Hybrid model- and signal-based chatter detection in the milling process

Online chatter detection considering beat effect based on Inception and LSTM neural networks

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

In the nuclear power plants (NPPs), fault detection and diagnosis (FDD) methods are very important to improve the safety and reliability of plants. Researchers have established various FDD methods such as model-based methods, data-driven methods, and signal-based methods. In practical applications, model-based methods are very difficult to achieve. Thus, various data-driven methods and signal- based methods have been applied for monitoring key subsystems in NPPs. In this paper, a brief overview of the Artificial Neural Network (ANN) based FDD method is presented. Simulated data have been generated to train the ANNs as per requirement and to compare with the plant signal during a fault. A technique has been proposed analyzing two sensors data (power sensor and coolant sensor) to determine the sensor and actuator fault in a closed-loop in presence of robust (Proportional-Integral-Derivative) PID controller. Results are produced with credible MATLAB simulation.

Study on milling tool wear morphology and mechanism during machining superalloy GH4169 with PVD-TiAlN coated carbide tool

Chatter is a self-excited vibration between the workpiece and tool with negative effects. In this work, a chatter detection method is proposed based on synchrosqueezing transform (SST) of sound signals. Firstly, the SST is used to analyze the sound signals recorded with the microphone and a time-frequency representation is obtained. Then, filtering is conducted to remove the disturbance of tooth passing frequency and its harmonics in time-frequency domain. Next, singular value decomposition (SVD) method is employed to condense the TF matrix and the first-order singular value is calculated as the chatter indicator. Finally, chatter threshold is set based on 3σ criterion for the detection of chatter occurrence. The proposed method is validated with cutting tests, and the results show that the method has great potential to be used for the online chatter detection of high-speed milling process.

Chatter happens due to the low rigidity of the machining system, and chatter identification is essential for improving the surface quality and productivity. More understanding on the dynamic characteristics of the machining system would help identify chatter more efficiently. This paper proposes a method for detecting chatter in the milling process of the thin-walled workpiece by monitoring and analyzing the cutting torque. A varying chatter detection threshold based on the workpiece geometries, tool path, and dynamic characteristics is utilized to identify chatter. A high-frequency filter and a median filter are utilized for signal denoising. The method is experimentally validated, which proved that it could predict chatter efficiently at its early infancy. The method could be a supplementary to other chatter identification methods.

The CNC milling is one of the most common processes in modern manufacturing, which characterized by highly nonlinear behavior and chatter problems. As other complex processes, Chatter detection in this situation is a crucial step for improving surface quality and reducing both noise and rapid wear of the cutting tool. This paper proposes a new methodology for chatter detection in computer numerical control milling machines. The originality of this method consists for using only motor current signals picked from electrical cabinet of machine and artificial intelligent. The methodology can be decomposed into four general tasks: (1) data acquisition, (2) signal processing, (3), features generation and selection, (4) classification. In signal processing task, electrical signals are resynchronized according to the electrical cycle (60 Hz) by exploiting the cyclostationarity of electrical signals through their cyclic statistics. After that, synchronous average is computed and subtracted from original signals in order to obtain the residual part. Wiener filter is then applied on residual signals by taking as reference the residual electrical signals acquired in spindle free rotation. This procedure allows estimating a signals corresponding to the electrical part and extracting the mechanical part, which linked to a chatter phenomenon and cutting mechanism. The signal processing task is primordial in order to decrease the dynamics of the electrical fundamental component and its harmonics and also to increase the contribution of mechanical parts. Extracted features computed from mechanical parts are then ranked based on theirs entropies in which only best features are selected and presented to the system for classification. At the classification step, the selected features are classified into two classes: stable and unstable utilizing a support vector machine (SVM). The intelligent chatter detection has accuracy above 96 % for the identification of cutting state after being trained by experimental data. The results show that it is possible to monitor chatter behavior in milling process by using motor current signal.

This dissertation outlines research on studying the effects of machining parameters such that cutting speed, feed rate, axial depth of cut, radial depth of cut and helix angle on system dynamic stability and the surface quality of high-speed milling. With the use of structural tool modal parameters, the material cutting force coefficients and the axial depth of cut, the system can avoid the chatter phenomenon of the tool at high cutting speeds. The surface roughness finish in the milling process is determined by the machining parameters and tool structure dynamics. To perform high-speed milling, the chance of tool vibration (chatter phenomenon) which affects the cutting tool, must be minimized or eliminated. In this research, the linear and nonlinear mathematical force models including the effect of the helix angle are presented for an end-milling process. The linear force model includes cutting-edge coefficients. The cutting force coefficients are determined for an end-milling process using two methods, the average force method and the optimization technique method. The second method is developed to identify the cutting force coefficients in the milling process by forming the objective functions using the optimization technique to minimize the error between the experimental and the analytical forces. Moreover, this method produced a good force model that approximates the experimental force results, which compared with the average force method. The stability lobe diagrams are created using the analytical method to determine whether the cut is stable or unstable. In addition, simulations are performed to predict stability of the milling process. By comparing simulated and experimental results, the dynamics and stability of the milling operation can be easily identified before performing any cutting operation. The slot milling experiments show that while the system in the chatter region close to the stability limits and the axial depth of cut increased, the system changes from stable chatter to chaotic chatter. Furthermore, the nature of bifurcation in milling is investigated by performing experiments and simulations. The linear and nonlinear mathematical force models are used for simulating end-milling process. Simulated bifurcation diagrams are generated using both models and compared to experimental results. In addition, the effect of the feed rate on the location of the bifurcation point (start and end of bifurcation) is studied. By comparing simulated and experimental results, the simulation using a nonlinear force model is found more accurate in predicting the dynamics and stability of the milling operation. The applications of Taguchi and response surface methodologies (RSM) are used to minimize the surface roughness in the end milling process. Taguchi’s method for optimum selection of the milling process parameters is applied based on the signal to noise ratio and ANOVA analysis of the surface finish. A second-order model contains quadratic terms that have been created between the cutting parameters and surface roughness using response surface methodology (RSM). Surface roughness of the machined surfaces are measured and used to identify the optimum levels of the milling parameters. Based on Taguchi, ANOVA, and RSM analyses, the end milling process can be optimized to improve surface finish quality and machining productivity.

Airborne Weather Radar (WXR) is an important system for civil aircraft to deal with hazardous weather conditions. Numbers of potential hazardous factors exist in task process. In this paper, a model-based method is proposed to analyze the accident occurrence mechanism and system safety while considering the interaction between social-technical elements. The hybrid model consists of five parts, including social-organizational risk model, human error model, failure model of system, maintenance error model, and failure model of WXR task process. Moreover, different modeling technologies are adopted to construct these models. Then, the hybrid model is utilized to identify potential hazardous factors and achieve safety analysis for the social-technical system in both qualitative and quantitative way. Additionally, potential hazardous causes are sorted according to influence degrees. The analysis results indicate that the method can achieve safety analysis for the task process, and provide technical support for the safety design of airborne WXR system.

Ultrasonic assisted electrochemical discharge milling of complex glass microstructure with high-quality

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

Evaluation of Chip-tool Interface Temperature: Effect of Tool Coating and Cutting Parameters during Turning Hardened AISI 4340 Steel

Machining is a commonly preferred production process. Machining with high pressure coolant, cryogenic coolant, or atomised coolant spray are the latest techniques investigated by the researchers to improve the productivity. Researchers have performed the work on various aspects of machining using high pressure coolant. Researchers have investigated effect of coolant related parameters, machining parameters, tool related parameters on surface finish, dimensional accuracy, tool wear, tool life, forces, chip morphology, tool and work piece temperature, and wear mechanism during machining of different materials. Researchers have performed the research with regard to coolant type, coolant pressure, flow rate, coolant concentration, and coolant jet direction and come up with interesting and beneficial findings. This paper presents a comprehensive literature review on high pressure coolant strategy in machining of materials with respect to various aspects. The paper presents important findings in this area and pinpoints the issues which should be investigated in future.

Computer simulations of animal groups are usually performed via individual‐based modelling, where simulated animals are designed on the level of individuals. With this approach, developers are able to capture behavioural nuances of real animals. However, modelling each individual as its own entity has the downside of having a high computational cost, meaning that individual‐based models are usually not suitable for real‐time simulations of very large groups. A common alternative approach is flow‐based modelling, where the dynamics of animal congregations are designed on the group level. This enables researchers to create real‐time simulations of massive phenomena at the cost of biological authenticity. A novel approach called hybrid modelling tries to mix the best of both worlds—precision of individual‐based models and speed of flow‐based ones. An unknown surrounding hybrid model is the question of their biological authenticity and relevance. In this study, we develop a hybrid model for the simulation of herds of grazing sheep. Through Bayesian data analysis, we show that such an approach can encompass several aspects of real‐world sheep behaviour. Our hybrid model is also extremely efficient, capable of simulating herds of more than 1,000 individuals in real time without resorting to graphics processing unit execution.

In this paper, the authors propose the generic concept of machining instability based on the analysis of all kinds of machining instable behaviours and their features. The investigation covers all aspects of the machining process, including the machine tool structural response, cutting process variables, tooling geometry and workpiece material property in a full dynamic scenario. The paper presents a novel approach for coping with the sophisticated machining instability and enabling better understanding of its affect on the surface generation through a combination of the numerical method with the characteristic equations and using block diagrams/functions to represent implicit equations and non-linear factors. It therefore avoids the lengthy algebraic manipulations in deriving the outcome and the solution scheme is thus simple, robust and intuitive. Several machining case studies and their simulation results demonstrate the proposed approach is feasible for shop floor CNC machining optimisation in particular. The results also indicate the proposed approach is useful to monitor the machining instability and surface topography and to be potentially applied in adaptive control of the instability in real time.