Sensor based real-time process monitoring for ultra-precision manufacturing processes with non-linearity and non-stationarity

This research investigates methodologies for real-time process monitoring in ultra-precision manufacturing processes, specifically, chemical mechanical planarization (CMP) and ultra-precision machining (UPM), are investigated in this dissertation.The three main components of this research are as follows: (1) developing a predictive modeling approaches for early detection of process anomalies/change points, (2) devising approaches that can capture the non-Gaussian and non-stationary characteristics of CMP and UPM processes, and (3) integrating multiple sensor data to make more reliable process related decisions in real-time.In the first part, we establish a quantitative relationship between CMP process performance, such as material removal rate (MRR) and data acquired from wireless vibration sensors. Subsequently, a non-linear sequential Bayesian analysis is integrated with decision theoretic concepts for detection of CMP process end-point for blanket copper wafers. Using this approach, CMP polishing end-point was detected within a 5% error rate.Next, a non-parametric Bayesian analytical approach is utilized to capture the inherently complex, non-Gaussian, and non-stationary sensor signal patterns observed in CMP process. An evolutionary clustering analysis, called Recurrent Nested Dirichlet Process (RNDP) approach is developed for monitoring CMP process changes using MEMS vibration signals. Using this novel signal analysis approach, process drifts are detected within 20 milliseconds and is assessed to be 3-7 times faster than traditional SPC charts. This is very beneficial to the industry from an application standpoint, because, wafer yield losses will be mitigated to a great extent, if the onset of CMP process drifts can be detected timely and accurately.Lastly, a non-parametric Bayesian modeling approach, termed Dirichlet Process (DP) is combined with a multi-level hierarchical information fusion technique for monitoring of surface finish in UPM process. Using this approach, signal patterns from six different sensors (three axis vibration and force) are integrated based on information fusion theory. It was observed that using experimental UPM sensor data that process decisions based on the multiple sensor information fusion approach were 15%-30% more accurate than the decisions from individual sensors. This will enable more accurate and reliable estimation of process conditions in ultra-precision manufacturing applications

Monitoring of tool wear in turning operations using vibration measurements

This study investigates the use of vibration and strain measurements on machine tools in order to identify the propagating wear of the selected tools. Two case studies are considered, one of which was conducted in the plant of a South African piston manufacturer. The purpose of the ftrst case study was to investigate the feasibility of vibration monitoring to identify tool wear, before attempting to implement a monitoring system in the manufacturing plant. During this case study, data from a turning process was recorded using two accelerometers coupled to a PL202 real time FFT analyser. Features indicative of tool wear were extracted from the sensor signals, and then used as inputs to a Self-Organising Map (SOM). The SOM is a type of neural network based on unsupervised learning, and can be used to classify the input data into regions corresponding to new and worn tools. It was also shown that the SOM can also be used very efficiently with discrete variables. One of the main contributions of the second case study was the fact that a unique type of tool was investigated, namely a synthetic diamond tool specifically used for the manufacturing of aluminium pistons. Data from the manufacturing of pistons was recorded with two piezoelectric strain sensors and a single accelerometer, all coupled to a DSPT Siglab analyser. A large number of features indicative of tool wear were automatically extracted from different parts of the original signals. These included features from time and frequency domain data, time series model coefficients as features and features extracted from wavelet packet analysis. A correlation coefficient approach was used to auto-lJUltically select the best features indicative of the progressive wear of the diamond tools. The SOM was once again used to identify the tool state. Some of the advantages of using different map sizes on the SOM were also demonstrated. A near 100% correct classification of the tool wear data was obtained by training the SOM with two independent data sets, and testing it with a third independent data set. It was also shown that the monitoring strategy proposed in the second case study can be fully automated and can be implemented on-line if the manufacturer wishes to. Some of the contributions of this study are the use of the SOM for tool wear classification, and conclusions regarding the wear modes of the synthetic diamond tools.Dissertation (M Eng (Mechanical Engineering))--University of Pretoria, 2006.Mechanical and Aeronautical Engineeringunrestricte

Optical diamond turning of rapidly solidified aluminium alloy grade - 431

The high demand for ultraprecision machining systems is increasing day by day. The technology leads to increased productivity and quality manufactured products, with an excellent surface finish. Therefore, these products are in demand in many industrial fields such as space, national defence, the medical industry and other high-tech industries. Single point diamond turning (SPDT) is the core technology of ultraprecision machining, which makes use of single-point crystalline diamond as a cutting tool. This technique is used for machining an extensive selection of complex optical surfaces and other engineering products with a quality surface finish. SPDT can achieve dimensional tolerances in order of 0.01um and surface roughness in order of 1nm. SPDT is not restricted, but mostly applicable, to non-ferrous alloys; due to their reflective properties and microstructure that discourages tool wear. The focus of this study is the development of predictive optimisation models, used to analyse the influence of machining parameters (speed, feed, and depth of cut) on surface roughness. Moreover, the study aims to obtain the optimal machining parameters that would lead to minimum surface roughness during the diamond turning of Rapidly Solidified Aluminium (RSA) 431. In this study, Precitech Nanoform 250 Ultra grind machine was used to perform two experiments on RSA 431. The first machining process, experiment 1, was carried out using pressurized kerosene mist; while experiment 2 was carried out with water as the cutting fluid. In each experiment, machine parameters were varied at intervals and the surface roughness of the workpiece was measured at each variation. The measurements were taken through a contact method using Taylor Hobson PGI Dimension XL surface Profilometer. Acoustic emission (AE) was employed as a precision sensing technique – to optimize the machining quality process and provide indications of the expected surface roughness. The results obtained revealed that better surface roughness can be generated when RSA 431 is diamond-turned using water as a cutting fluid, rather than kerosene mist. Predictive models for surface roughness were developed for each experiment, using response surface methodology (RSM) and artificial neural networks (ANN). Moreover, RSM was used for optimisation. Time domain features acquired from AE signals, together with the three cutting parameters, were used as input parameters in the ANN design. The results of the predictive models show a close relationship between the predicted values and the experimental values for surface roughness. The developed models have been compared in terms of accuracy and cost of computation - using the mean absolute percentage error (MAPE)

Quality and inspection of machining operations: Review of condition monitoring and CMM inspection techniques 2000 to present

In order to consistently produce quality parts, many aspects of the manufacturing process must be carefully monitored, controlled, and measured. The methods and techniques by which to accomplish these tasks has been the focus of numerous studies in recent years. With the rapid advances in computing technology, the complexity and overhead that can be feasibly incorporated in any developed technique has dramatically improved. Thus, techniques that would have been impractical for implementation just a few years ago can now be realistically applied. This rapid growth has resulted in a wealth of new capabilities for improving part and process quality and reliability. In this paper, overviews of recent advances that apply to machining are presented. Moreover, due to the relative significance of two particular machining aspects, this review focuses specifically on research publications pertaining to using tool condition monitoring and coordinate measurement machines to improve the machining process. Tool condition has a direct effect on part quality and is discussed first. The application of tool condition monitoring as it applies to turning, drilling, milling, and grinding is presented. The subsequent section provides recommendations for future research opportunities. The ensuing section focuses on the use of coordinate measuring machines in conjunction with machining and is subdivided with respect to integration with machining tools, inspection planning and efficiency, advanced controller feedback, machine error compensation, and on-line tool calibration, in that specific order and concludes with recommendations regarding where future needs remain

Tool wear monitoring in machining of stainless steel

monitoring systems for automated machines must be capable of operating on-line and interpret the working condition of machining process at a given point in time because it is an automated and unmanned system. But this has posed a challenge that lead to this research study. Generally, optimization of machining process can be categorized as minimization of tool wear, minimization of operating cost, maximization of process output and optimization of machine parameter. Tool wear is a complex phenomenon, capable of reducing surface quality, increases power consumption and increased reflection rate of machined parts. Tool wear has a direct effect on the quality of the surface finish for any given work-piece, dimensional precision and ultimately the cost of parts produced. Tool wear usually occur in combination with the principal wear mode which depends on cutting conditions, tool insert geometry, work piece and tool material. Therefore, there is a need to develop a continuous tool monitoring systems that would notify operator the state of tool to avoid tool failure or undesirable circumstances. Tool wear monitoring system for macro-milling has been studied using design and analysis of experiment (DOE) approach. Regression analysis, analysis of variance (ANOVA), Box Behnken and Response Surface Methodology (RSM). These analysis tools were used to model the tool wear. Hence, further investigations were carried out on the data acquired using signal processing and Neural networks frame work to validate the model. The effects of cutting parameters are evaluated and the optimal cutting conditions are determined. The interaction of cutting parameters is established to illustrate the intrinsic relationship between cutting parameters, tool wear and material removal rate. It was observed that when working with stainless steel 316, a maximum tool wear value of 0.29mm was achieved through optimization at low values of feed about 0.06mm/rev, speed of 4050mm/min and depth of cut about 2mm

Frontiers in Ultra-Precision Machining

Ultra-precision machining is a multi-disciplinary research area that is an important branch of manufacturing technology. It targets achieving ultra-precision form or surface roughness accuracy, forming the backbone and support of today’s innovative technology industries in aerospace, semiconductors, optics, telecommunications, energy, etc. The increasing demand for components with ultra-precision accuracy has stimulated the development of ultra-precision machining technology in recent decades. Accordingly, this Special Issue includes reviews and regular research papers on the frontiers of ultra-precision machining and will serve as a platform for the communication of the latest development and innovations of ultra-precision machining technologies

Tool wear monitoring in end milling of mould steel using acoustic emission

Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features

Tool wear monitoring in end milling of mould steel using acoustic emission

Today’s production industry is faced with the challenge of maximising its resources and productivity. Tool condition monitoring (TCM) is an important diagnostic tool and if integrated in manufacturing, machining efficiency will increase as a result of reducing downtime resulting from tool failures by intensive wear. The research work presented in the study highlights the principles in tool condition monitoring and identifies acoustic emission (AE) as a reliable sensing technique for the detection of wear conditions. It reviews the importance of acoustic emission as an efficient technique and proposes a TCM model for the prediction of tool wear. The study presents a TCM framework to monitor an end-milling operation of H13 tool steel at different cutting speeds and feed rates. For this, three industrial acoustic sensors were positioned on the workpiece. The framework identifies a feature selection, extraction and conditioning process and classifies AE signals using an artificial neural network algorithm to create an autonomous system. It concludes by recognizing the mean and rms features as viable features in the identification of tool state and observes that chip coloration provides direct correlation to the temperature of machining as well as tool condition. This proposed model is aimed at creating a timing schedule for tool change in industries. This model ultimately links the rate of wear formation to characteristic AE features

Cutting tool wear progression index via signal element variance

This paper presents a new statistical-based method of cutting tool wear progression in a milling process called Z-rotation method in association with tool wear progression. The method is a kurtosis-based that calculates the signal element variance from its mean as a measurement index. The measurement index can be implicated to determine the severity of wear. The study was conducted to strengthen the shortage in past studies notably considering signal feature extraction for the disintegration of non-deterministic signals. The Cutting force and vibration signals were measured as a tool of sensing element to study wear on the cutting tool edge at the discrete machining conditions. The monitored flank wear progression by the value of the RZ index, which then outlined in the model data pattern concerning wear and number of samples. Throughout the experimental studies, the index shows a significant degree of nonlinearity that appears in the measured impact. For that reason, the accretion of force components by Z-rotation method has successfully determined the abnormality existed in the signal data for both force and vibration. It corresponds to the number of cutting specifies a strong correlation over wear evolution with the highest correlation coefficient of R2 = 0.8702 and the average value of R2 = 0.8147. The index is more sensitive towards the end of the wear stage compared to the previous methods. Thus, it can be utilised to be the alternative experimental findings for monitoring tool wear progression by using threshold values on certain cutting condition