CNC spindle signal investigation for the prediction of cutting tool health

The deterioration of cutting tools plays a significant role in the progression of subtractive manufacturing and substantially affects the quality of machined parts. Recognising this most organisations have implemented conventional methods for tool management. These reduce the economic loss associated with time-dependent and stochastic tool wear, and limit the damage arising from tools at end-of-life. However, significant costs still remain to be addressed and more development towards tool and process prognostics is desirable. In response, this work investigates process deterioration through the acquisition and processing of selected machine signals. This utilises the internal processor of a CNC Vertical Machining Centre and considers the possible applications of such an approach for the prediction of tool and process health. This paper considers the prediction of tool and process condition with a discussion of the assumptions, benefits, and limitations of such approaches. Furthermore, the efficacy of the approach is tested using the correlation between an offline measurement of part accuracy and an active measure of process variation

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

CNC spindle signal investigation for the prediction of cutting tool health

The deterioration of cutting tools plays a significant role in the progression of subtractive manufacturing and substantially affects the quality of machined parts. Recognising this most organisations have implemented conventional methods for tool management. These reduce the economic loss associated with time-dependent and stochastic tool wear, and limit the damage arising from tools at end-of-life. However, significant costs still remain to be addressed and more development towards tool and process prognostics is desirable. In response, this work investigates process deterioration through the acquisition and processing of selected machine signals. This utilises the internal processor of a CNC Vertical Machining Centre and considers the possible applications of such an approach for the prediction of tool and process health. This paper considers the prediction of tool and process condition with a discussion of the assumptions, benefits, and limitations of such approaches. Furthermore, the efficacy of the approach is tested using the correlation between an offline measurement of part accuracy and an active measure of process variation

Indirect monitoring of surface quality based on the integration of support vector machine and 3D I-kaz techniques in the machining process

Improved machining process quality can contribute to sustainable manufacturing in terms of economic, environmental, and social sustainability. Reducing waste, increasing efficiency, and improving product quality can also help manufacturers to reduce costs and increase productivity rate. Machining is one of the common methods in industry and plays a central role in modern manufacturing. For many years, researchers have been studying monitoring methods to produce the best surface quality. The measurement involves three distinct techniques, which are categorised into quantitative and visualisation methods. Monitoring methods can be classified as either direct or indirect methods. The common method of measuring machining quality undergoes manufacturing bottlenecks, as it is constrained by human inspection and expensive equipment. A slow process leads to higher labour costs and a high risk of equipment damage to the workpiece. The present study aims to bridge this gap by leveraging the capabilities of 3D I-kaz and medium Gaussian SVM models to improve accuracy and classification rates for determining surface quality. The specific objectives are to analyse the impact of machining parameters on statistical analysis, classify acceleration signals for surface roughness identification using SVM, integrate SVM with 3D I-kaz to improve surface quality identification and validate its effectiveness through experiments. The quantification of signal processing for ductile iron, FCD450 material on cutting parameters: rotation speed with 1000–3026 rev/mm, feed rate of 120–720 mm/min, axial of 0.75–3.5 mm, and radial depth of cut (RDOC) is studied and validated through experiments under dry and minimum quantity lubrication (MQL) conditions. Surface roughness was measured to verify the acceleration signal, while Pearson’s correlation coefficient was used to evaluate the correlation strength between the acceleration signal and surface roughness. The calculated coefficient, r-value, was found to be 0.6543, which indicates a positive but nonlinear correlation between the acceleration signal and surface roughness. The kurtosis value measured from acceleration signals and surface roughness information was then used to classify the machining condition and identification of the surface quality. In the first experiment, the model displayed an accuracy of 84.87% and 84.57% in terms of F1 values. It was observed that by adjusting the hyperparameter, the model’s accuracy was augmented to 85.53% and its F1 score was enhanced to 84.93%. Additionally, the model was applied in the second experiment, resulting in an accuracy of 84.0%. Before the classification of machined surface condition, the condition is identified through the support vector machine (SVM) technique, and it was demonstrated that the condition could be demarcated into five different levels of surface quality. From the experimental test data, acceleration and average roughness (Ra)-based indicators are identified for correlation analysis. A relation is developed, which enables the prediction or identification of surface quality directly based on the selected based indicators (3D I-kaz coefficient) without having to inspect the milling process for surface roughness. It was demonstrated that the integration of the 3D I-kaz and SVM model resulted in an accuracy and F1 score of 96.0% and 96.3% respectively, suggesting that the quantification data is viable for surface quality identification. A monitoring experiment was conducted in this study to validate the identification of surface quality through the instantaneous surface roughness level obtained from the experiment. In conclusion, indirect monitoring of surface quality using vibration signals can quickly identify the surface quality using SVM and 3D I-kaz analyses, thus reducing the time and cost associated with manual inspection and allowing for its use in many other machining processes

Ensemble Machine Learning Model Generalizability and its Application to Indirect Tool Condition Monitoring

A practical, accurate, robust, and generalizable system for monitoring tool condition during a machining process would enable advancements in manufacturing process automation, cost reduction, and efficiency improvement. Previously proposed systems using various individual machine learning (ML) models and other analysis techniques have struggled with low generalizability to new machining and environmental conditions, as well as a common reliance on expensive or intrusive sensory equipment which hinders their industry adoption. While ensemble ML techniques offer significant advantages over individual models in terms of performance, overfitting reduction, and generalizability improvement, they have only begun to see limited applications within the field of tool condition monitoring (TCM). To address the research gaps which currently surround TCM system generalizability and optimal ensemble model configuration for this application, nine ML model types, including five heterogeneous and homogeneous ensemble models, are employed for tool wear classification. Sound, spindle power, and axial load signals are utilized through the sensor fusion of practical external and internal machine sensors. This original experimental process data is collected through tool wear experiments using a variety of machining conditions. Four feature selection methods and multiple tool wear classification resolution values are compared for this application, and the performance of the ML models is compared across metrics including k-fold cross validation and leave-one-group-out cross validation. The generalizability of the models to data from unseen experiments and machining conditions is evaluated, and a method of improving the generalizability levels using noisy training data is examined. T-tests are used to measure the significance of model performance differences. The extra-trees ensemble ML method, which had never before been applied to signal-based TCM, shows the best performance of the nine models.M.S

Dynamical systems : mathematical and numerical approaches

Proceedings of the 13th Conference „Dynamical Systems - Theory and Applications" summarize 164 and the Springer Proceedings summarize 60 best papers of university teachers and students, researchers and engineers from whole the world. The papers were chosen by the International Scientific Committee from 315 papers submitted to the conference. The reader thus obtains an overview of the recent developments of dynamical systems and can study the most progressive tendencies in this field of science