Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system

This paper outlines a comparative study to compare between using the power of the spindle and the infrared images of the cutting tool to design a condition monitoring system. This paper compares the two technologies for the development of a tool condition monitoring for milling processes. Wavelet analysis is used to process the power signal. Image gradient and Wavelet analyses are used to process the infrared images. The results show that the image gradient and wavelet analysis are powerful image processing techniques in detecting tool wear. The power of the motor of the spindle has shown less sensitivity to tool conditions in this case when compared to infrared thermography

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Chip Production Rate and Tool Wear Estimation in Micro-EndMilling

abstract: In this research, a new cutting edge wear estimator for micro-endmilling is developed and the reliabillity of the estimator is evaluated. The main concept of this estimator is the minimum chip thickness effect. This estimator predicts the cutting edge radius by detecting the drop in the chip production rate as the cutting edge of a micro- endmill slips over the workpiece when the minimum chip thickness becomes larger than the uncut chip thickness, thus transitioning from the shearing to the ploughing dominant regime. The chip production rate is investigated through simulation and experiment. The simulation and the experiment show that the chip production rate decreases when the minimum chip thickness becomes larger than the uncut chip thickness. Also, the reliability of this estimator is evaluated. The probability of correct estimation of the cutting edge radius is more than 80%. This cutting edge wear estimator could be applied to an online tool wear estimation system. Then, a large number of cutting edge wear data could be obtained. From the data, a cutting edge wear model could be developed in terms of the machine control parameters so that the optimum control parameters could be applied to increase the tool life and the machining quality as well by minimizing the cutting edge wear rate. In addition, in order to find the stable condition of the machining, the stabillity lobe of the system is created by measuring the dynamic parameters. This process is needed prior to the cutting edge wear estimation since the chatter would affect the cutting edge wear and the chip production rate. In this research, a new experimental set-up for measuring the dynamic parameters is developed by using a high speed camera with microscope lens and a loadcell. The loadcell is used to measure the stiffness of the tool-holder assembly of the machine and the high speed camera is used to measure the natural frequency and the damping ratio. From the measured data, a stability lobe is created. Even though this new method needs further research, it could be more cost-effective than the conventional methods in the future.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

Measurement of micro burr and slot widths through image processing: Comparison of manual and automated measurements in micro‐milling

In this study, the burr and slot widths formed after the micro‐milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user‐defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open‐sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user‐friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation

A Vision-Based Quality Control Model for Manufacturing Systems

A thesis presented to the faculty of the College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Alejandra Figueroa-Lopez on November 25, 2021

Burr detection and classification using RUSTICO and image processing

.Machined workpieces must satisfy quality standards such as avoid the presence of burrs in edge finishing to reduce production costs and time. In this work we consider three types of burr that are determined by the distribution of the edge shape on a microscopic scale: knife-type (without imperfections), saw-type (presence of small splinters that could be accepted) and burr-breakage (substantial deformation that produces unusable workpieces). The proposed method includes RUSTICO to classify automatically the edge of each piece according to its burr type. Experimental results validate its effectiveness, yielding a 91.2% F1-Score and identifying completely the burr-breakage type.S

Intelligent machining methods for Ti6Al4V: a review

Digital manufacturing is a necessity to establishing a roadmap for the future manufacturing systems projected for the fourth industrial revolution. Intelligent features such as behavior prediction, decision- making abilities, and failure detection can be integrated into machining systems with computational methods and intelligent algorithms. This review reports on techniques for Ti6Al4V machining process modeling, among them numerical modeling with finite element method (FEM) and artificial intelligence- based models using artificial neural networks (ANN) and fuzzy logic (FL). These methods are intrinsically intelligent due to their ability to predict machining response variables. In the context of this review, digital image processing (DIP) emerges as a technique to analyze and quantify the machining response (digitization) in the real machining process, often used to validate and (or) introduce data in the modeling techniques enumerated above. The widespread use of these techniques in the future will be crucial for the development of the forthcoming machining systems as they provide data about the machining process, allow its interpretation and quantification in terms of useful information for process modelling and optimization, which will create machining systems less dependent on direct human intervention.publishe

Analysis and modeling of depth-of-cut during end milling of deposited material

This study addresses depth-of-cut detection and tool-workpiece engagement using an acoustic emission monitoring system during milling machining for a deposited material. Online detection of depth-of-cut presents many technical difficulties. Researchers have used various types of sensors and methods to assess the depth-of-cut and surface errors. Due to the strong correlation between acoustic emission and cutting depth during the depth end milling process, it is useful to forecast the depth-of-cut from the acoustic emission signal. This work used regression analysis to model and detect the depth-of-cut. The experiments were carried out on a Fadal vertical 5-Axis computer numerical control machine using a carbide end-mill tool, and a piezoelectric sensor (Kistler 8152B211) was used to acquire the acoustic emission signal. A National Instruments real-time system, combined with a National Instruments LabVIEW graphical development environment, was used as a data acquisition system. A series of experiments were conducted to create a depth-of-cut model. The inputs were used to predict depth-of cut are the identified root mean square of the acoustic emission, spindle speed, feed rate, and tool status. The effects of these inputs were evaluated using a fractional factorial design-of-experiment approach --Abstract, page iii

CNN YOLO를 이용한 열화상기반 영상과 점구름 기반 엔드밀 감시 시스템

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022. 8. 안성훈.As adoption of smart-factory system in manufacturing becoming inevitable, autonomous monitoring system in the field of machining has become viral nowadays. Among various methods in autonomous monitoring, vision-based monitoring is the most sought-after. This system uses vision sensors integrated with detection models developed through deep learning. However, the disadvantage of being greatly affected by optical conditions, such as ambient lighting or reflective materials, critically affects the performance in terms of monitoring. Instead of vision sensors, LiDAR, which provides depth map by measuring light returning time using infrared radiation (IR) directly to the object, can be complementary method. The study presents a LiDAR ((Light Detection and Ranging)-based end mill state monitoring system, which renders strengths of both vision and LiDAR detecting. This system uses point cloud and IR intensity data acquired by the LiDAR while object detection algorithm developed based on deep learning is engaged during the detection stage. The point cloud data is used to detect and determine the length of the endmill while the IR intensity is used to detect the wear present on the endmill. Convolutional neural network based You Only Look Once (YOLO) algorithm is selected as an object detection algorithm for real-time monitoring. Also, the quality of point cloud has been improved using data prep-processing method. Finally, it is verified that end mill state has been monitored with high accuracy at the actual machining environment.제조 분야에서 스마트 팩토리 시스템의 도입으로 인해 가공 과정의 무인 모니터링 시스템이 필연적으로 도입되고 있다. 무인 모니터링의 다양한 방법 중 비전 기반 모니터링이 가장 많이 쓰이고 있다. 해당 비전 기반 시스템의 경우 딥 러닝을 통해 개발된 감지 모델과 통합된 비전 센서를 사용한다. 하지만 주변 조명이나 반사 물질과 같은 광학적 조건에 크게 영향을 받는 단점은 모니터링 측면에서 성능에 치명적인 영향을 미치기에 이를 보완하는 대안이 필요하다. 이 연구에서는 비전 센서 대신 적외선(IR)을 물체에 직접 조사하여 빛의 왕복 시간을 측정하여 깊이 정보를 측정하는 LiDAR를 이용하여 비전 센서의 한계를 보완하는 시스템을 소개한다. 또한 비전과 LiDAR 감지의 장점을 모두 제공하는 LiDAR 기반 엔드밀 상태 모니터링 시스템을 제시한다. 이 시스템은 LiDAR에서 획득한 점 구름 정보 및 IR 강도 데이터를 사용하며, 딥 러닝을 기반으로 개발된 객체 감지 알고리즘은 감지 단계와 엔드밀의 길이를 감지하고 측정하는 데 사용되며 IR 강도는 엔드밀에 존재하는 마모 혹은 파손 정보를 감지하는 데 사용된다. 실시간 모니터링을 위한 객체 감지 알고리즘으로 YOLO(You Only Look Once) 알고리즘을 기반으로 하는 컨볼루션 신경망이 선택되었으며 데이터 전처리를 통해 포인트 클라우드의 품질을 향상시켰다. 마지막으로 실제 가공 환경에서 엔드밀 상태를 높은 정확도로 모니터링하는 과정을 진행하였다.1. Introduction . 1 1.1 Tool monitoring in CNC machines 1 1.2 LiDAR and point cloud map. 5 1.3 IR intensity application 7 2. System modelling 9 2.1 End mill monitoring system overview 9 2.2 Hardware setup . 11 2.3 End mill failure monitoring 15 2.4 YOLO setup 18 3. Data processing . 19 3.1 Confidence score. 19 3.2 Noise removal 20 3.3 Point cloud accumulation. 22 3.4 IR intensity monitoring 26 4. Experiments and results . 28 4.1 Data gathering 28 4.2 Training 30 4.3 Results . 32 5. Conclusion . 39 Reference 41 Abstract (In Korean) 43석