By Tim Shinbara, Technical Director This month we take a break from disruptive hardware technologies to provide insight on some of the advancements realized in traditional manufacturing by leveraging machining dynamics data and raising visibility to its role in supporting optimal machining.
You have your machine, your tool, tool holder and a machinist handbook for all necessary feeds and speeds to make the best part you can … you must be set, right? Not so fast! It may not take a great leap of faith to agree that not everything in a machinist book is absolute truth, but what you may not know is where you can find such data to lead one down that elusive path of optimized part production.
This month I had the opportunity to follow up with Dr. Tony Schmitz (Associate Professor, University of North Carolina at Charlotte, and co-author with Dr. Scott Smith of the book “Machining Dynamics”) and Mr. Dave Barton (co-founder of BlueSwarf) from an initial meeting at the 2012 National Center for Defense Manufacturing and Machining (NCDMM) Summit. We had an interesting discussion on technology gaps in traditional machining, as well as how value is added to production by knowing how to find that manufacturing “sweet spot” of optimal material removal rate (MRR).
Traditionally, master machinists used their fine-tuned ears and years of experience to “listen” for the right tone produced by a particular setup (feeds, speeds, tool length, etc.) in a particular material to establish the best parameters for that operation on that machine. This process, though effective, is not efficient, transferrable or scalable with the multitude of new materials and processes in the market today. This process is also not without its challenges, the least of which is the drastic reduction in the available skill set like that of a master machinist. This dependence upon a person’s intuition and conservative paper-based calculations has created the “last gap in technology and automation in CNC machining” as stated by Schmitz and Barton. This gap could be filled with a science-based approach to identifying speeds, feeds and depths of cuts; then providing such data via an interactive dashboard.
Their approach takes the current state-of-the-art tap testing, where the dynamic response of each tool in each machine is measured, to the next level by predicting the tool point dynamics from just one spindle measurement and tool-holder model. This data can subsequently be used to calculate the milling process stability.
Once the input data including the tool and holder geometry and spindle response are known, then the elements are combined that are convenient to model (tool and tool holder) to the measurements of those that are inconvenient to model (the spindle-machine subassembly). The procedure for combining these two elements is known as Receptance Coupling Substructure Analysis (RCSA).
The benefit of using RCSA capabilities to replace the tap test is that now any machine’s response can be initially baselined using a simple artifact and then simulations will provide, with significantly high confidence, a profile of feeds and speeds that correspond to given material removal rates for other selected tool-holder combinations. What is interesting, counter-intuitive in fact, is that if one were to encounter chatter and vibration at a given set of parameters, the knee-jerk reaction would be to decrease tool length or begin changing spindle and feed rates. While these typical approaches can provide an acceptable solution in general, it does not necessarily provide an optimum one. Many studies have been completed to demonstrate that higher MRR can often be achieved if the tool point dynamics are known (see figure above). The secret lies in finding the combination that matches the Tooth Passing Frequency (the number of tooth impacts per 1 second; in Hertz) with the dominant natural frequency from the Tool Point Frequency Response (a natural frequency is a frequency at which the system “wants” to vibrate; in Hertz). This harmony is the “sweet spot” in machining and also what maximizes productivity (optimal system solution for chip removal, tool life and part quality). As shown in the figure, by changing the tool length, the dynamic response of the tool-holder-spindle-machine assembly can be tuned to enable higher MRR to be achieved when identifying the sweet spot for each tool overhang length.
The point is made that only listening and depending upon tribal knowledge proves less efficient and, in a time where those types of resources are dwindling, there is another physics-based path to optimal machining that may even begin attracting much of the younger talent to manufacturing. Many thanks to Tony and Dave for their time spent with me and I look forward to more great work from Blueswarf and Tony’s research at UNCC.