Multilayer Perceptron Model Research Articles

Use of Machine Learning Techniques to Predict the In-situ Concrete Compressive Strength using Non-Destructive Testing

Recently, machine learning-based prediction models have gained considerable popularity in predicting the compressive strength of concrete using results from Non-Destructive Testing (NDT) methods. This research considers NDT results from Rebound Hammer (RH) and Ultrasonic Pulse Velocity (UPV) tests for concrete samples with different concrete grades. 270 test samples were considered for the analysis using different machine learning techniques such as Multi-Layer Perceptron (MLP), Long Short-Term Memory network (LSTM), and Convolutional Neural Network (CNN). Concrete compressive strength results are obtained from Destructive Test (DT) results of the same for the purpose of labelling. Each model's performance is evaluated through the computation of the Coefficient of Determination (R²), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). The findings of this study underscore the potential of machine learning techniques in predicting concrete compressive strength based on Non-Destructive Test (NDT) results. The analysis findings suggested that employing UPV and RH data in conjunction with a trained MLP model to be an effective approach for accurately estimating the compressive strength of concrete specimens. The outcomes of the research serve as a valuable reference for researchers and industry practitioners alike when assessing in-situ concrete compressive strength through Non-Destructive Testing (NDT) methods.

Integrated machine learning models for predictive analysis of thermal and electrical power generation of a photo-thermal system at Catania, Italy

The Photovoltaic-Thermal (PV/T) system is designed for producing both electrical and thermal energy. Its efficiency is improved when temperature-sensitive PV cells are cooled using nanofluid coolants. However, the current models for predicting PV/T system performance with nanofluid cooling are limited. In order to fill this gap, this study aims to develop machine learning models to predict the electrical and thermal efficiencies of a water-based PV/T system. Three types of machine learning algorithms from the supervised learning method were selected in this study because of their ability to handle complex data and provide accurate predictions: the Multi-layer Perceptron (MLP), the Gradient Boosting Regressor (GBR) and the Light Gradient-Boosting Machine (LightGBM). Initially, the models are trained and validated using data consisting of 15,540 samples from a water-based PV/T system. On the basis of the results obtained, the MLP algorithm proved to be the best for the prediction of electrical energy, with an R2 value = 0.9906, against 0.9709 and 0.99 respectively for the other GBR and LGB algorithms, while the LGB algorithm proved effective for the prediction of thermal energy, with an R2 value = 0.983, against 0.983, 0.97 and 0.988 respectively for the GBR and LGB algorithms. Secondly, the best MLP and LGB models previously identified for the water-based PV/T system were adapted to predict, this time, the energy performance of an Ag/water nanofluid-based PV/T system. The results obtained show that these models are highly effective in predicting the electrical and thermal performance of the Ag/water nanofluid PV/T system, even in the absence of real data, making them very useful for real applications.

Modelling the present and future scenario of urban green space vulnerability using PSR based AHP and MLP models in a Metropolitan city Kolkata Municipal Corporation

ABSTRACT Urban green spaces, despite their recognized importance for sustainable urban development, face significant challenges to their resilience and viability due to rapid urbanization and various associated pressures. The scientific problem addressed in this paper is the vulnerability of urban green spaces to various stressors, including proximity to settlement, land use and land cover changes, and proximity to roads, among others. Despite their importance, there is a lack of comprehensive assessments and frameworks to systematically evaluate and address this vulnerability. Therefore, this study aims to fill this gap by employing a multi-criteria decision-making framework, namely the Analytic Hierarchy Process (AHP), and the Pressure-State-Response (PSR) framework. By analyzing 10 indicators related to urban green space vulnerability through a pairwise comparison matrix, the study identifies critical factors influencing vulnerability and categorizes urban green spaces into different susceptibility levels. Additionally, the research extends its analysis to future scenario development for the years 2030 and 2040, using a Multi-Layer Perceptron (MLP) based Markov Chain model, incorporating various parameters such as distances from built-up areas, roads, rails, streams, slopes, and elevation. Through these methodologies, the study aims to provide valuable insights to urban planners, policymakers, and environmental practitioners, enabling them to make informed decisions to enhance the resilience and sustainability of urban green spaces and urban areas as a whole.

Urban air quality index forecasting using multivariate convolutional neural network based customized stacked long short-term memory model

In the context of increasing urban pollution and its adverse health effects, this study focuses on enhancing the air quality prediction model by forecasting future concentrations of various Air Quality Index (AQI) levels to support the development of green smart cities. The primary objective of this study is to develop a robust multivariate time series forecasting model using a combination of Convolutional Neural Networks (CNN) and Customized Stacked-based Long Short-Term Memory (CSLSTM) networks, namely C2SLSTM. The proposed methodology involved preprocessing AQI and meteorological data, scaling the features, and employing a hybrid CNN-LSTM architecture to capture spatial and temporal dependencies in the data. Dynamic AQI pattern changes are computed using online learning. The proposed model was trained on a big dataset containing AQIs such as particulate matter (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2), along with meteorological factors like weather count, temperature, wind, humidity, and pressure. Experimental results demonstrate that the model achieved a mean absolute error (MAE) of 0.25, mean square error (MSE) of 0.083, root mean square error (RMSE) of 0.289 and a R2-Score of 0.84 on the test set. Also, compared to Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Stacked Long Short-Term Memory (SLSTM), and Multilayer Perceptron (MLP) models, the C2SLSTM model showcases a performance enhancement of 35 %, 50 %, 30 %, and 32 %. Notably, the CNN component effectively extracted spatial features, while the LSTM layers captured temporal patterns, leading to precise predictions. These results indicate that the proposed approach effectively captures temporal dependencies and improves forecasting performance, with R2-Score values tending towards 1 for 12-hour prediction intervals. This research demonstrates the potential of advanced SLSTM architectures in accurately predicting AQI metrics, which can aid in better environmental monitoring and management. Also, the spatiotemporal correlation analysis is done through the designed ir metric.

Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.

Predicting bathymetry using multisource differential marine geodetic data with multilayer perceptron neural network

ABSTRACT We propose a method for enhancing the accuracy of bathymetry models based on a multilayer perceptron (MLP) neural network that integrates the differences in multisource marine geodetic data (MMGD) (longitude, latitude, reference bathymetry, slope, the meridional and prime components of vertical deflection, gravity anomaly, vertical gravity gradient, and mean dynamic topography). First, we use the MMGD differences between the shipborne sounding control points within 8′ × 8′ grid points and shipborne sounding control points as input data, as well as the differences between the topo_24.1 model and the measured bathymetric values at the control points as output data to train the MLP model. Second, we feed the input data from the central point of a 1′ × 1′ grid into the MLP model to obtain predictions, and then use the topo_24.1 model to recover the predicted bathymetry at the prediction point. We focus on the Caribbean Sea, and construct a Caribbean Bathymetric Chart of the Oceans (CBCO1) model using MLP neural network. The reliability of MMGD, a CBCO2 model using MMGD, and the reliability and effectiveness of the overall method are demonstrated through comparisons with the CBCO2, GEBCO_2022, topo_24.1, DTU18 models at the checkpoints.

Machine-learning-based ensemble regression for vehicle-to-vehicle distance estimation using a toe-in style stereo camera

Adjusting the following distance from the front vehicle in highway traffic is important to reduce the risk of collision. Distance estimation is an important research area for advanced driver assistance systems. Therefore, this paper presents a methodology that combines the strengths of several machine learning algorithms using joint decision mechanisms and searches for optimal results for vehicle-to-vehicle distance estimation. The hyperparameter optimization of machine learning models is performed by an iterative algorithm that compares combinations of hyperparameter values. In addition, machine learning algorithms are combined and tested with ensemble learning methods to improve the results obtained. According to the experiments, the ensemble voting regression created by combining extreme gradient boosting, categorical boosting and two multi-layer perceptron models achieves the best result with a mean absolute percentage error value of 1.5444. Considering the comparisons made with other methods, the accuracy of the results obtained with the proposed method is quite high.Code is at: https://github.com/ozgurduran/V2V-VR.

Eye-Net: A Low-Complexity Distributed Denial of Service Attack-Detection System Based on Multilayer Perceptron

Distributed Denial of Service (DDoS) attacks disrupt service availability, leading to significant financial setbacks for individuals and businesses. This paper introduces Eye-Net, a deep learning-based system optimized for DDoS attack detection that combines feature selection, balancing methods, Multilayer Perceptron (MLP), and quantization-aware training (QAT) techniques. An Analysis of Variance (ANOVA) algorithm is initially applied to the dataset to identify the most distinctive features. Subsequently, the Synthetic Minority Oversampling Technique (SMOTE) balances the dataset by augmenting samples for under-represented classes. Two distinct MLP models are developed: one for the binary classification of flow packets as regular or DDoS traffic and another for identifying six specific DDoS attack types. We store MLP model weights at 8-bit precision by incorporating the quantization-aware training technique. This adjustment slashes memory use by a factor of four and reduces computational cost similarly, making Eye-Net suitable for Internet of Things (IoT) devices. Both models are rigorously trained and assessed using the CICDDoS2019 dataset. Test results reveal that Eye-Net excels, surpassing contemporary DDoS detection techniques in accuracy, recall, precision, and F1 Score. The multiclass model achieves an impressive accuracy of 96.47% with an error rate of 8.78%, while the binary model showcases an outstanding 99.99% accuracy, maintaining a negligible error rate of 0.02%.

AsymUNet: An Efficient Multi-Layer Perceptron Model Based on Asymmetric U-Net for Medical Image Noise Removal

With the continuous advancement of deep learning technology, U-Net–based algorithms for image denoising play a crucial role in medical image processing. However, most U-Net-based medical image denoising algorithms typically have large parameter sizes, which poses significant limitations in practical applications where computational resources are limited or large-scale patient data processing are required. In this paper, we propose a medical image denoising algorithm called AsymUNet, developed using an asymmetric U-Net framework and a spatially rearranged multilayer perceptron (MLP). AsymUNet utilizes an asymmetric U-Net to reduce the computational burden, while a multiscale feature fusion module enhances the feature interaction between the encoder and decoder. To better preserve the image details, spatially rearranged MLP blocks serve as the core building blocks of AsymUNet. These blocks effectively extract both the local and global features of the image, reducing the model’s reliance on prior knowledge of the image and further accelerating the training and inference processes. Experimental results demonstrate that AsymUNet achieves superior performance metrics and visual results compared with other state-of-the-art methods.

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

Deep Learning Models for PV Power Forecasting: Review

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning models for PV power forecasting, concentrating on comparisons of the features, advantages, and limitations of different model architectures. First, we analyze the commonly used datasets for PV power forecasting. Additionally, we provide an overview of mainstream deep learning model architectures, including multilayer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN), and graph neural networks (GNN), and explain their fundamental principles and technical features. Moreover, we systematically organize the research progress of deep learning models based on different architectures for PV power forecasting. This study indicates that different deep learning model architectures have their own advantages in PV power forecasting. MLP models have strong nonlinear fitting capabilities, RNN models can capture long-term dependencies, CNN models can automatically extract local features, and GNN models have unique advantages for modeling spatiotemporal characteristics. This manuscript provides a comprehensive research survey for PV power forecasting using deep learning models, helping researchers and practitioners to gain a deeper understanding of the current applications, challenges, and opportunities of deep learning technology in this area.

Research on standardization of power transformer monitoring and early warning based on multi-source data

To meet the growing demand for integrated monitoring of complex power grid equipment, it is necessary to improve the situational awareness model of power transformers. The model is expected to assist monitoring personnel in timely identifying transformers with deteriorating trends among massive and discrete monitoring information, and to make responses in advance. However, the current transformer state awareness technology generally has the problem of single data source and poor timeliness, and still requires monitoring personnel to make artificial analysis and prediction in combination with telemetry information, which cannot fully meet the requirements of power grid equipment monitoring. This paper is based on multi-source data fusion technology, through associating and mining transformer alarm information, equipment maintenance records and power transmission and transformation online monitoring data, to extract the dimension features of transformer operation situation assessment. By constructing a multi-layer perceptron model, a transformer state transition model based on the principle of Markov chain is established, which can predict possible defects 2 h in advance and achieve good results, and determine the transformer state early warning index, providing sufficient time for monitoring personnel to deploy transformer operation and maintenance work in advance. Finally, the effectiveness of the method proposed in this paper is proved by the case of transformer crisis state in a city substation, and the method proposed in this paper has important significance for transformer state early warning.

Evaluation of aircraft engine performance during takeoff phase with machine learning methods

During the takeoff phase, aircraft engines reach maximum speed and temperature to achieve the required thrust. Due to these harsh operating conditions, the performance of aircraft engines may decrease. This decrease in performance increases both fuel consumption and environmental damage. Reducing or eliminating the damages caused by aircraft is among the objectives of ICAO. In order to achieve this goal, aircraft engines are compulsorily tested, evaluated by experts and certified. The data obtained during the test process is recorded and stored in the engine emission databank (EEDB). During the takeoff phase, there is no system that can evaluate aircraft engines without dismantling and without expert knowledge. In this study, EEDB 2019 and 2021 takeoff phase data sets were used. Fuel flow T/O parameter is an important parameter used both in the calculation of aircraft emissions and in the evaluation of engine performance. Gaussian process regression (GPR), support vector machine (SVM) and multilayer perceptron (MLP) models were used to estimate the fuel flow T/O parameter. The results obtained were compared according to error performance criteria and the best model was selected. In MATLAB® environment, confidence intervals were plotted with the estimated fuel flow T/O value at 99% confidence level. This study demonstrates that the performance evaluation of aircraft engines during the takeoff phase can be performed without the need for expert knowledge.

The technological assessment of green buildings using artificial neural networks

This study aims to construct a comprehensive evaluation model for efficiently assessing appropriate technologies within green buildings. Initially, an Internet of Things (IoT)-based environmental monitoring system is devised and implemented to collect real-time environmental parameters both inside and outside the building. To evaluate the technical suitability of green buildings, this study employs a multifaceted approach encompassing various criteria, including energy efficiency, environmental impact, economic benefits, user comfort, and sustainability. Specifically, it involves real-time monitoring of environmental parameters, analysis of energy consumption data, and indoor environmental quality indicators derived from user satisfaction surveys. Subsequently, a Multi-Layer Perceptron (MLP) is selected as a conventional artificial neural network (ANN) model, while a Long Short-Term Memory (LSTM) model is chosen as an advanced recurrent neural network model in the realm of deep learning. These models are utilized to process and explore the collected data and assess the technical suitability of green buildings. The dataset comprises physical quantities such as temperature, humidity, and light intensity, as well as economic indicators including energy efficiency and building operating costs. Furthermore, the assessment process considers the building's life cycle assessment and indoor environmental quality factors such as health, comfort, and safety. By incorporating these comprehensive criteria, a holistic evaluation of green building technologies is achieved, ensuring the selected technologies' suitability and effectiveness. The model prediction results demonstrate that the proposed hybrid evaluation model exhibits high accuracy and robust stability in predicting building environmental parameters. For instance, the Root Mean Square Error (RMSE) for temperature prediction is 1.2 °C, the Mean Absolute Error (MAE) is 0.9 °C, and the determination coefficient (R2) reaches 0.95. Similarly, for humidity prediction, the RMSE, MAE, and R2 are 3.5 %, 2.8 %, and 0.88. Compared to the traditional MLP and LSTM models alone, the proposed hybrid model shows significant improvements in predicting building energy consumption, with approximately 15 % and 12 % reductions in RMSE and MAE, respectively, and an increase in R2 values of approximately 7 percentage points. These findings indicate that by amalgamation of the IoT and ANNs, this study successfully establishes a comprehensive model for accurately assessing technologies suitable for green buildings. This approach offers a novel perspective and methodology for the design and evaluation of green buildings.

Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences

The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies but ignores bias or only can learn bias but cannot mine data dependencies. To solve these problems, in this article, we propose a neural collaborative sequential learning mechanism, which learns sequential information from user behavior sequences that contain bias. We propose a neural collaborative filtering (NCF) model that fully takes advantage of all data dependencies among users, items, and biased sequential behaviors. Our sequential learning mechanism employs a self-attention mechanism to learn sequential features into an embedding space and inputs this sequential embedding into the generalized matrix factorization (GMF) model and the multilayer perceptron (MLP) model. We performed experiments on two real-world datasets and compared our model with many well-known baselines. The experimental results demonstrate that our model achieves superior performance. We also give a thorough analysis through ablation experiments and sensitivity experiments.

Detection of DDoS Attack Based on Deep Neural Network with various Number of Features

Distributed Denial of Service (DDoS) attacks have become an effective threat to the reliability and availability of the services of the internet in last decades. The effectiveness of utilizing Deep Neural Networks (DNNs) for DDoS attack detection is investigated in this paper. We implemented a reliable detection system that analyzes network traffic data to spot any DDoS activities. A multi-layer perceptron model trained on a dataset containing five different forms of DDoS attacks and normal traffic is used in this method. Also, three cases of varous number of features was investigated to extract the optimal number of features that can be used for detection of DDoS attacks. To improve accuracy, a great deal of testing was done on the model's architecture using various hyperparameters and training procedures. With a 96.5% detection rate, the DNN results showed a high degree of accuracy. This demonstration highlights the ability of deep neural networks to identify DDoS attacks in the midst of regular traffic. The six-category classification enhances detection granularity and facilitates the application of more specialized and successful mitigation techniques. Given the great precision attained, DNNs have the potential to be an essential part of real-time detection systems, providing a major advancement over conventional techniques.

Prediction of free chloride concentration in fly ash concrete by machine learning methods

Free chloride concentration distribution is important for assessing the corrosion risk of steel bars in reinforced concrete structures under chloride environment. In this study, a group of 3150 free chloride concentration data sets were obtained. Afterwards, three machine learning methods, namely support vector regression (SVR), multilayer perceptron (MLP) and one-dimensional convolutional neural network (1D-CNN) were adopted to construct models to predict chloride concentration distribution. The results show that 1D-CNN and MLP models are better at predicting the chloride concentration in fly ash concrete, whereas the prediction capability of SVR is relatively poor. Moreover, free chloride concentration prediction based on unmeasured parameters was conducted. The results show that the 1D-CNN and MLP models both have high prediction abilities – that is, predicted results are consistent with experimental measurements, performing generally better than the time-varying model constructed based on Fick's second law. When the free chloride concentrations were higher than 0.1%, the SVR model had a better prediction effect, but had unsatisfactory results and differed significantly from the actual chloride concentration when at a lower concentration. Overall, the 1D-CNN model performs the best in predicting free chloride concentration of concrete at different penetration depths, exposure time and with different fly ash content.

XGBoost algorithm assisted multi-component quantitative analysis with Raman spectroscopy

To improve prediction performance and reduce artifacts in Raman spectra, we developed an eXtreme Gradient Boosting (XGBoost) preprocessing method to preprocess the Raman spectra of glucose, glycerol and ethanol mixtures. To ensure the robustness and reliability of the XGBoost preprocessing method, an X-LR model (which combined XGBoost preprocessing and a linear regression (LR) model) and a X-MLP model (which combined XGBoost preprocessing and a multilayer perceptron (MLP) model) were developed. These two models were used to quantitatively analyze the concentrations of glucose, glycerol and ethanol in the Raman spectra of mixed solutions. The proportion map of hyperparameters was firstly used to narrow down the search range of hyperparameters in the X-LR and the X-MLP models. Then the correlation coefficients (R2), root mean square of calibration (RMSEC), and root mean square error of prediction (RMSEP) were used to evaluate the models’ performance. Experimental results indicated that the XGBoost preprocessing method achieved higher accuracy and generalization capability, with better performance than those of other preprocessing methods for both LR and MLP models.

Unveiling anomalies: harnessing machine learning for detection and insights

The rise of Internet of Things (IoT) devices has brought about an increase in security risks, emphasizing the need for effective anomaly detection systems. Previous research introduced a dynamic voting classifier to overcome overfitting or inaccurate accuracies caused by dataset imbalance. This article introduces a new method for IoT anomaly detection that employs a hybrid voting classifier, which combines several machine learning models. To solve the overfitting and class weight issues, an adaptive voting classifier is used that adjusts weights according to the highest preference for accuracy. The developing voting system increases the effectiveness of more accurate classifiers, enhancing the group’s overall capability. A proposed combined classifier combines Logistic Regression, AdaBoost, Gradient Boosting, and Multi-Layer Perceptron models using a soft voting method. To develop and assess this method, the CIC-IoT-2023 dataset is utilized, which contains 33 types of IoT attacks across 7 categories. This process includes thorough data preprocessing and feature selection from a pool of 42 available attributes. The performance of this approach is measured against individual classifiers across binary, 8-class, and 34-class classification tasks. The results highlight the effectiveness of the hybrid model. It achieves 98.95% accuracy, 76.72% recall, and 72.01% F1-score in the 34-class problem, surpassing the performance of all individual models. For the 8-class task, the hybrid classifier attains 99.39% accuracy, 90.89% recall, and an 83.01% F1-score. This demonstrates the high potential of the hybrid approach for IoT anomaly detection.

Mechanical Quantities Prediction of Metal Cutting by Machine Learning and Simulation Data

Metal cutting is an important process in industrial manufacturing. Using the mechanical quantities of metal cutting to optimize process design is helpful to improve productivity. However, it is expensive to obtain these quantities due to the complexity of the cutting process, including material nonlinearity, geometric nonlinearity, state nonlinearity and their interactions. In this paper, a prediction model is constructed by combining machine learning (ML) and simulation data to quickly acquire multi-difficult-to-obtain metal cutting mechanical quantities to solve this problem. First, Adaptive Smoothed Particle Hydrodynamics (ASPH) is used to generate a simulation dataset of 2000 metal cutting cases. Based on the simulation data, six machine learning (ML) methods are employed to establish two prediction models, single-task learning and multi-task learning, to predict the mechanical quantities of metal cutting. The experimental results demonstrate that the ML method can predict abundant reference data efficiently after understanding the relationship between simulation parameters and mechanical quantities from simulation data, which is expected to replace some similar and repetitive simulation work. The Multilayer Perceptron (MLP) model under the multi-task setting provides the best prediction performance, fastest prediction time efficiency, and stable model behavior. Additionally, input erasure experiments reveal that the prediction of maximum equivalent plastic strain is significantly affected by particle spacing, and cutting speed plays a vital role in predicting maximum velocity. This work highlights the promotion of the data-driven ML method in quickly obtaining abundant reference data for the metal cutting process, and provides an auxiliary means for process optimization.