Milling tool wear state classification and prediction using machine learning, homogeneous and heterogeneous ensemble learning approach

Improving performance with hybrid feature selection and ensemble machine learning techniques for code smell detection

Tool condition monitoring (TCM) during the manufacturing process is of great significance for ensuring product quality and plays an important role in intelligent manufacturing. Current TCM systems deployed in the local device or cloud computing environment unable meet the requirements of low response latency and high accuracy at the same time. The emerging fog computing provides new solutions for the above problem. This paper presents a tool wear monitoring and prediction (TWMP) system based on deep learning models and fog computing. In order to improve monitoring and prediction accuracy, we propose a multiscale convolutional long short-term memory model (MCLSTM) to complete the tool wear monitoring task and a bi-directional LSTM model (BiLSTM) to complete the tool wear prediction task. To reduce the response latency of the TWMP system, we deploy the MCLSTM model and the BiLSTM model in a fog computing architecture. The fog computing architecture consists of an edge computing layer, a fog computing layer, and a cloud computing layer. The edge computing layer undertakes real-time signal collection task. The fog computing layer undertakes real-time tool wear monitoring task. The cloud computing layer with powerful computing resources undertakes intensive computing and latency-insensitive tasks such as data storage, tool wear prediction, and model training. A twist drill wear monitoring and prediction experiment is conducted to test the performance of the proposed system in terms of accuracy, response time, and network bandwidth consumption.

Tool wear identification and prediction method based on stack sparse self-coding network

Suitability of CAE neural networks and FEM for prediction of wear on die radii in hot forging

Sentiment classification involves determining the sentiment expressed in text, such as positive, negative, or neutral, but social media data presents challenges due to its high dimensionality, noise, and unstructured nature. This study proposes a novel sentiment classification approach by combining hybrid feature selection methods with deep learning techniques. Social media platforms generate vast amounts of data daily, which is often noisy, redundant, and irrelevant for sentiment analysis. Hybrid feature selection techniques, which integrate filter and wrapper-based methods, assist in reducing the feature space while retaining the most informative features. By applying deep learning models, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, classification performance can be substantially enhanced. The proposed framework uses hybrid feature selection to eliminate noisy and irrelevant features, thereby improving the model's generalization capabilities. Experimental results reveal that the combination of hybrid feature selection and deep learning techniques not only boosts sentiment classification accuracy but also decreases computational overhead. This study highlights the effectiveness of merging traditional feature selection methods with modern deep learning models to better address the complexities of social media datasets and deliver more precise sentiment analysis. The results achieved by proposed model is 98.50% on social media dataset which is higher than conventional approaches.

Long-time discontinuous contact is easy to cause tool wear during milling. To decrease the impact of severe wear on workpiece quality and processing efficiency, cutting tools should be replaced timely. Therefore, tool wear prediction is an important aspect in improving process efficiency, ensuring machining precision and realizing intelligent manufacturing. To boost the precision of online prediction of tool wear, this paper suggests a novel approach to monitor tool wear by optimizing backpropagation (BP) neural network via firefly algorithm (FA). Specifically, the progressive semi-soft threshold function is applied to the process of cutting force signal noise reduction, which reduces redundant signals and noise interference in the signal. Time-domain analysis, frequency-domain analysis, and wavelet packet decomposition are utilized, cutting force features are extracted, and Pearson correlation coefficient is used to sift out signal features that are highly connected to tool wear. The FA is used to improve the BP neural network's weights and thresholds. Through learning nonlinear mapping, relationship between tool flank wear and signal features is realized. A prediction model of tool wear of FA-BP is constructed. Milling experiments validate the prediction model in the milling process. The experimental outcomes confirm the precision and reliability of the method. In comparison to BP neural network, genetic algorithm optimized BP neural network and particle swarm optimization algorithm optimized BP neural network prediction method, it has a greater prediction accuracy and a stronger training impact, and has superior performance. The research results can give a theoretical foundation and technological assistance for predicting tool wear, which is crucial for improving workpiece quality, processing efficiency, and promoting intelligent development.

Nickel-based superalloy Inconel 718 micro-parts are widely used in aerospace, electronics and other fields. But Inconel 718 is notoriously a kind of difficult-to-cut material due to its high strength and hard particles. Laser-assisted micro-milling (LAMM) is one of the effective methods for machining Inconel 718 micro-parts. However, micro-milling tools with small diameters are easy to wear under high-temperature conditions in LAMM. Tool wear directly affects the machining dimensional accuracy. To guide timely tool change to ensure the machining accuracy of the parts, the high-precision prediction of micro-milling tool wear during LAMM is urgent to study. Firstly, the preheating temperature field of the Inconel 718 workpiece heated by laser is numerically calculated. Then, based on DEFORM-3D software, a LAMM Inconel 718 process simulation model is achieved. The cutting time and predicted tool wear depth are calculated to obtain the relative diameter reduction rate of the tool. Finally, the LAMM Inconel 718 experiments are carried out. The measured value of the diameter reduction rate of the micro-milling tool is compared with the predicted value. The maximum relative error is 14.85%, and the average relative error is 10.91%, which verifies the validity of the built simulation model.

To accurately predict the amount of tool wear in the machining process, a monitoring model of tool wear based on multi-sensor information feature fusion is proposed. First, by collecting the cutting force, vibration, and acoustic emission signals of the tool during the whole life cycle, the multi-domain characteristics of the signal are extracted; then, kernel principal component analysis is used to reduce the dimensionality of the extracted data, and the principal components whose cumulative contribution ratio exceeds 85% are obtained. The redundant features with little correlation with tool wear were removed from the feature vectors to generate the fusion features. Finally, the fusion features are input into the least squares support vector machine model optimized by particle swarm algorithm for regression prediction of tool wear. The non-linear mapping relationship between the physical signal and the tool wear is discovered, which effectively realizes the prediction of the tool wear. Compared with the existing tool wear prediction methods, the method proposed has higher prediction accuracy.

Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics

Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality is directly affected by the processing technology and tool life; burrs, tearing, delamination and other defects will reduce the service life of GFRP structural parts. Through drilling damage and tool wear experiments of GFRP, the thrust force, vibration amplitude, the number of processed holes, feed rate and cutting speed were found to be the main factors in drilling damage and tool wear. Using those main factors as the input layer, a tool wear and delamination factors prediction model was established based on an improved circle chaotic mapping (CCM) Grey Wolf algorithm for a back propagation (BP) neural network. Compared with the original BP neural network, the maximum prediction error of the improved BP neural network model was reduced by 71.2% and the root mean square (RMS) prediction error was reduced by 63.82%. The maximum prediction error of the delamination factor at the entrance was less than 3%, and the maximum prediction error of the delamination factor at the exit was less than 1%. The prediction results showed that the BP neural network model optimized by an improved circle chaotic mapping Grey Wolf algorithm can better predict the GFRP drilling quality and tool wear, and had higher accuracy, optimization efficiency and better robustness than the ordinary BP neural network.

The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results.

Previously cubic boron nitride (CBN) and ceramic inserts were used for the dry turning of heat-treated D3 tool steel with cutting speed beyond 200 m/min, suffers from severe tool wear and generation of high temperature at the interface. To overcome these shortcomings, a hybrid ceramic insert that is, Al2O3 + Ti (C,N) with coated (TiN) is used in dry turning of D3 tool steel (heat-treated). Use of the hybrid insert with a lower feed rate (0.04 mm/rev) and a minimum depth of cut (0.4 mm) improves the surface quality of the turning process by significantly reducing the tool wear and cutting forces requirement in a cutting speed range of 165– 175 m/min. Even though ceramic tool inserts have high wear resistance at elevated temperatures but it has limited impact strength. So, ceramics is only recommended for shocks and vibration-free high-speed cutting operations of hardened steel under a dry environment. The novelty of this article is the development of a wear map for the analysis and prediction of tool wear. The Scanning electron microscope studies of wear region elucidate that low tool wear that the predominant modes of wear for low tool wear cutting conditions are delamination and abrasion. Besides for higher tool wear the prevailing wear mode is a combination of abrasion as well as adhesion wear. It is perceived that our proposed methodology outperforms the state-of-the-art methods.

Tool wear monitoring and prediction are of great importance for machining precision and surface integrity. In order to ensure good quality of the machining components, tool wear should be monitored and predicted in time. However, the traditional methods greatly depend on feature selection and extraction, and accuracy and generalization are limited. In this paper, a novel model based on deep learning was proposed to monitor and predict tap tool wear. Firstly, 1D CNN was designed to extract features from the vibration data, and then the residual block with the dilated convolution was specially developed to accept the features from 1D CNN. After that, the fully connected neural networks were designed to predicate tool wear. Moreover, to verify the superiority and generalization of the proposed method, the taping experiment from a real engine cylinder head production line was conducted. During the tapping process, the vibration signal and tool wear were collected. And then the training experiment was conducted with the collected data. The prediction results with the proposed model were compared with those of the state-of-the-art algorithms. The compared results showed that the proposed model was more robust and accurate for tool wear prediction.

Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images.

A novel tool wear predictive model was developed based on the current signals in this study. The system adapts to different part geometry with accurate prediction of the tool wear during the operation. The current sensor was utilized presenting a practical and better choice for tool wear monitoring which is inexpensive and no need to be attached to the working table or spindle. To avoid interruptions during the machining process, the tool wear was only measured at the end of the operation. The Long Short-Term Memory model was used to develop the tool wear prediction system. The tool wear prediction results indicate 23.92% and 36.41% average error for all the testing samples after 1/3 of the operations for profiling and straight turning, respectively. When the tool wear prediction was carried out after 2/3 of the operations, excellent results are observed with 6.15% error for profiling and 9.44% error for straight turning. The prediction results at the end of the operation shows 0.18% and 0.68% error for profiling and straight turning. The performance of the model using the current sensor shows that the model can predict the tool wear with less than 10% error after 2/3 of the turning operation without interfering with the turning process.