Machining process classification using PCA reduced histogram features and the support vector machine

Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoidance of human errors due to inconsistency and fatigue. In this paper, the Support Vector Machine (SVM) classifier with various kernels is investigated for the categorization of machined surfaces into the six machining processes of Turning, Grinding, Horizontal Milling, Vertical Milling, Lapping, and Shaping. The effectiveness of the gray-level histogram as the discriminating feature is explored. Experimental results suggest that the SVM with the linear kernel provides superior performance for a dataset consisting of 72 workpiece images

Application of advanced regression methods for wear prediction of superalloys

Analytical models able to predict the tool wear can provide companies instruments to optimize the cutting processes. The focus of this thesis is to accomplish a study of the tool wear process in the turning process of superalloys, including its dependence on multiple factors related to the characteristics of the workpiece and machinery used for turning. As a natural extension of this study we propose the application of some statistical and machine learning techniques to address the prediction of the tool wear. Data corresponding to different tests carried out as part of the European project called Himmoval is used. The process of prediction involves selecting features from the variables acquired by different sensors that characterize the machining process. Additionally, several machine learning algorithms are implemented and applied to analyze the data from the wear experiments. Among these algorithms, Gradient Boosting Regressor predominates over the rest of regression methods evaluated.Tecnali

Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities

The Detection of Stress Corrosion Cracking in Natural Gas Pipelines Using Electromagnetic Acoustic Transducers

This thesis describes the refinement of a non-destructive, in-line inspection system sensor for the detection of stress corrosion cracks (SCCs) in natural gas pipelines. The sensors are prototype electromagnetic acoustic transducers (EMATs) for noncontact ultrasonic inspection. The focus areas discussed involve the statistically validated performance improvements achieved through the addition of 12 more features, the addition of Principal Component Analysis plus Linear Discriminant Analysis (PCA+LDA) to the classification algorithm, and most significantly the creating of a training set. The training set allowed PCA+LDA to be included in the classification algorithm, as well as allowing one set of no-flaw signature features, one PCA projection matrix, and one LDA projection matrix to be used on multiple pipes and on multiple scanned paths from a pipe. A discrete wavelet decomposition is used to separate the frequency content of each EMAT sample (signature) into five distinct bands. From these decomposed signatures, features are extracted for classification. The classification begins with the projection of the features using the PCA projection matrix derived from the training set, immediately followed by the projection of the PCA projected features using the LDA projection matrix that was also derived from the training set. Finally, the PCA+LDA projected features are classified based on their Mahalanobis distances from the PCA+LDA projected no-flaw training set features. Using the improved feature set and this classification procedure, SCC identification improved 14% and there was an 80% reduction in the number of false positives. In addition, there was a 30% improvement in the detection of the most critical SCCs. SCCs whose average through wall depths were between 35% and 54%

Eddy current defect response analysis using sum of Gaussian methods

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics

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

소비전력 측정을 통한 밀링 공정의 가공 모니터링

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 안성훈.공구 모니터링은 공구의 상태를 진단하거나 공구의 파괴를 진단, 예측하는 데 필수 요소이다. 밀링 가공의 중단시간 중 7-20%가 공구 파괴로 인한 것이며 공정 비용의 3-12%가 공구 파괴로 인한 비용이다. 그 외에 공구 마모로 인한 품질저하 등 간접적인 비용 또한 공정비용을 증가시키는 요인으로 작용한다. 기존 공구 교체 전략들은 많은 비용을 요하거나 중단시간을 필요로 하여 적용하기 어려운 면이 있다. 본 연구는 외부에 부착할 수 있는 모듈로 CNC 밀링 머신의 총 전력소모를 측정하여 저렴하고 중단시간 없는 무선 모니터링시스템을 제안한다. 이를 위해 간단한 형상의 마모가 진행된 공구와 새 공구를 이용해 밀링 공정을 실행하여 공구 마모가 총 전력소모에 어떻게 영향을 미치는 지 알아보았다. 전력 측정은 전류센서와 전압센서가 연결된 아두이노로 측정을 하였고 측정된 데이터는 MQTT를 이용해 Wi-Fi로 전송되었다. G&M code 시뮬레이션을 이용해 전력 소모 프로파일을 가공공정과 일치시켰다. 수학적 모델링을 이용한 모델과 SVM을 이용한 모델을 제안하고 테스트하였다.Tool condition monitoring is crucial in accurately diagnosing tool wear and detecting or preventing tool failure. 7-20% of total milling-machine downtime is due to tool failure and 3-12% of total processing cost comes from tool change costs. In addition, indirect costs due to poor surface quality can be added with the absence of a monitoring system. Conventional tool monitoring systems are difficult to implement due to high costs or the need for downtime. This thesis proposes a low-cost wireless monitoring system with very little downtime for implementation that can deduce the state of the tool with the monitoring of power consumed by a CNC milling machine.Chapter 1. Introduction ........................................................ 1 1.1. Study Background ........................................................... 1 1.2. Purpose of Research ....................................................... 4 Chapter 2. Hardware ........................................................... 5 2.1. System Layout ................................................................. 5 2.2. System Design ................................................................. 6 Chapter 3. Experiments and Results ................................. 12 3.1. Early Experiments ......................................................... 12 3.2. Mathematical Model ....................................................... 19 3.3. Machine Learning Model ............................................... 28 3.4. Summary ........................................................................ 31 Chapter 4. Conclusion ........................................................ 32 Reference........................................................................... 33 Abstract in Korean ............................................................ 37Maste