine, and extract features on a user-friendly interface, using their fingers or a digital pen, to uncover trends and patterns.

Spending too much time and money on incorrect color? Even if you use the best color measurement tools available, your color will still fail without quality control. You may think you’re doing everything right, but if you (or worse, your customers) are rejecting a lot of products, then there’s more you should know.

Quality control (QC) means verifying the color you specify is the same color you manufacture, throughout production. Setting up a color QC program can help you accurately communicate color with clients and suppliers, inspect raw materials before you begin working, and verify that your color is correct before you ship.

Whether you have a quality control program or are considering one, here are five important components to consider with regards to color.

1. Quantify color using a spectrophotometer

Human vision is subjective, which leads to communication errors and confusion. A little brighter, a touch bluer, or a smidge darker are impossible to achieve without hours of trial and error. Measuring color with an instrument such as a spectrophotometer instead of just evaluating by eye can dramatically reduce that wasted time.