Monitoring of the BTA Deep Hole Drilling Process Using Residual Control Charts

Deep hole drilling methods are used for producing holes with a high lengthto- diameter ratio, good surface finish and straightness. The process is subject to dynamic disturbances usually classified as either chatter vibration or spiralling. In this work, we propose to monitor the BTA drilling process using control charts to detect chatter as early as possible and to secure production with high quality. These control charts use the residuals obtained from a model which describes the variation in the amplitude of the relevant frequencies of the process. The results showed that chatter is detected and some alarm signals are related to changing physical conditions of the process. --

A comparison study of distribution-free multivariate SPC methods for multimode data

The data-rich environments of industrial applications lead to large amounts of correlated quality characteristics that are monitored using Multivariate Statistical Process Control (MSPC) tools. These variables usually represent heterogeneous quantities that originate from one or multiple sensors and are acquired with different sampling parameters. In this framework, any assumptions relative to the underlying statistical distribution may not be appropriate, and conventional MSPC methods may deliver unacceptable performances. In addition, in many practical applications, the process switches from one operating mode to a different one, leading to a stream of multimode data. Various nonparametric approaches have been proposed for the design of multivariate control charts, but the monitoring of multimode processes remains a challenge for most of them. In this study, we investigate the use of distribution-free MSPC methods based on statistical learning tools. In this work, we compared the kernel distance-based control chart (K-chart) based on a one-class-classification variant of support vector machines and a fuzzy neural network method based on the adaptive resonance theory. The performances of the two methods were evaluated using both Monte Carlo simulations and real industrial data. The simulated scenarios include different types of out-of-control conditions to highlight the advantages and disadvantages of the two methods. Real data acquired during a roll grinding process provide a framework for the assessment of the practical applicability of these methods in multimode industrial applications

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

Manufacturing of high precision mechanical components

The main goal of the thesis is to analyze key aspects of Precision Manufacturing, aiming at optimizing critical manufacturing processes: innovative experimental methodologies and advanced modelling techniques will be applied to cases study of industrial interest which have been successfully optimized

Development of chatter threshold boundary for milling of metals

This study reports on a novel experimental method for the prediction of chatter based on the chaos theory. The variation of Poincaré sections of the reconstructed phase space attractor is able to identify the transition of the milling system from a stable to an unstable condition, continuously during the milling process. Two mathematical tools are used to measure the variation of Poincaré sections they being; image correlation and a designed regression model. Image correlation uses Poincaré sections as a pattern and the computation of Pearson’s coefficient assists to develop a chatter threshold boundary. Titanium is the main material in this research, as chatter is more applicable during cutting of titanium due to its specific mechanical properties. Moreover, the method is used in detection of chatter during milling of stainless steel and aluminum in order to demonstrate the method can detect chatter during cutting of other metals. The new method can be used to detect chatter on-line, as it is independent of the cutting parameters and dynamics of the milling process, and can be integrated in the cutting machines. The method does not need expensive equipment and complex process, so it can be easily used in normal production workshop environment. A regression model computes the trend of changes in the Poincaré sections and gives a numerical output value to define the boundary between the stable and unstable state of the milling process. These mathematical tools can be used in expert software to monitor the milling process on-line and detect the onset of chatter

High Definition Metrology based Process Control: Measurement System Analysis and Process Monitoring.

Process control in high precision machining necessitates high-definition metrology (HDM) systems that provide fine resolution data needed to characterize surface shape. HDM data is critical for the evaluation of process surface variation, as it reveals local surface patterns that are undetectable using low definition metrology (LDM) systems. Monitoring of the part-to-part variation of these patterns identified by HDM enables the detection of abnormal surface variation and the degradation of process conditions. HDM systems present many opportunities for surface variation reduction. However, there are challenges to using HDM data for process control. Conventional HDM systems are inefficient and may take a long time to measure a part, such that sufficient samples cannot be obtained for process control purposes. In addition, conventional monitoring methods are difficult to implement due to the high density of data. A new study uncovered significant cross-correlations between part surface height and process variables in an automotive engine milling process. This dissertation aims to apply new insights gained from HDM to develop algorithms and methods for surface variation control, specifically: - Surface modeling through fusion of process variables and HDM data: An improved surface model is developed by incorporating process and multi-resolution data through spatial and cross-correlation to increase prediction accuracy and reduce the amount of HDM measurements necessary for process control. - Measurement system analysis for HDM using: A method to effectively estimate the gage capability for HDM systems is proposed. - Surface variation monitoring using HDM data: A sequential monitoring framework is developed to monitor surface variations as reflected by HDM data. Based on the surface data-process fusion model, a progressive monitoring algorithm under a Bayesian framework is developed to monitor surface variations when limited HDM measurements are available. - Multistage modeling and monitoring of HDM Data: A morphing-based approach is proposed to model process multistage interdependence. A new multistage monitoring procedure is developed based on the morphing model. The research presented in this dissertation will aid in transforming quality control practices from dimensional variation reduction to surface shape variation control. The proposed HDM data monitoring algorithms can be extended to other high precision manufacturing processes.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99874/1/ssuriano_1.pd

The design of an effective sensor fusion model for condition monitoring systems of turning processes

High energy price and the increasing requirements of quality and low cost of products have created an urgent need to implement new technologies in current automated manufacturing environments. Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the essential technologies that provide the competitive advantage in many manufacturing environments. This research aims to develop an effective sensor fusion model for turning processes for the detection of tool wear. Multi-sensors combined with a novelty detection algorithm and Learning Vector Quantisation (LVQ) neural networks are used in this research to detect tool wear and provide diagnostic and prognostic information. A novel approach, termed ASPST, (Automated Sensor and Signal Processing Selection System for Turning) is used to select the most appropriate sensors and signal processing methods. The aim is to reduce the number of sensors needed in the overall system and reduce the cost. The ASPST approach is based on simplifying complex sensory signals into a group of Sensory Characteristic Features (SCFs) and evaluating the sensitivity of these SCFs in detecting tool wear. A wide range of sensory signals (cutting forces, strain, acceleration, acoustic emission and sound) and signal processing methods are also implemented to verify the capability of the approach. A cost reduction method is also implemented based on eliminating the least utilised sensor in an attempt to reduce the overall cost of the system without sacrificing the capability of the condition monitoring system. The experimental results prove that the suggested approach provides a responsive and effective solution in monitoring tool wear in turning with reduced time and cost

Robust tool condition monitoring in Ti6Al4V milling based on specific force coefficients and growing self-organizing maps

Tool condition monitoring (TCM) is a mean to optimize production systems trying to use cutting tool life at its best. Nevertheless, nowadays available TCM algorithms typically lack robustness in order to be consistently applied in industrial scenarios. In this paper, an unsupervised artificial intelligence technique, based on Growing Self-Organizing Maps (GSOM), is presented in synergy with real-time specific force coefficients (SFC) estimation through the regression of instantaneous cutting forces. The conceived approach allows robustly mapping the SFC, exploiting process parameters and similarity to manage the variability of their estimation due to unmodelled phenomena, like machine dynamics and tool run-out. The devised approach allowed detecting the tool end-of-life in cutting tests with variable lubrication, machine tool and cutting speed, through the adoption of a self-starting control chart running on real-time clustered data. The solution was validated through the comparison of the GSOM framework with respect to the optimized self-starting control chart applied without GSOM clustering. The GSOM reached a root mean squared percentage error (RMSPE) of 13.2% with respect to 56.1% obtained with the analogous control chart in a full-set optimization scenario. When optimised on tests for a unique machine tool and tested on another machine tool, GSOM scored an RMSPE of 34.5%, whereas the optimized control chart scored 64.5%

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

Multi-angle valve seat machining: experimental analysis and numerical modelling

Modern automotive manufacturers operate in highly competitive markets, heavily influenced by Government regulation and ever more environmentally conscious consumers. Modern high-temperature, high-pressure engines that use high hardness multi-angle valve seats are an attractive environmental option, but one that manufacturers find requires more advanced materials and tighter geometric tolerances to maintain engine performance.Tool manufacturers meet these increasingly tougher demands by using, higher hardness cutting materials such as polycrystalline cubic boron nitride (pcBN), that on paper, promise to wear at a lower rate, require less coolant and deliver tighter tolerances than their carbide counterparts.The low brittle fracture toughness of pcBN makes tools that use it vulnerable to minute chipping. A review of literature for this work pointed to no clear answer to this problem, although suggestions range from manufacturing defects, dynamic and flexibility problems with the production line machinery and fixtures, and radial imbalances in the cutting loads.This work set about experimentally investigating those potential explanations, coming to the conclusion that the high radial imbalance of the cutting loads is responsible for pcBN cutting insert failure during multi-angle valve seat machining, and that by simply relocating the cutting inserts around the multi angle cutting tool, the imbalance can be reduced, thus extending the life of the cutting inserts.It is not always easy to predict the imbalance due to the multiple flexibilities in the system, and simulating such a system in 3D with all its associated cutting phenomena such as friction, thermal expansion, chip flow and shearing, would call upon extraordinary computational power and extremely precise experimental inputs to reduce cumulative error.This thesis proves that such a 3D simulation can be made, that runs in exceptionally short durations compared to traditional methods, by making a number of simplifications.MSC Marc was used to host the simulation, with a parametric script written in Python responsible for generating the model geometry and cutter layout. A Fortran program was developed that is called upon by Marc to calculate the required cutting load outputs and generate new workpiece meshes as material is removed.</div