Processing Model Research Articles

A Natural Language Processing Model for Automated Organization and Analysis of Intangible Cultural Heritage

This paper investigates text similarity methods in the field of NLP, improves upon the WMD, and develops the SWC-WMD distance, forming the basis for a clustering method for long ICH texts. Clustering experiments on the constructed ICH long text dataset using WMD, SWC-WMD, and TF-IDF-WMD distances were conducted. The impact of the number of feature words on clustering results and the effect of different distances on clustering outcomes were assessed based on accuracy and F1 values from the evaluation criteria. The final results show that the SWC-WMD distance improves the accuracy and F1 values of the ICH long text clustering results compared to the other two distances, thereby proving the effectiveness of the methods proposed in this paper.

The artistic image processing for visual healing in smart city

This study investigates the processing methods of artistic images within the context of Smart city (SC) initiatives, focusing on the visual healing effects of artistic image processing to enhance urban residents’ mental health and quality of life. Firstly, it examines the role of artistic image processing techniques in visual healing. Secondly, deep learning technology is introduced and improved, proposing the overlapping segmentation vision transformer (OSViT) for image blocks, and further integrating the bidirectional long short-term memory (BiLSTM) algorithm. An innovative artistic image processing and classification recognition model based on OSViT-BiLSTM is then constructed. Finally, the visual healing effect of the processed art images in different scenes is analyzed. The results demonstrate that the proposed model achieves a classification recognition accuracy of 92.9% for art images, which is at least 6.9% higher than that of other existing model algorithms. Additionally, over 90% of users report satisfaction with the visual healing effects of the artistic images. Therefore, it is found that the proposed model can accurately identify artistic images, enhance their beauty and artistry, and improve the visual healing effect. This study provides an experimental reference for incorporating visual healing into SC initiatives.

Developing EMDR for psychosis from a Power, Threat, Meaning Framework perspective

Evidence has been accumulating for the crucial role that trauma plays in the development of psychotic experiences. As such, there has been increased interest in the use of trauma-focused therapies, like Eye Movement Desensitisation and Reprocessing therapy (EMDR), for people who experience psychosis. However, the medical model has traditionally understood psychosis as a brain disease, leading to conceptual confusion regarding the role of trauma memory networks in the development of psychotic experiences. This paper proposes a trauma-informed understanding of psychosis in accordance with the Power, Threat, Meaning Framework (PTMF). The commonalities between the PTMF and the key models employed in EMDR, i.e. the Adaptive Information Processing and Structural Dissociation models, are explored.

Strain rate affects the deformation mechanism of a Ti-55511 titanium alloy: Modeling of constitutive model and 3D processing map using machine learning

The constitutive model and processing map are effective tools for analyzing the hot deformation behavior of titanium alloys. The stress-strain curves of the Ti-5Al-5Mo-5V-1Cr-1Fe alloy were obtained through the Gleeble thermal simulation experiment. The deformation microstructures at different strain rates were characterized by EBSD and TEM. According to the stress-strain curve, the constitutive models were established by using the Arrhenius equation and the BP artificial neural network (BP-ANN). The constitutive model constructed by BP-ANN has higher fitting accuracy, with the Pearson’s R of 0.99902 and the MAPE of 5.499 %. The 3D processing map of the Ti-55511 alloy was constructed by the BP-ANN constitutive model, which takes temperature, strain, and strain rate as variables. In the processing map, the deformation mechanism is dynamic recovery when the power dissipation efficiency is 0.2–0.33, and the deformation mechanism is dynamic recrystallization when the efficiency is 0.33–0.5. At temperatures of 1123 K and 1153 K and the strain of 0.4, the deformation mechanism is dynamic recovery at the strain rate of 1 s−1. At the strain rate of 0.1 s−1, the deformation mechanism is dynamic recrystallization. This is due to the fact that the higher strain rate prevents dislocations from rearranging, thereby inhibiting dynamic recrystallization behavior.

Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria

The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures.

Fine‐Tuned Pretrained Transformer for Amharic News Headline Generation

ABSTRACTAmharic is one of the under‐resourced languages, making news headline generation particularly challenging due to the scarcity of high‐quality linguistic datasets necessary for training effective natural language processing models. In this study, we fine‐tuned the small check point of the T5v1.1 model (t5‐small) to perform Amharic news headline generation with an Amharic dataset that is comprised of over 70k news articles along with their headline. Fine‐tuning the model involves dataset collection from Amharic news websites, text cleaning, news article size optimization using the TF‐IDF algorithm, and tokenization. In addition, a tokenizer model is developed using the byte pair encoding (BPE) algorithm prior to feeding the dataset for feature extraction and summarization. Metrics including Rouge‐L, BLEU, and Meteor were used to evaluate the performance of the model and a score of 0.5, 0.24, and 0.71, respectively, was achieved on the test partition of the dataset that contains 7230 instances. The results were good relative to result of the t5 model without fine‐tuning, which are 0.1, 0.03, and 0.14, respectively. A postprocessing technique using a rule‐based approach was used for further improving summaries generated by the model. The addition of the postprocessing helped the system to achieve Rouge‐L, BLEU, and Meteor scores of 0.72, 0.52, and 0.81, respectively. The result value is relatively better than the result achieved by the nonfine‐tuned T5v1.1 model and the result of previous studies report on abstractive‐based text summarization for Amharic language, which had a 0.27 Rouge‐L score. This contributes a valuable insight for practical application and further improvement of the model in the future by increasing the article length, using more training data, using machine learning–based adaptive postprocessing techniques, and fine‐tuning other available pretrained models for text summarization.

Optimization of machine learning models through quantization and data bit reduction in healthcare datasets

This study focuses on enhancing complex machine learning models through quantization and data bit reduction. The primary goal is to reduce processing time while maintaining model performance, which is particularly relevant for intricate models with prolonged execution times. The research employs two medical datasets, namely Heart Disease Prediction and Breast Cancer Detection, and applies optimization techniques to K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) machine-learning models. To achieve optimization, the study employs effective quantization and data bit reduction techniques such as QuantileTransformer, Numpy.round, and KBinsDiscretizer functions. These techniques are utilized to convert input data from float64 to float32 and int32, resulting in a streamlined data representation. The trade-off between processing time and model accuracy is explored, acknowledging that some compromise in accuracy might occur after optimization. The experimentation reveals that there is a noticeable reduction in time complexity after optimization, with a marginal impact on model accuracy. Interestingly, the study concludes that the outcome and efficiency of optimization techniques are influenced not only by the specific technique used but also by the nature of the dataset and machine learning model under consideration. This comprehensive research showcases the applicability of optimization techniques, specifically quantization and data bit reduction, in complex machine learning models. By conducting experiments on medical datasets and analyzing KNN and SVM models, the study underscores the delicate balance between processing time and model accuracy. The findings emphasize that the success of optimization strategies is context-dependent, relying not only on the chosen technique but also on the interplay between the technique, model, and dataset.

Large-area magnetic skin for multi-point and multi-scale tactile sensing with super-resolution

The advancements in tactile sensor technology have found wide-ranging applications in robotic fields, resulting in remarkable achievements in object manipulation and overall human-machine interactions. However, the widespread availability of high-resolution tactile skins remains limited, due to the challenges of incorporating large-sized, robust sensing units and increased wiring complexity. One approach to achieve high-resolution and robust tactile skins is to integrate a limited number of sensor units (taxels) into a flexible surface material and leverage signal processing techniques to achieve super-resolution sensing. Here, we present a magnetic skin consisting of multi-direction magnetized flexible films and a contactless Hall sensor array. The key features of the proposed sensor include the specific magnetization arrangement, K-Nearest Neighbors (KNN) clustering algorithm and convolutional neural network (CNN) model for signal processing. Using only an array of 4*4 taxels, our magnetic skin is capable of achieving super-resolution perception over an area of 48400 mm2, with an average localization error of 1.2 mm. By employing neural network algorithms to decouple the multi-dimensional signals, the skin can achieve multi-point and multi-scale perception. We also demonstrate the promising potentials of the proposed sensor in intelligent control, by simultaneously controlling two vehicles with trajectory mapping on the magnetic skin.

The judge, the AI, and the Crown: a collusive network

ABSTRACT The article examines the potential implications of ChatGPT ChatGPT (Generative Pre-trained Transformer) [Alec Radford and others, ‘Improving Language Understanding by Generative Pre-Training’ Amazon Web Services (2018) <https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf> accessed 28 May 2024; When this article was submitted, only ChatGPT-3.5 and ChatGPT-4 was available. However, during the revision stage, ChatGPT-4o (also known as GPT-4 Omnus) was introduced to the market, as announced on 13 May 2024.] in judicial decision-making process. This is especially salient given that judicial decisions are made within an interactive ritual chain (Bergman Blix) rather than an ivory tower. This cutting-edge natural language processing model leverages deep learning techniques to potentially aid judiciary members in formulating legal judgements. Therefore, this article assesses the capabilities, limitations, and potential applications of ChatGPT, aiming to evaluate the model's feasibility as a collaborative contributor to legal judgements. In particular, the article examines the utilisation of ChatGPT as an adjunctive tool, supporting human judges in their decision-making processes and, consequently, establishing to what extent artificial intelligence (AI) is used as a tool or collaborator in judicial decision-making. This determines authorship and, accordingly, ownership of judgements.

Artificial intelligence and clinical guidance in male reproductive health: ChatGPT4.0's AUA/ASRM guideline compliance evaluation.

Male infertility is defined as the inability of a male to achieve a pregnancy in a fertile female by the American Urological Association (AUA) and the American Society for Reproductive Medicine (ASRM). Artificial intelligence, particularly in language processing models like ChatGPT4.0, offers new possibilities for supporting clinical decision-making. This study aims to assess the effectiveness of ChatGPT4.0 in responding to clinical queries regarding male infertility, which is aligned with AUA/ASRM guidelines. This observational study employed a design to evaluate the performance of ChatGPT4.0 across 1073 structured clinical queries categorized into true/false, multiple-choice, and open-ended. Two independent reviewers specializing in reproductive medicine assessed the responses using a six-point Likert scale to evaluate accuracy, relevance, and guideline adherence. In the true/false category, the initial accuracy was 92%, which increased to 94% by the end of the study period. For multiple-choice questions, accuracy improved from 85% to 89%. The most significant gains were seen in open-ended questions, where accuracy rose from 78% to 86%. Initially, some responses did not fully align with the AUA/ASRM guidelines. However, by the end of the 60 days, these responses had become more comprehensive and clinically relevant, indicating an improvement in the model's ability to generate guideline-conformant answers (p<0.05). The depth and accuracy of responses for higher difficulty questions also showed enhancement (p<0.01). ChatGPT4.0 can serve as a valuable support tool in managing male infertility, providing reliable, guideline-based information that enhances the accuracy of clinical decision-making tools and supports patient education.

Enriching Building Information Modeling Models through Information Delivery Specification

The efficient acquisition and dissemination of information are crucial in building information modeling (BIM). Current BIM models face significant challenges, including inadequate modeling techniques, poorly defined information requirements, and low interoperability. These issues result in poor information quality and complicate the transition from information acquisition to model processing. Public authorities often provide documentation in various formats, requiring manual transfer to software, which is error-prone and burdensome. This process is particularly difficult for small and medium enterprises lacking resources and knowledge. To address these issues, the IDS (Information Delivery Specification) Collab Tool is under development. This tool aims to automate the import of requirements into authoring software, perform automated compliance checks, and enhance interoperability among stakeholders. It will assist designers in providing accurate information according to requirements through the IDS standard, improving model quality and efficiency from early design stages. Adapting BIM models to specific project requirements and aligning new IDS capabilities with traditional industry practices remain significant challenges. Preliminary evaluations indicate the tool’s potential to significantly improve workflow efficiency and compliance in BIM modeling. However, broader awareness and adoption of the IDS standard are needed. Further research and refinement are essential to fully realize the benefits of digital tools in revolutionizing design and construction practices.

Evolution and advancements in deep learning models for Natural Language Processing

This paper provides a comprehensive review of the evolution and advancements in deep learning models for Natural Language Processing (NLP). It explores the transition from statistical models to neural networks, highlighting the paradigm shift towards data-driven methodologies and the implications for NLP tasks. The emergence of neural network architectures, such as Recurrent Neural Networks (RNNs) and transformer-based models like BERT and GPT, has revolutionized language understanding and generation. Furthermore, the integration of deep learning in traditional NLP tasks, such as part-of-speech tagging and named entity recognition, has led to significant improvements in accuracy and efficiency. The paper also discusses the quantitative analysis of deep learning models, including performance metrics, computational efficiency, and mathematical modeling of language tasks. Case studies and applications, including sentiment analysis, machine translation, and automated content generation, exemplify the transformative impact of deep learning in NLP.

SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities

The rapid development of generative artificial intelligence has brought significant opportunities for the advancement of digital humanities research. Intelligent processing of ancient texts, as an essential part of digital humanities, is also undergoing a transformation in research methodologies in the wave of AIGC. The integration of generative pre-trained models with Chinese ancient texts, a vital carrier of Chinese culture, allows for deep mining of the content of these texts and provides services that make ancient texts more understandable and accessible to the general public. In this research, we propose a method that combines the most renowned Chinese anthology, the “Siku Quanshu,” with generative pre-trained models. We developed the SikuGPT model, a generative model for ancient text processing tasks, based on GPT-type language models by continued pretraining. This model was tested on two typical tasks of ancient text processing: translation between classical and modern Chinese, and classification of ancient texts. The findings reveal that our model achieves advantages in understanding and generating scenarios of ancient texts. The capability of SikuGPT in processing traditional Chinese texts helps to promote the organization of ancient information and knowledge services, and advances the international dissemination of traditional Chinese culture.

Techno economic and life cycle assessment of olefin production through CO2 hydrogenation within the power-to-X concept

The paper deals with exhaustive process modelling, techno-economic and life cycle assessment (TEA/LCA) of olefin (ethylene and propylene) production through captured CO2 and electrolytic hydrogen. Olefins are important building block chemicals with several applications and carbon capture and utilisation (CCU) can provide a sustainable production route. The proposed system involves direct air capture (DAC) of CO2; proton exchange membrane (PEM) water electrolysis for hydrogen production, methanol synthesis, methanol to olefins (MTO) upgrade, and power generation from off-shore wind turbines. This study proposes a new integrated process as the first attempt to holistically assess a whole CCU assembly aiming at olefins production. Processing modelling has been implemented using the Aspen plus V12.1 and MATLAB R2022a software to solve the mass and energy balances of each unit operation. The modelling results showed a carbon efficiency of 72.3% to ethylene and propylene. In addition, the process is designed and integrated in such a way that no external heat supply is required. A specific energy consumption (SEC) of 150 MJ/kg olefins (41 kWh/kg) has been estimated. A minimum selling price of £3.67 per kg of olefins is required for the proposed process to break-even. The sensitivity analysis has revealed that the major cost driver is the cost of electricity. In addition, the life cycle assessment (LCA) has exposed that the proposed synthesis route of olefins has the potential to reduce the global warming potential (GWP) by 47% compared to fossil - based production. The outcomes of this study can be beneficial to engineering conceptual studies, policy makers and contribute new information to the CCU academic community.

Low-cost preparation of 3D-Au@SiO2 substrates for surface-enhanced Raman spectroscopy and finite difference time domain enhancement analysis

Thin films (thickness of ∼2 to 4 μm) comprised of SiO2 particles (300–400 nm) were deposited on glass slides using a screen-printing approach. After annealing, films were functionalized with polyethyleneimine (PEI) to facilitate the adsorption of 3 to 5 nm Au nanoparticles (Au NPs) on the SiO2 particle films, and the resulting Au@SiO2 substrates were used to evaluate their potential for Surface-Enhanced Raman Spectroscopy (SERS) applications. Two types of supported silica particles, monodisperse solid silica (s-SiO2) and textured silica (t-SiO2), were used to investigate the impact of the SiO2 surface properties on the formation of Au NPs hotspots. Monodisperse s-SiO2 particles were synthesized using the Stöber method, while textured surface materials were obtained by incorporating cetrimonium bromide (CTAB) into the solution at a concentration below the critical micelle concentration.SERS studies were conducted using Rhodamine 6G (R6G) as a target molecule. For Au@s-SiO2, the SERS signal results showed a reasonable spatial reproducibility with relative standard deviation values (RSD ∼5 to 16% for studies on six different samples), in agreement with literature values (RSD ∼10 %). In the case of t-SiO2, the spatial reproducibility was also good in some slides with low% RSD between 7 and 12 %, but in other substrates with values as high as 20 % to 50 % RSD. Also, a lower slide-to-slide %RSD value, 26 % vs. 34 %, was found for the s-SiO2 sample.For the Au@t-SiO2 substrates, the estimated calibration sensitivity and the calculated limit of quantification (LoQ) for R6G were 0.392 (log (peak area) vs. log ([R6G]) and 0.381 μM, respectively, close to the visual practical limit. For Au@s-SiO2, the sensitivity was lower, 0.305 vs 0.392, and the LoQ was 0.016 μM, which is lower than the visual practical limit 0.1 μM. To put these results in context, SiO2 and Au@SiO2 films were characterized using different methods, including profilometry, high resolution scanning and transmission electron microscopy (HR-SEM and TEM), UV–visible and Raman spectroscopy.In addition, finite-difference time domain (FDTD) simulations were performed to understand the relationship between substrate morphology and SERS enhancement. Image processing and 3D modelling software were used to convert the HR-SEM images of the SERS substrates into FDTD simulation-compatible models. The simulations showed that the Au@s-SiO2 substrate had a greater average plasmon resonance enhancement over the Au@t-SiO2 substrate, in good agreement with the LoQ differences between substrates, ∼0.02 μM for Au@ s-SiO2 vs. ∼0.4 μM for Au@t-SiO2.

An Analytical Study of the Mikhailov–Novikov–Wang Equation with Stability and Modulation Instability Analysis in Industrial Engineering via Multiple Methods

Solitary waves, inherent in nonlinear wave equations, manifest across various physical systems like water waves, optical fibers, and plasma waves. In this study, we present this type of wave solution within the integrable Mikhailov–Novikov–Wang (MNW) equation, an integrable system known for representing localized disturbances that persist without dispersing, retaining their form and coherence over extended distances, thereby playing a pivotal role in understanding nonlinear dynamics and wave phenomena. Beyond this innovative work, we examine the stability and modulation instability of its gained solutions. These new solitary wave solutions have potential applications in telecommunications, spectroscopy, imaging, signal processing, and pulse modeling, as well as in economic systems and markets. To derive these solitary wave solutions, we employ two effective methods: the improved Sardar subequation method and the (℧′/℧, 1/℧) method. Through these methods, we develop a diverse array of waveforms, including hyperbolic, trigonometric, and rational functions. We thoroughly validated our results using Mathematica software to ensure their accuracy. Vigorous graphical representations showcase a variety of soliton patterns, including dark, singular, kink, anti-kink, and hyperbolic-shaped patterns. These findings highlight the effectiveness of these methods in showing novel solutions. The utilization of these methods significantly contributes to the derivation of novel soliton solutions for the MNW equation, holding promise for diverse applications throughout different scientific domains.

Early identification of high attention content for online mental health community users based on multi-level fusion model

PurposeThe overload of user-generated content in online mental health community makes the focus and resonance tendencies of the participating groups less clear. Thus, the purpose of this paper is to build an early identification mechanism for users' high attention content to promote early intervention and effective dissemination of professional medical guidance.Design/methodology/approachWe decouple the identification mechanism from two processes: early feature combing and algorithmic model construction. Firstly, based on the differentiated needs and concerns of the participant groups, the multiple features of “information content + source users” are refined. Secondly, a multi-level fusion model is constructed for features processing. Specifically, Bidirectional Encoder Representation from Transformers (BERT)-Bi-directional Long-Short Term Memory (BiLSTM)-Linear are used to refine the semantic features, while Graph Attention Networks (GAT) is used to capture the entity attributes and relation features. Finally, the Convolutional Neural Network (CNN) is used to optimize the multi-level fusion features.FindingsThe results show that the ACC of the multi-level fusion model is 84.42%, F1 is 79.43% and R is 76.71%. Compared with other baseline models and single feature elements, the ACC and F1 values are improved to different degrees.Originality/valueThe originality of this paper lies in analyzing multiple features based on early stages and constructing a new multi-level fusion model for processing. Further, the study is valuable for the orientation of psychological patients' needs and early guidance of professional medical care.

A Reference Paper Collection System Using Web Scraping

Collecting reference papers from the Internet is one of the most important activities for progressing research and writing papers about their results. Unfortunately, the current process using Google Scholar may not be efficient, since a lot of paper files cannot be accessed directly by the user. Even if they are accessible, their effectiveness needs to be checked manually. In this paper, we propose a reference paper collection system using web scraping to automate paper collections from websites. This system can collect or monitor data from the Internet, which is considered as the environment, using Selenium, a popular web scraping software, as the sensor; this examines the similarity against the search target by comparing the keywords using the Bert model. The Bert model is a deep learning model for natural language processing (NLP) that can understand context by analyzing the relationships between words in a sentence bidirectionally. The Python Flask is adopted at the web application server, where Angular is used for data presentations. For the evaluation, we measured the performance, investigated the accuracy, and asked members of our laboratory to use the proposed method and provide their feedback. Their results confirm the method’s effectiveness.

Evolving role of public-private stakeholders in technology mediated teaching

Purpose Post-pandemic sovereign authorities in several economies have nudged primary education institutions to adopt platform-based teaching. The shift to platform technology attempts to ensure continuity in the teaching–learning process. In the context of predominantly digitally mediated teaching process, this shift may exacerbate disparities and social injustice by limiting access to primary education in resource-constrained developing economies. The purpose of this study is to explore the efficacy of such a digital framework provided by government and private partners and the challenges faced by the teachers in absence of proper scaffolding. Design/methodology/approach Using an integrative theoretical framework that is composed of capability theory, technology adoption theories and the scaffolding framework, this paper analyses the challenges faced by primary school teachers when adapting to platform-based teaching. Social media analytics along with text analytics using Natural Language Processing and latent Dirichlet allocation-based topic modelling approach to extract latent topics or themes used by users during their tweets related to e-teaching. Findings The findings of this study highlight that adopting a platform-based and hybrid approach improves access to education and flexibility and highlights the importance of scaffolds in achieving desired learning outcomes. EdTech companies can play a significant role through private-public partnership models to offer technical scaffold. Collaborative efforts between educational institutions and EdTech service providers are essential for ensuring the long-term sustainability of platform-based teaching and learning. Originality/value After the pandemic, there has been no published literature available which examined the role of scaffolds and EdTech companies in ensuring digital ecosystem for better teaching–learning outcome through platforms.

New discoveries of stress fluctuations at the tool-chip interface and sticking tool tendency in metal cutting processes

Addressing the issue of sticking tools during the metal cutting process is crucial for enhancing cutting efficiency and ensuring industrial safety. This study employs molecular dynamics (MD) simulations to investigate single-crystal SiC tools and 27 different grain orientations of single-crystal pure metal workpieces (Ni, Cu, Co, and Fe). MD models were established to simulate the nanocutting and tool retraction processes. This study revealed a new phenomenon of stress fluctuations at tool-chip interfaces and proposed a new theory on the relationship between stress fluctuations and metal sticking tool tendencies. Research indicates that during nanocutting, the normal stress distribution at the tool-chip interface of different materials exhibits the same periodic fluctuation (T=0.5a, where a is the tool lattice constant). The greater the stress fluctuation is, the greater the sticking tool tendency. By fitting the results for 27 workpieces, an approximate relationship was established. For metals with the same lattice type, such as FCC-structured Ni and Cu, the stress fluctuation and sticking tool tendency are both greater when the cutting orientation is 11111¯0, and the chip morphology and temperature distribution are similar. Through the analysis of lattice transformation, atomic shear strain, and temperature field, this study explored the microscopic mechanisms and thermomechanical theories underlying the new findings of stress fluctuations and sticking tool tendencies. The stress fluctuation distribution at the tool-chip interface is a new discovery that differentiates nanocutting from traditional processing models. Future research can further investigate the cutting behavior of complex materials under complex conditions, which will help to further elucidate the mechanism of sticking tools during processing.