Automated machine tool dynamics identification for predicting milling stability charts in industrial applications
As the machine tool dynamics at the tooltip is a crucial input for chatter prediction, obtaining these dynamics for industrial applications is neither feasible through experimental impact testing for numerous tool-holder-spindle combinations nor feasible through physics-based modeling of the entire machine tool due to their sophisticated complexities and calibrations. Hence, the often-chosen path is a mathematical coupling of experimentally measured machine tool dynamics to model-predicted tool-holder dynamics. This paper introduces a novel measurement device for the experimental characterization of machine tool dynamics. The device can be simply mounted to the spindle flange to automatically capture the corresponding dynamics at the machine tool side, eliminating the need for expertise and time-consuming setup efforts thus presenting a viable solution for industries. The effectiveness of this method is evaluated against conventional spindle receptance measurement attempts using impact tests. The obtained results are further validated in the prediction of tooltip dynamics and stability boundaries.
Briquetting Manufacturer Tools Up for Faster Turnaround Times
Briquettes are dense blocks created by compressing certain materials. For instance, charcoal and water softener salt are prepared as briquettes using charcoal dust and sodium chloride, respectively. These blocks can be compressed in cantilevered removable cutting rolls like those machined by K.R. Komarek, a Wood Dale, Illinois, company whose founder, Gustav Komarek, patented briquetting processes in the early 1900s to make coal less dusty and easier to transport.
With a wide variety of briquette designs, sizes and materials, machining these rolls is a demanding task for machine tools and, in particular, tooling. To overhaul a lengthy, hands-on process, Komarek paired a Mazak turn-mill machine with Ceratizit ISO-P-grade tooling to reduce production cycles and eliminate an arduous hand-grinding process.
Employees who formerly would have been tackling manual tasks on the briquetting roll production line can now work in Komarek’s reconditioning and repair department. These new processes also eliminated the potential for human error from hand-ground pockets, and by previewing milling paths through simulation features within Mastercam, Komarek is seeing less than a thousandth of an inch of variance in pockets. Tool life has also become much more predictable.
Creative Robot Tool Use with Large Language Models
We introduce RoboTool, enabling robots to use tools creatively with large language models, which solves long-horizon hybrid discrete-continuous planning problems with the environment- and embodiment-related constraints.
In this work, we are interested in solving language-instructed long-horizon robotics tasks with implicitly activated physical constraints. By providing LLMs with adequate numerical semantic information in natural language, we observe that LLMs can identify the activated constraints induced by the spatial layout of objects in the scene and the robot’s embodiment limits, suggesting that LLMs may maintain knowledge and reasoning capability about the 3D physical world. Furthermore, our comprehensive tests reveal that LLMs are not only adept at employing tools to transform otherwise unfeasible tasks into feasible ones but also display creativity in using tools beyond their conventional functions, based on their material, shape, and geometric features.
Industrial AI-Enabled Machine Tool Spindle Health Prognostics Has Been Transformed To Commercialized Value In Partnership With Mazak In Florence, KY.
In a significant collaboration with Mazak, based in Florence, KY, the AI-driven prognostics for machine tool spindle health developed at the Industrial AI center has transitioned from a research concept to a commercial asset. This partnership not only underscores the practical applications of the center’s research but also emphasizes the transformative potential of AI in the industrial domain.
🧠 Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data
CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs.
3Din30: How Its Made – the Evolution of Tooling
Using the Toolchanger to Automate Production
The benefits of automation are potentially huge, but the investment required for a robot arm or pallet changer can be intimidating or even prohibitive. “Our customers wanted to get more usage out of their precision vises and felt they wanted to get into automation, but every time you start talking with those ballpark numbers jumping into $250,000 or $300,000 to do setups and vises, it scares so many off,” says Jon Dobosenski, general manager of Lang Technovation. This inspired Lang’s Haubex system, which it designed to be a low-cost, simple way for shops to take a first step in automation by using a feature that’s already included on many milling machines — the toolchanger.
Grinding Simulation Enables Growth in Custom Tooling
Even the best grinding simulation has flaws — namely, a reliance on perfection. Real-world scenarios on the shop floor can diverge from the tested parameters, requiring adjustments to achieve the performance promised in the simulation. Gorilla Mill, a toolmaker based out of Waukesha, Wisconsin, relies on ANCA’s CIMulator3D software to control for these differing parameters.
By providing a virtual testing ground for complex custom designs, the software ensures tool quality, prevents scrap and streamlines the process of developing customer prints. A machine-side simulator application reduces setup time by highlighting how differences between ideal and actual circumstances will affect the ground part and by enabling machinists to adjust settings to achieve optimal results rather than regrind wheels.