Sensor-Based Control of Cutting Tools, Machine Tools Moves From Drawing Board to Mainstream
Manufacturing Engineering Magazine
By: Robert Puhr
A key success factor for Industry 4.0 and IIoT (Industrial Internet of Things) initiatives is the emergence of more and better sensors in machining centers, and even in the cutting tools themselves. These sensors proved the data and connectivity that are the foundation for the “factory of the future”.
But, far from being futuristic, there are a range of “smart sensors” available today—collecting data and showing operators the health of their machines and the metalcutting process. The evolution is achieved through increasingly accurate measurement of the position of the part and the geometrical form of the finished part, as well as the configuration and control of the tools used in the process.
Now and in the Future
In the area of sensor-based control and optimization, where are we on the continuum from drawing board to mature products? The answer to that question is, “It depends”. Machine tools can be equipped with a variety of in-process sensors and transducers. At the first level, these sensors are utilized for machine protection since a system can react 1000 times faster than an operator to an unexpected force strain or potential collision. The technology is advancing through increasingly accurate measurement of the position of the part and the geometrical form of the finished part, as well as the configuration and control of the tools used in the process.
In terms of Industry 4.0, cutting tool digital manufacturing technology is far closer to the drawing board than a mature product. We are at the precipice of a paradigm shift in our industry. In fact we are taking the initial leap into sensor-based, intelligent cutting tools at the company. But, there are other company that had a little different view. They said, products are proven and well along the learning curve, and can be applied in revolutionary ways. They introducing a product for milling operations based on proven science, which presents a solution in a completely new way at the edge of machine physics.
For basic equipment efficiency and the ability to communicate with peripheral devices, there are a number of off-the-shelf solutions ready to go right now. However, artificial intelligence (AI) and augmented reality (AR) technologies are more up and coming. Applications inside the manufacturing industry have lagged, but software using these technologies, like Razor, are helping machine shops realize the value of AI and ML.
Our industry experts all agreed on the key role that sensors play. There’s an old adage, “What gets measured gets managed.” Though it is uncertain who said it, it underscores the fact that any attempt to control or optimize a machine must be based on fast, accurate, and reliable data at the key point of contact—where the cutting tool meets the workpiece.
Technically speaking, technology to successfully collect, distribute, and analyze data for adaptive control was available long before the current interest in Industry 4.0 and IIoT. Historically, in-process measuring equipment can trace its roots back over 60 years. However, the major advances in sensors have occurred much more recently.
One of these advances is a boring tool that automatically compensates for cutting edge wear. Regardless of how sophisticated a CNC machine tool is, it cannot automatically compensate for cutting edge wear on a boring tool. It uses wireless technology to remotely adjust multiple cutting edge diameters on a single tool, optimizing process performance and eliminating the need for operator intervention.
While some sensor output is for information only, the value-added is when sensor signals are processed and used to control part quality and tool condition. Manufacturers can minimize toolpaths and machining time, improve surface finish, maximize machine life, and efficiently machine more challenging parts, such as those with complex geometries, thin walls, hollow cylinders, and slender shafts.
The algorithms for analyzing data can sift through vast amounts of data to find interrelationships and patterns that a human with a spreadsheet would never uncover. AI and ML technologies are used to develop models that help operators and managers arrive at clear conclusions very quickly—and take action based on data-driven conclusions. While the intelligence is artificial, it very closely mimics how humans in the manufacturing world approach things. The only difference is that it doesn’t get overwhelmed by massive amounts of data. The longer data are collected, and the more data collected.
Today’s systems have proven their ability to maintain the cutting process, anticipating when something is not right and notifying an operator or the machine to take corrective action. The decision to notify only, or take automatic actions, is dictated by the customer, and technically, it have a lot of capabilities for automatic control that in some cases are not being utilized.