A machine-learning based solution for chatter prediction in heavy-dutymilling machines

The main productivity constraints of milling operations are self-induced vibrations, especially regenerative chatter vibrations. Two key parameters are linked to these vibrations: the depth of cut achievable without vibrations and the chatter frequency. Both parameters are linked to the dynamics of machine component excitation and the milling operation parameters. Their identification in any cutting direction in milling machine operations requires complex analytical models and mechatronic simulations, usually only applied to identify the worst cutting conditions in operating machines. This work proposes the use of machine learning techniques with no need to calculate the two above-mentioned parameters by means of a 3-step strategy. The strategy combines: 1) experimental frequency responses collected at the tool center point; 2) analytical calculations of both parameters; and, 3) different machine learning techniques. The results of these calculations can then be used to predict chatter under different combinations of milling directions and machine positions. This strategy is validated with real experiments on a bridge milling machine performing concordance roughing operations on AISI 1045 steel with a 125 mm diameter mill fitted with nine cutters at 45°, the results of which have confirmed the high variability of both parameters along the working volume. The following regression techniques are tested: artificial neural networks, regression trees and Random Forest. The results show that Random Forest ensembles provided the highest accuracy with a statistical advantage over the other machine learning models; they achieved a final accuracy of 0.95 mm for the critical depth and 7.3 Hz for the chatter frequency (RMSE) in the whole working volume and in all feed directions, applying a 10 × 10 cross validation scheme. These RMSE values are acceptable from the industrial point of view, taking into account that the critical depth of this range varies between 0.68 mm and 19.20 mm and the chatter frequency between 1.14 Hz and 65.25 Hz. Besides, Random Forest ensembles are more easily optimized than artificial neural networks (1 parameter configuration versus 210 MLPs). Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.Hidrodamp Project (IDI-20110453) of the Centre for Industrial Technological Development (CDTI

An Intelligent Metrology Informatics System based on Neural Networks for Multistage Manufacturing Processes

The ability to gather manufacturing data from various workstations has been explored for several decades and the advances in sensory and data acquisition techniques have led to the increasing availability of high-dimensional data. This paper presents an intelligent metrology informatics system to extract useful information from Multistage Manufacturing Process (MMP) data and predict part quality characteristics such as true position and circularity using neural networks. The input data include the tempering temperature, material conditions, force and vibration while the output data include comparative coordinate measurements. The effectiveness of the proposed method is demonstrated using experimental data from a MMP

NASA Tech Briefs, July 2007

Topics covered include: Miniature Intelligent Sensor Module; "Smart" Sensor Module; Portable Apparatus for Electrochemical Sensing of Ethylene; Increasing Linear Dynamic Range of a CMOS Image Sensor; Flight Qualified Micro Sun Sensor; Norbornene-Based Polymer Electrolytes for Lithium Cells; Making Single-Source Precursors of Ternary Semiconductors; Water-Free Proton-Conducting Membranes for Fuel Cells; Mo/Ti Diffusion Bonding for Making Thermoelectric Devices; Photodetectors on Coronagraph Mask for Pointing Control; High-Energy-Density, Low-Temperature Li/CFx Primary Cells; G4-FETs as Universal and Programmable Logic Gates; Fabrication of Buried Nanochannels From Nanowire Patterns; Diamond Smoothing Tools; Infrared Imaging System for Studying Brain Function; Rarefying Spectra of Whispering-Gallery-Mode Resonators; Large-Area Permanent-Magnet ECR Plasma Source; Slot-Antenna/Permanent-Magnet Device for Generating Plasma; Fiber-Optic Strain Gauge With High Resolution And Update Rate; Broadband Achromatic Telecentric Lens; Temperature-Corrected Model of Turbulence in Hot Jet Flows; Enhanced Elliptic Grid Generation; Automated Knowledge Discovery From Simulators; Electro-Optical Modulator Bias Control Using Bipolar Pulses; Generative Representations for Automated Design of Robots; Mars-Approach Navigation Using In Situ Orbiters; Efficient Optimization of Low-Thrust Spacecraft Trajectories; Cylindrical Asymmetrical Capacitors for Use in Outer Space; Protecting Against Faults in JPL Spacecraft; Algorithm Optimally Allocates Actuation of a Spacecraft; and Radar Interferometer for Topographic Mapping of Glaciers and Ice Sheets

A probabilistic framework for product health monitoring in multistage manufacturing using Unsupervised Artificial Neural Networks and Gaussian Processes

The emergence of highly instrumented manufacturing systems has enabled the paradigm of smart manufacturing that provides high levels of prognostics functionality. Of particular interest is to precisely determine geometric conformance or non-conformance of workpieces during manufacturing. This paper presents a new dimensional product health monitoring system that learns from in-process sensor data and updates the prediction of the product quality as the product is manufactured. The system uses data from multiple manufacturing stages, unlike from a single stage at a time, to predict the dimensional quality of the finished product that is updated with subsequent measurements such as On-Machine Measurements (OMMs), in on-line incremental learning fashion. It is based on self-supervised neural networks for dimensionality reduction, Gaussian Process Regression (GPR) models for probabilistic prediction about the end product condition and the associated uncertainty, and Bayesian information fusion for updating the conditional probability distribution of the end product quality in the light of new information. The monitoring approach was tested on the prediction of diameter deviations with validation results showing its ability to achieve an average accuracy better than 5 μm in terms of the Root Mean Squared Error (RMSE). Having obtained a Probability Density Function (PDF) for the measurand of interest, the conformance and non-conformance probabilities given the tolerance specifications are computed to support the principle of inspection by exception. This ability to construct a conformance probability-based product quality monitoring system using probabilistic machine learning methods constitute a step change to manufacturing prognostics

Decision support system for form verification of manufactured parts.

The form verification of manufactured parts is a process composed of a set of operations that are expensive and yet add no value to the product. Yet, the resources used to inspect the parts add a small but significant amount of noise that can affect the outcome of the process. For this reason, this research provides guidelines to effectively perform the inspection process by suggesting new mathematical models and approaches that can be used for the creation of a decision support system that can assist in the verification of the accuracy of machined parts.This research proposes two approaches to improve the robustness of the mathematical models from the noise induced by the inspection process. The Dynamic Angle Approach (DAA) and the Free Form Orientation approach (FFO) presented here focus on finding the parameters of the axes and origin of the form that counteract the inaccuracies of the inspection equipment.In summary, this research suggests formalized methods for feature extraction, sampling, path planning, and form fitting, although the last mentioned received the most attention. It is believed that this comprehensive, integrated analysis will lead to the development of a decision support system.The proposed approaches and mathematical models were verified using measurements from features that were perfectly aligned with the coordinate system of the inspection equipment and from features that were intentionally misaligned. The results showed that the models were accurate and robust enough to estimate the parameters and zone of error of the form features and they performed better than existing models.The main goal of this research is to develop procedures that are simple to implement but at the same time are robust enough to provide reliable information that help the metrologist to make accurate decisions about the inspected parts. Form features such as spheres, cylinders, cones, frustums, and torus forms are commonly used to design complex parts. However, the procedures to verify most of these form features have not been developed yet by the national standards. Therefore, this research proposes new mathematical models that combine the concepts of analytic geometry and optimization to provide optimal solutions

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

The Use of Advanced Soft Computing for Machinery Condition Monitoring

The demand for cost effective, reliable and safe machinery operation requires accurate fault detection and classification. These issues are of paramount importance as potential failures of rotating and reciprocating machinery can be managed properly and avoided in some cases. Various methods have been applied to tackle these issues, but the accuracy of those methods is variable and leaves scope for improvement. This research proposes appropriate methods for fault detection and diagnosis. The main consideration of this study is use Artificial Intelligence (AI) and related mathematics approaches to build a condition monitoring (CM) system that has incremental learning capabilities to select effective diagnostic features for the fault diagnosis of a reciprocating compressor (RC). The investigation involved a series of experiments conducted on a two-stage RC at baseline condition and then with faults introduced into the intercooler, drive belt and 2nd stage discharge and suction valve respectively. In addition to this, three combined faults: discharge valve leakage combined with intercooler leakage, suction valve leakage combined with intercooler leakage and discharge valve leakage combined with suction valve leakage were created and simulated to test the model. The vibration data was collected from the experimental RC and processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. A large number of potential features are calculated from the time domain, the frequency domain and the envelope spectrum. Applying Neural Networks (NNs), Support Vector Machines (SVMs), Relevance Vector Machines (RVMs) which integrate with Genetic Algorithms (GAs), and principle components analysis (PCA) which cooperates with principle components optimisation, to these features, has found that the features from envelope analysis have the most potential for differentiating various common faults in RCs. The practical results for fault detection, diagnosis and classification show that the proposed methods perform very well and accurately and can be used as effective tools for diagnosing reciprocating machinery failure

Near Infrared Thermal Imaging for Process Monitoring in Additive Manufacturing

This work presents the design and development of a near infrared thermal imaging system specifically designed for process monitoring of additive manufacturing. The overall aims of the work were to use in situ thermal imaging to develop methods for monitoring process parameters of additive manufacturing processes. The main motivations are the recent growth in use of additive manufacturing and the underutilisation of near infrared camera technology in thermal imaging. The combination of these two technologies presents opportunities for unique process monitoring methods which are demonstrated here. A thermal imaging system was designed for monitoring the electron beam melting process of an Arcam S12. With this system a new method of dynamic emissivity correction based on tracking the melted material is shown. This allows for the automatic application of emissivity values to previously melted areas of a layer image. This reduces the potential temperature error in the thermal image caused by incorrect emissivity values or the assumption of a single value for a whole image. Methods for determining materials properties such as porosity and tensile strength from the in situ thermal imaging are also shown. This kind of analysis from in situ images is the groundwork for allowing part properties to be tuned at build time and could remove the need for post build testing that would determine if it is suitable for use. The system was also used to image electron beam welding and gas tungsten arc welding. With the electron beam welding of dissimilar metals, the thermal images were able to show the preheating effect that the melt pool had on the materials, the suspected reason for the process’s success. For the gas tungsten arc welding process analysis methods that would predict weld quality were developed, with the aim of later integrating these into the robotic welding process. Methods for detecting the freezing point of the weld bead and tracking slag spots were developed, both of which could be used as indicators of weld quality or defects. A machine learning algorithm was also applied to images of pipe welding on this process. The aim of this was to develop an image segmentation algorithm that could be used to measure parts of the weld in process and inform other analysis, like those above

A multi-step learning approach for in-process monitoring of depth-of-cuts in robotic countersinking operations

Robotic machining is a relatively new and promising technology that aims to substitute the conventional approach of Computer Numeric Control machine tools. Due to the low positional accuracy and variable stiffness of the industrial robots, the machining operations performed by robotic systems are subject to variations in the quality of the finished product. The main focus of this work is to provide a means of improving the performance of a robotic machining process by the use of in-process monitoring of key process variables that directly influence the quality of the machined part. To this end, an intelligent monitoring system is designed, which uses sensor signals collected during machining to predict the amount of errors that the robotic system introduces into the manufacturing process in terms of imperfections of the finished product. A multi-step learning procedure that allows training of process models to take place during normal operation of the process is proposed. Moreover, applying an iterative probabilistic approach, these models are able to estimate, given the current training dataset, whether the prediction is likely to be correct and further training data is requested if necessary. The proposed monitoring system was tested in a robotic countersinking experiment for the in-process prediction of the countersink depth-of-cut and the results showed good ability of the models to provide accurate and reliable predictions