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A recent effort by the Norton Advanced Applications Engineering Group demonstrates that for difficult-to-machine materials, grinding can be an economical alternative to other machining processes.

The classic manufacturing conundrum is how to make products quicker, cheaper, and better.

Until the middle of 2010, first-tier subcontract machinist, JJ Churchill, could produce turbine blades only if they had their fir-tree root-forms preground elsewhere, or if they were subsequently added by another subcontractor. No longer is this the case.

Nesting is the process of arranging parts to be cut from sheets of metal or wood in the most efficient manner possible in order to maximize yield and speed the cutting process. By reducing scrap and accelerating the cutting process, fabricators are saving on material cost while running more jobs.

In this podcast, John Derry, senior application engineer at Promess, talks to SME Media contributing editor Michael Anderson about reducing complexity in assembly equipment. Derry discusses how assembly systems may become unnecessarily complex--and how that complexity can reduce quality and increase costs. Derry goes on to describe effective ways of addressing these issues and to reduce and even eliminate unnecessary complexity.

Turning to software over paper reports does more than just save trees. Chris Mahar, Associate Editor of Manufacturing Engineering, talks with David McPhail, President and CEO of Memex Inc., about how manufacturers can utilize technology to improve efficiency, reduce downtime and boost productivity and profits across their business.

Manufacturers require a deep understanding of operations to make quick, data-driven decisions and keep production running in the face of unpredictable challenges.

To climb the Mastercam learning curve, Ariel Corp. worked with CNC Software to craft a training course for its workforce.

High productivity, speed and long abrasives life are key for grinding applications in all types of materials.

The complex world of artificial intelligence and machine learning—and why it all matters