PM Blog

Machine learning revolutionizes industries across the board by helping teams analyze mountains of data to find patterns, anomalies and actionable insights. Manufacturing and precision machining are not left out of this equation, especially when it comes to the potential of predictive maintenance.

At its heart, predictive maintenance uses self-learning algorithms driven by data and explicitly trained to perform a particular task. Predictive models require near real-time data from sensors on machines in the modern shop or factory. Based on the data, the self-learning algorithms help human operators monitor the quality of the resulting machined parts and characteristics of the machines. This helps better predict when to perform maintenance, ultimately saving the organization time, money and resources.

“Has growth peaked?” reads a headline from The Washington Post this month. The Guardian says “the global economy is slowing down.” Likewise, Reuters reports that economic forecasters are cutting global economic growth in their 2019 analyses.

But for many—including, it seems, those in the U.S. metalworking sector—spirits, if not expectations, are more optimistic about the short-term economic future.

Yngwie Malmsteen got his first guitar at five years old. A couple years later, when he got a better model, he smashed his first guitar. Since then, he has become recognized as one of the world’s best electric guitar players. He’s also become infamous for smashing guitars onstage. So, when Sandvik set out to build an unsmashable guitar, Mr. Malmsteen was a natural pick to test the final product.

The project was an ideal way for Sandvik to showcase its different areas of manufacturing skills and expertise. “We don’t make products for consumers, so people don’t realize how far in the forefront our methods are,” says Klas Forsström, president of Sandvik Machining Solutions. “Creating a smash-proof guitar for a demanding musician like Malmsteen highlights the capabilities we bring to all complex manufacturing challenges.”

Most of us are familiar with National Signing Day when high school seniors can sign a binding letter of intent for a collegiate sport. But how about a Manufacturing Recognition and Signing Day?

Butler Tech, one of the largest career technical schools in Ohio, located just north of Cincinnati, held such a ceremony for the first time at its main campus last Friday, April 12, for 39 students signing a commitment to work for local industries upon graduating from their high school program. The students are accepting jobs in the precision machining, welding and manufacturing industries.

Deburring of cross bores can be a challenging task, especially in series production. Chemical deburring and common mechanical deburring strategies often require extensive secondary operations outside the machining center, especially where complex geometries and several cross bores leading into one main bore are involved.

With its X-Bores series of deburring tools, Switzerland-based Heule has developed solutions for the automated deburring of cross bores. The COFA-X, Snap-X, Cofa or cross bore deburring tool (CBD) can be integrated into a machine tool’s magazine and enable complete machining in one setup with defined cutting edges, the company says.