Machine Tool
Assembly Line
🧠 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.