改良Aldrete量表和DPART在全身麻醉术后复苏病人中的应用效果比较

目的:探讨中文版DPART量表与改良Aldrete量表对全身麻醉病人术后复苏效果的评估效果。方法:以全身麻醉术后入麻醉复苏室(PACU)接受治疗的病人为研究对象,按手术日期先后顺序分为改良Aldrete组和DPART组。改良Aldrete组105例病人采用改良Aldrete作为出室评估工具,DPART组109例病人采用DPART作为出室评估工具,比较两组病人PACU停留时间、出室时疼痛评分、镇静躁动得分、苏醒期躁动发生率、恶心呕吐评分和护士对于两种量表的可行性、实用性评价。结果:DPART组PACU停留时间长于改良Aldrete组(P<0.05),但出室时疼痛评分、恶心呕吐评分低于改良Aldrete组(P<0.05);两组出室时镇静躁动得分及苏醒期躁动发生率比较差异无统计学意义(P>0.05);DPART组量表的可行性评分低于改良Aldrete组(P<0.05),实用性评分高于改良Aldrete组(P<0.05)。结论:应用中文版DPART量表评估PACU病人,病人PACU停留时间较使用改良Aldrete量表评估延长,但病人出室时各项体征表现较好,更有利于保障全身麻醉术后病人安全转出PACU。

Shock index and its variants as predictors of mortality in severe traumatic brain injury

BACKGROUND The increase in severe traumatic brain injury(sTBI)incidence is a worldwide phenomenon,resulting in a heavy disease burden in the public health systems,specifically in emerging countries.The shock index(SI)is a physiological parameter that indicates cardiovascular status and has been used as a tool to assess the presence and severity of shock,which is increased in sTBI.Considering the high mortality of sTBI,scrutinizing the predictive potential of SI and its variants is vital.AIM To describe the predictive potential of SI and its variants in sTBI.METHODS This study included 71 patients(61 men and 10 women)divided into two groups:Survival(S;n=49)and Non-survival(NS;n=22).The responses of blood pressure and heart rate(HR)were collected at admission and 48 h after admission.The SI,reverse SI(rSI),rSI multiplied by the Glasgow Coma Score(rSIG),and Age multiplied SI(AgeSI)were calculated.Group comparisons included Shapiro-Wilk tests,and independent samples t-tests.For predictive analysis,logistic regression,receiver operator curves(ROC)curves,and area under the curve(AUC)measurements were performed.RESULTS No significant differences between groups were identified for SI,rSI,or rSIG.The AgeSI was significantly higher in NS patients at 48 h following admission(S:26.32±14.2,and NS:37.27±17.8;P=0.016).Both the logistic regression and the AUC following ROC curve analysis showed that only AgeSI at 48 h was capable of predicting sTBI outcomes.CONCLUSION Although an altered balance between HR and blood pressure can provide insights into the adequacy of oxygen delivery to tissues and the overall cardiac function,only the AgeSI was a viable outcome-predictive tool in sTBI,warranting future research in different cohorts.

基于Ladybug Tools对日光温室的方位角与光照模拟

日光温室是重要的农业基础设施,温室的方位角是评判温室结构设计是否合理的基础之一。选择较好的温室方位角拥有更优的采光性能是本文研究的意义。为此,选择三维可视化技术模拟分析日光温室的光照值,为更好地研究分析日光温室采光性能及光照变化提供较为直观的途径和方法,为优化设计高产、高效、节能的日光温室提供一定的辅助作用。通过实测数据和模拟数据的对比,说明Ladybug Tools软件模拟不同方位角的变化及和田日光温室的光照的对比准确度较高,误差不超过5%,为研究日光温室在光照方面的设计提供了一定的理论依据和参考。

Novel Batch Polishing Method of Ceramic Cutting Inserts for Reducing Tool Wear

Ceramic cutting inserts are a type of cutting tool commonly used in high-speed metal cutting applications.However,the wear of these inserts caused by friction between the workpiece and cutting inserts limits their overall effectiveness.In order to improve the tool life and reduce wear,this study introduces an emerging method called magnetic field-assisted batch polishing(MABP)for simultaneously polishing multiple ceramic cutting inserts.Several polishing experiments were conducted under different conditions,and the wear characteristics were clarified by cutting S136H steel.The results showed that after 15 min of polishing,the surface roughness at the flank face,edge,and nose of the inserts was reduced to below 2.5 nm,6.25 nm,and 45.8 nm,respectively.Furthermore,the nose radii of the inserts did not change significantly,and there were no significant changes in the weight percentage of elements before and after polishing.Additionally,the tool life of the batch polished inserts was found to be up to 1.75 times longer than that of unpolished inserts.These findings suggest that the MABP method is an effective way to mass polish ceramic cutting inserts,resulting in significantly reduced tool wear.Furthermore,this novel method offers new possibilities for polishing other tools.

Digital Twin Modeling Enabled Machine Tool Intelligence:A Review

Machine tools,often referred to as the“mother machines”of the manufacturing industry,are crucial in developing smart manufacturing and are increasingly becoming more intelligent.Digital twin technology can promote machine tool intelligence and has attracted considerable research interest.However,there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool intelligence.Herein,digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital twin.The review then delves into state-of-the-art digital twin modelingenabled machine tool intelligence,examining it from the aspects of data-based modeling and mechanism-data dual-driven modeling.Additionally,it highlights three bottleneck issues facing the field.Considering these problems,the architecture of a digital twin machine tool(DTMT)is proposed,and three key technologies are expounded in detail:Data perception and fusion technology,mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology,and dynamic optimization and collaborative control technology for multilevel parameters.Finally,future research directions for the DTMT are discussed.This work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence,making it significant for developing intelligent machine tools.

Nutritional Assessment Tools for Patients with Cancer:A Narrative Review

Cancer patients are at high risk of malnutrition,which can lead to adverse health outcomes such as prolonged hospitalization,increased complications,and increased mortality.Accurate and timely nutritional assessment plays a critical role in effectively managing malnutrition in these patients.However,while many tools exist to assess malnutrition,there is no universally accepted standard.Although different tools have their own strengths and limitations,there is a lack of narrative reviews on nutritional assessment tools for cancer patients.To address this knowledge gap,we conducted a non-systematic literature search using PubMed,Embase,Web of Science,and the Cochrane Library from their inception until May 2023.A total of 90 studies met our selection criteria and were included in our narrative review.We evaluated the applications,strengths,and limitations of 4 commonly used nutritional assessment tools for cancer patients:the Subjective Global Assessment(SGA),Patient-Generated Subjective Global Assessment(PG-SGA),Mini Nutritional Assessment(MNA),and Global Leadership Initiative on Malnutrition(GLIM).Our findings revealed that malnutrition was associated with adverse health outcomes.Each of these 4 tools has its applications,strengths,and limitations.Our findings provide medical staff with a foundation for choosing the optimal tool to rapidly and accurately assess malnutrition in cancer patients.It is essential for medical staff to be familiar with these common tools to ensure effective nutritional management of cancer patients.

Geometric Error Identification of Gantry-Type CNC Machine Tool Based on Multi-Station Synchronization Laser Tracers

Laser tracers are a three-dimensional coordinate measurement system that are widely used in industrial measurement.We propose a geometric error identification method based on multi-station synchronization laser tracers to enable the rapid and high-precision measurement of geometric errors for gantry-type computer numerical control(CNC)machine tools.This method also improves on the existing measurement efficiency issues in the single-base station measurement method and multi-base station time-sharing measurement method.We consider a three-axis gantry-type CNC machine tool,and the geometric error mathematical model is derived and established based on the combination of screw theory and a topological analysis of the machine kinematic chain.The four-station laser tracers position and measurement points are realized based on the multi-point positioning principle.A self-calibration algorithm is proposed for the coordinate calibration process of a laser tracer using the Levenberg-Marquardt nonlinear least squares method,and the geometric error is solved using Taylor’s first-order linearization iteration.The experimental results show that the geometric error calculated based on this modeling method is comparable to the results from the Etalon laser tracer.For a volume of 800 mm×1000 mm×350 mm,the maximum differences of the linear,angular,and spatial position errors were 2.0μm,2.7μrad,and 12.0μm,respectively,which verifies the accuracy of the proposed algorithm.This research proposes a modeling method for the precise measurement of errors in machine tools,and the applied nature of this study also makes it relevant both to researchers and those in the industrial sector.

On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

Comparison of bone artifacts from the Schöningen site in Germany and the Lingjing site in China

Similarities play an important role in the reconstruction of human physical,cultural and technological evolution.The two sites presented in this paper,the Middle Palaeolithic site Lingjing in China Layer 10 and 11 and the Lower Palaeolithic site Schöningen 13Ⅱ-4,the socalled Schöningen Spear Horizon in Germany,show striking similarities.The archaeological record of both sites includes lithic artifacts as well as a very large assemblage of fossil bones.The preservation of the material at both sites is excellent and the faunas encountered at both sites show many similarities.The faunal lists of both sites include a diverse carnivore guild,an elephant species,two different rhinoceros species,two different equids,different cervids and large bovids.Both sites also yielded bone retouchers as well as a unique record of bone hammers that show identical,unusual flaking and percussion damage.These similarities are remarkable if one takes into account the difference in age(ca 200 kaBP)and the geographical distance between the two sites of ca 8000 km.Therefore,we do not assume a close cultural link between the hominin populations active at both sites.The authors assume that the observed similarities show more or less identical,opportunistic hominin behaviour at both sites located in a comparable environment with more or less similar taphonomic conditions.

Assessing forest cover changes and fragmentation in the Himalayan temperate region: implications for forest conservation and management

This study comprehensively assessed long-term vegetation changes and forest fragmentation dynamics in the Himalayan temperate region of Pakistan from 1989 to 2019.Four satellite images,including Landsat-5 TM and Landsat-8 Operational Land Imager(OLI),were chosen for subsequent assessments in October 1989,2001,2011 and 2019.The classified maps of 1989,2001,2011 and 2019 were created using the maximum likelihood classifier.Post-classification comparison showed an overall accuracy of 82.5%and a Kappa coefficient of 0.79 for the 2019 map.Results revealed a drastic decrease in closed-canopy and open-canopy forests by 117.4 and 271.6 km^(2),respectively,and an increase in agriculture/farm cultivation by 1512.8 km^(2).The two-way ANOVA test showed statistically significant differences in the area of various cover classes.Forest fragmentation was evaluated using the Landscape Fragmentation Tool(LFT v2.0)between 1989 and 2019.The large forest core(>2.00 km^(2))decreased from 149.4 to 296.7 km^(2),and a similar pattern was observed in medium forest core(1.00-2.00 km^(2))forests.On the contrary,the small core(<1.00 km^(2))forest increased from 124.8 to 145.3 km^(2) in 2019.The perforation area increased by 296.9 km^(2),and the edge effect decreased from 458.9 to 431.7 km^(2).The frequency of patches also increased by 119.1 km^(2).The closed and open canopy classes showed a decreasing trend with an annual rate of 0.58%and 1.35%,respectively.The broad implications of these findings can be seen in the studied region as well as other global ecological areas.They serve as an imperative baseline for afforestation and reforestation operations,highlighting the urgent need for efficient management,conservation,and restoration efforts.Based on these findings,sustainable land-use policies may be put into place that support local livelihoods,protect ecosystem services,and conserve biodiversity.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

3D modeling for effect of tool eccentricity on coupled thermal and material flow characteristics during friction stir welding

A novel three-dimensional numerical model is proposed to investigate the effect of tool eccentricity on the coupled thermal and material flow characteristics in friction stir welding(FSW) process.An asymmetrical boundary condition at the tool-workpiece interface,and the dynamic mesh technique are both employed for the consideration of the tool eccentricity during tool rotating.It is found that tool eccentricity induces the periodical variation of the heat densities both at the tool-workpiece interface and inside the shear layer,but the fluctuation amplitudes of the heat density variations are limited.However,it is demonstrated that tool eccentricity results in significant variation of the material flow behavior in one tool rotating period.Moreover,the material velocity variation at the retreating side is particularly important for the formation of the periodic characteristics in FSW.The modeling result is found to be in good agreement with the experimental one.

A review on the multi-scaled structures and mechanical/thermal properties of tool steels fabricated by laser powder bed fusion additive manufacturing

The laser powder bed fusion(LPBF) process can integrally form geometrically complex and high-performance metallic parts that have attracted much interest,especially in the molds industry.The appearance of the LPBF makes it possible to design and produce complex conformal cooling channel systems in molds.Thus,LPBF-processed tool steels have attracted more and more attention.The complex thermal history in the LPBF process makes the microstructural characteristics and properties different from those of conventional manufactured tool steels.This paper provides an overview of LPBF-processed tool steels by describing the physical phenomena,the microstructural characteristics,and the mechanical/thermal properties,including tensile properties,wear resistance,and thermal properties.The microstructural characteristics are presented through a multiscale perspective,ranging from densification,meso-structure,microstructure,substructure in grains,to nanoprecipitates.Finally,a summary of tool steels and their challenges and outlooks are introduced.

A Systematic Review and Performance Evaluation of Open-Source Tools for Smart Contract Vulnerability Detection

With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.

Preheating-assisted solid-state friction stir repair of Al-Mg-Si alloy plate at different rotational speeds

Additive friction stir deposition(AFSD)is a novel structural repair and manufacturing technology has become a research hotspot at home and abroad in the past five years.In this work,the microstructural evolution and mechanical performance of the Al-Mg-Si alloy plate repaired by the preheating-assisted AFSD process were investigated.To evaluate the tool rotation speed and substrate preheating for repair quality,the AFSD technique was used to additively repair 5 mm depth blind holes on 6061 aluminum alloy substrates.The results showed that preheat-assisted AFSD repair significantly improved joint bonding and joint strength compared to the control non-preheat substrate condition.Moreover,increasing rotation speed was also beneficial to improve the metallurgical bonding of the interface and avoid volume defects.Under preheating conditions,the UTS and elongation were positively correlated with rotation speed.Under the process parameters of preheated substrate and tool rotation speed of 1000 r/min,defect-free specimens could be obtained accompanied by tensile fracture occurring in the substrate rather than the repaired zone.The UTS and elongation reached the maximum values of 164.2MPa and 13.4%,which are equivalent to 99.4%and 140%of the heated substrate,respectively.

A Cross-matching Service for Data Center of Xinjiang Astronomical Observatory

Cross-matching is a key technique to achieve fusion of multi-band astronomical catalogs. Due to different equipment such as various astronomical telescopes, the existence of measurement errors, and proper motions of the celestial bodies, the same celestial object will have different positions in different catalogs, making it difficult to integrate multi-band or full-band astronomical data. In this study, we propose an online cross-matching method based on pseudo-spherical indexing techniques and develop a service combining with high performance computing system(Taurus) to improve cross-matching efficiency, which is designed for the Data Center of Xinjiang Astronomical Observatory. Specifically, we use Quad Tree Cube to divide the spherical blocks of the celestial object and map the 2D space composed of R.A. and decl. to 1D space and achieve correspondence between real celestial objects and spherical patches. Finally, we verify the performance of the service using Gaia 3 and PPMXL catalogs. Meanwhile, we send the matching results to VO tools-Topcat and Aladin respectively to get visual results. The experimental results show that the service effectively solves the speed bottleneck problem of crossmatching caused by frequent I/O, and significantly improves the retrieval and matching speed of massive astronomical data.

Resolution Enhancement of SOHO/MDI Magnetograms

Research on the solar magnetic field and its effects on solar dynamo mechanisms and space weather events has benefited from the continual improvements in instrument resolution and measurement frequency.The augmentation and assimilation of historical observational data timelines also play a significant role in understanding the patterns of solar magnetic field variation.Within the realm of astronomical data processing,super-resolution(SR)reconstruction refers to the process of using a substantial corpus of training data to learn the nonlinear mapping between low-resolution(LR)and high-resolution(HR)images,thereby achieving higherresolution astronomical images.This paper is an application study in high-dimensional nonlinear regression.Deep learning models were employed to perform SR modeling on SOHO/MDI magnetograms and SDO/HMI magnetograms,thus reliably achieving resolution enhancement of full-disk SOHO/MDI magnetograms and enhancing the image resolution to obtain more detailed information.For this study,a data set comprising 9717pairs of data from 2010 April to 2011 February was used as the training set,1332 pairs from 2011 March were used as the validation set and 1034 pairs from 2011 April were used as the test set.After data preprocessing,we randomly cropped 128×128 sub-images as the LR cases from the full-disk MDI magnetograms,and the corresponding 512×512 sub-images as HR ones from the HMI full-disk magnetograms for model training.The tests conducted have shown that the study successfully produced reliable 4×SR reconstruction of full-disk MDI magnetograms.The MESR model's results(0.911)were highly correlated with the target HMI magnetographs as indicated by the correlation coefficient values.Furthermore,the method achieved the best PSNR,SSIM,MAE and RMSE values,indicating that the MESR model can effectively reconstruct magnetograms.

Phosphorylated protein chip combined with artificial intelligence tools for precise drug screening

We have developed a protein array system,named"Phospho-Totum",which reproduces the phosphorylation state of a sample on the array.The protein array contains 1471 proteins from 273 known signaling pathways.According to the activation degrees of tyrosine kinases in the sample,the corresponding groups of substrate proteins on the array are phosphorylated under the same conditions.In addition to measuring the phosphorylation levels of the 1471 substrates,we have developed and performed the artificial intelligence-assisted tools to further characterize the phosphorylation state and estimate pathway activation,tyrosine kinase activation,and a list of kinase inhibitors that produce phosphorylation states similar to that of the sample.The Phospho-Totum system,which seamlessly links and interrogates the measurements and analyses,has the potential to not only elucidate pathophysiological mechanisms in diseases by reproducing the phosphorylation state of samples,but also be useful for drug discovery,particularly for screening targeted kinases for potential drug kinase inhibitors.

Analysis of secondary makeup characteristics of drill collar joint and prediction of downhole equivalent impact torque of Well SDTK1

Based on the three-dimensional elastic-plastic finite element analysis of the 8"(203.2 mm)drill collar joint,this paper studies the mechanical characteristics of the pin and box of NC56 drill collar joints under complex load conditions,as well as the downhole secondary makeup features,and calculates the downhole equivalent impact torque with the relative offset at the shoulder of internal and external threads.On the basis of verifying the correctness of the calculation results by using measured results in Well GT1,the prediction model of the downhole equivalent impact torque is formed and applied in the first extra-deep well with a depth over 10000 m in China(Well SDTK1).The results indicate that under complex loads,the stress distribution in drill collar joints is uneven,with relatively higher von Mises stress at the shoulder and the threads close to the shoulder.For 203.2 mm drill collar joints pre-tightened according to the make-up torque recommended by American Petroleum Institute standards,when the downhole equivalent impact torque exceeds 65 kN·m,the preload balance of the joint is disrupted,leading to secondary make-up of the joint.As the downhole equivalent impact torque increases,the relative offset at the shoulder of internal and external threads increases.The calculation results reveal that there exists significant downhole impact torque in Well SDTK1 with complex loading environment.It is necessary to use double shoulder collar joints to improve the impact torque resistance of the joint or optimize the operating parameters to reduce the downhole impact torque,and effectively prevent drilling tool failure.

Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.