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Deep Learning-Based Toolkit Inspection:Object Detection and Segmentation in Assembly Lines 认领 引用
1
作者 Arvind Mukundan Riya Karmakar +1 位作者 Devansh Gupta Hsiang-Chen Wang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1255-1277,共23页
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t... Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities. 展开更多
关键词 Tool detection image segmentation object detection assembly line automation Industry 4.0 Intel RealSense deep learning toolkit verification RGB-D imaging quality assurance
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Letter to the Editor:Do we need an artificial intelligence-assisted electrocardiographic tool to diagnose diabetes mellitus or to predict its unseen cardiovascular consequences? 认领 引用
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作者 Ayman El-Menyar 《World Journal of Cardiology》 2026年第6期149-153,共5页
Diabetes mellitus(DM)has a significant negative impact on the global health.Its burden on the medical sector is tremendous as its complications affect all body organs across all age groups.The most dramatic complicati... Diabetes mellitus(DM)has a significant negative impact on the global health.Its burden on the medical sector is tremendous as its complications affect all body organs across all age groups.The most dramatic complications of DM include cardiovascular and neurological disorders.Microvascular damage can start years before the diagnosis of type 2 diabetes(T2DM)is made;therefore,early screening is of utmost value.Moreover,subclinical electrocardiographic(ECG)changes are common in patients with T2DM without evident cardiac disease.I have read with great interest the recent study published in World Journal of Cardiology by Karbovskaya et al,on the utility of a single-lead ECG for diagnosing DM,using machine learning and multinomial regression.The utility of single-lead ECG for predicting glycemic levels appears questionable.It should be interpreted with caution,particularly in light of the model’s limited explanatory power and elevated maximum error.A single-center,non-randomized study with a small sample size in the DM groups and misclassification bias are limitations of the study.A simple,easily accessible tool for early detection or prediction of cardiac dysfunction in DM or in people at risk is more valuable than merely distinguishing healthy from DM or type 1 diabetes from T2DM.A single-lead ECG,especially with artificial intelligence,can flag risk and help bridge some gaps in predicting event risk.However,this study needs validation with a precise aim to predict high-risk diabetics and not only to diagnose DM. 展开更多
关键词 Diabetes mellitus Electrocardiography Artificial intelligence-assisted Diagnostic tool Machine learning Cardiovascular Photoplethysmography Electrocardiographic
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Tool wear in enhanced minimum quantity lubrication assisted milling:From mechanism to application 认领 引用
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作者 Zhenjing DUAN Ziheng WANG +7 位作者 Shuaishuai WANG Bingzhen ZHANG Peng BIAN Yuheng LI Jiyu LIU Jinlong SONG Changhe LI Xin LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期600-639,共40页
Milling is widely used in aerospace structures,molds,automotive parts,and other mechanical parts manufacturing fields.However,milling tool wear is a serious constraint on the production quality,cost control,and produc... Milling is widely used in aerospace structures,molds,automotive parts,and other mechanical parts manufacturing fields.However,milling tool wear is a serious constraint on the production quality,cost control,and productivity of parts.Traditional flood milling depends on large quantities of cutting fluid for cooling and lubrication.Although cutting fluid plays an important role in the cutting of metal materials,this large-scale use not only causes serious pollution of the environment but also poses a threat to the health of workers.As an ideal alternative to cutting fluid,eco-friendly lubricant-based Minimum Quantity Lubrication(MQL)is attracting attention for its clean and sustainable properties.However,when it comes to efficiently milling difficult-tomachine materials,MQL technology still faces technical challenges in terms of mechanical and thermal damage,making it difficult to meet stringent surface integrity requirements.To improve the performance of MQL,enhanced MQL technologies including Nano-lubricant Minimum Quantity Lubrication(NMQL),Cold Plasma(CP)enhanced Minimum Quantity Lubrication(CPMQL),Ultrasonic Vibration(UV)enhanced Minimum Quantity Lubrication(UVMQL),and Cryogenic Minimum Quantity Lubrication(CMQL)have been applied to milling processes.This paper reviews the recent research advances in enhanced MQL technologies and elucidates the key scientific issues.First,the tribological and heat transfer mechanisms of the milling area in MQL-assisted milling are summarized,and the bottleneck of insufficient cooling and lubrication is analyzed.Subsequently,the mechanisms of different enhanced MQL-assisted technologies are summarized and revealed,and the Coefficient Of Friction(COF),milling force,milling temperature,and tool wear under different enhanced MQL conditions are comparatively evaluated.Finally,the research gaps and future exploration directions of enhanced MQL-assisted milling technology are envisioned.It makes it convenient for researchers to gain a deeper understanding of the mechanism,tribological behavior,and development trend of enhanced MQL technology. 展开更多
关键词 Cold plasma Cryogenic minimum quantity lubrication Milling Minimum quantity lubrication Nano-lubricant minimum quantity lubrication Tool wear Ultrasonic vibration
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Surface partitioning and iso-scallop field-based five-axis machining method with a non-spherical cutting tool 认领 引用
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作者 Jiancheng HAO Pengcheng HU +5 位作者 Dong HE Ke CHENG Zhaoyu LI Xiangyu LI Haokun CHEN Bowen DU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期643-660,共18页
The machining performance of five-axis machining can be significantly enhanced by partitioning the surface into subregions,each employing an adaptive machining strategy.This approach is particularly beneficial because... The machining performance of five-axis machining can be significantly enhanced by partitioning the surface into subregions,each employing an adaptive machining strategy.This approach is particularly beneficial because non-spherical cutting tools offer a wide range of effective cutting radii,making them ideal for efficiently machining of complex surfaces while preventing local gouging.Current methods for partitioning complex surface primarily focus on individual surface geometries designed for conventional cutting tools,which are inadequate for accommodating non-spherical cutting tools and fail to consider the comprehensive geometric factors related to both the surface and the cutting tool.In this research,we propose a vertex clustering-based surface partitioning method that utilizes three novel geometric metrics to represent interference conditions,tool orientation smoothness,and cutting width.Based on the partitioned surface,we introduce a method for generating and smoothing a preferred tool orientation vector field.From this,we generate an iso-scallop distance scalar field,where the iso-scallop Cutter Contact(CC)curves are defined as the iso-curves of the proposed scalar field.To validate our proposed method,we conducted computer simulations and physical cutting experiments.The results demonstrated that the average cutting width achieved by our approach significantly surpasses that of two benchmark methods,leading to drastically reduced path lengths and machining time. 展开更多
关键词 Computer aided manufacturing Customized tool Five-axis tool path Iso-scallop tool path Machining efficiency Surface partitioning
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Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease 认领 引用
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作者 Kristina Bliznakova Nikola Kolev +4 位作者 Zhivko Bliznakov Ivan Buliev Anton Tonev Elitsa Encheva Krasimir Ivanov 《Journal of Software Engineering and Applications》 2014年第2期118-127,共10页
Preoperative assessment of the liver volume and function of the remnant liver is a mandatory prerequisite before performing major hepatectomy. The aim of this work is to develop and test a software application for eva... Preoperative assessment of the liver volume and function of the remnant liver is a mandatory prerequisite before performing major hepatectomy. The aim of this work is to develop and test a software application for evaluation of the residual function of the liver prior to the intervention of the surgeons. For this purpose, a complete software platform consisting of three basic modules: liver volume segmentation, visualization, and virtual cutting, was developed and tested. Liver volume segmentation is based on a patient examination with non-contrast abdominal Computed Tomography (CT). The basis of the segmentation is a multiple seeded region growing algorithm adapted for use with CT images without contrast-enhancement. Virtual tumor resection is performed interactively by outlining the liver region on the CT images. The software application then processes the results to produce a three-dimensional (3D) image of the “resected” region. Finally, 3D rendering module provides possibility for easy and fast interpretation of the segmentation results. The visual outputs are accompanied with quantitative measures that further provide estimation of the residual liver function and based on them the surgeons could make a better decision. The developed system was tested and verified with twenty abdominal CT patient sets consisting of different numbers of tomographic images. Volumes, obtained by manual tracing of two surgeon experts, showed a mean relative difference of 4.5%. The application was used in a study that demonstrates the need and the added value of such a tool in practice and in education. 展开更多
关键词 Non-Contrast Enhanced Computed Tomography Images Evaluation of the Residual Function of the Liver Liver Segmentation Seeded Regional Growing Algorithm Virtual Tumor Resection Decision-Making Educational Tool
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Intelligent evolution strategies for high-performance cutting tools:status,challenges,and trends 认领 引用
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作者 Nian Wan Zhihao Wang +2 位作者 Biao Zhao Wenfeng Ding Qi Liu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期250-283,共34页
Traditional tools have limited adaptability in complex machining environments due to their lack of working-condition perception and autonomous regulation.With advances in sensors,materials,and data-processing technolo... Traditional tools have limited adaptability in complex machining environments due to their lack of working-condition perception and autonomous regulation.With advances in sensors,materials,and data-processing technologies,tool design is shifting from a single-function‘mechanical arm’for cutting towards integrated intelligent terminals.This paper systematically reviews progress in intelligent tool technology from two perspectives:design and regulation.For intelligent design,the fundamental principles of condition-perception tools equipped with built-in multi-type sensors are discussed,enabling in situ,real-time monitoring of multidimensional parameters such as cutting force,temperature,and vibration.Force monitoring is achieved through elastic deformation or dynamic charge response,temperature monitoring through the thermoelectric effect,and vibration monitoring through micro-displacement and intensity detection.The design focus emphasises sensor miniaturisation and integration,balancing measurement accuracy with tool stiffness while minimising machining interference.In regulation,key technologies for constructing closed-loop control systems(CLCS)are summarised,which dynamically adjust cutting speed,feed rate,and other parameters based on sensed data,achieving precise control of force,temperature,and vibration via feedback mechanisms and driving units.Breakthroughs in tool wear compensation(TWC)mechanisms are introduced.Multi-source signal fusion combined with deep learning algorithms is further examined for improving monitoring accuracy and remaining useful life(RUL)prediction.Through model predictive control,intelligent regulation of cutting parameters within process flows is realised.Finally,challenges such as sensor reliability,multi-source coupling,and balancing cost with industrial applicability are analysed.Future directions highlight novel structural designs,high-performance material development,and multi-technology integration,aiming to establish a fully intelligent machining system through the integrated design of‘perception-decision-execution’. 展开更多
关键词 intelligent tool condition perception process intelligent control novel tool structure multi-technology integration
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Thermal fatigue of hot work tool steels:a review on damage mechanisms,performance improvement,and evaluation methods 认领 引用
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作者 Peng-Peng Zuo Hai-Lan Zhao +3 位作者 Xin-Yu Chen Hong-Qing Wu Shao-Hong Li Jun-Wan Li 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第7期276-294,共19页
Thermal fatigue failure is one of the main factors affecting the service life of hot-working dies.Thermal fatigue resistance also plays a fundamental role in work safety and cost saving in the rapidly developing autom... Thermal fatigue failure is one of the main factors affecting the service life of hot-working dies.Thermal fatigue resistance also plays a fundamental role in work safety and cost saving in the rapidly developing automotive industry.The recent studies on the thermal fatigue phenomenon of hot work tool steels are reviewed.Those researches primarily focus on damage mechanism,performance improvement,and evaluation methods,encompassing both testing methods and life prediction.Compared to previous researches,notable progress has been made in the following areas:damage mechanisms have been extensively studied from macroscale to microscale.Furthermore,damage mechanisms in different fatigue regimes have also been investigated.In terms of improving the thermal fatigue resistance of hot work tool steels,additive manufacturing is increasingly being adopted as a novel forming method,particularly for designing conformal cooling channel systems in dies.Regarding the evaluation methods for thermal fatigue behavior,iterated numerical analysis and elaborated finite-element models are playing an essential role in predicting thermal fatigue life.This field is currently undergoing tremendous evolution.Finally,current challenges and future research directions are presented for incoming investigators.It is acknowledged that significant scope remains for advancing these areas to more effectively guide the practical manufacture and application of hot-working dies. 展开更多
关键词 Hot work tool steel Thermal fatigue Damage mechanism Performance improvement Evaluation method Life prediction
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Establishment of a molecular tool for blood meal identification in Malaysia 认领 引用 被引量：1
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作者 Ernieenor Faraliana Che Lah Mariana Ahamad +1 位作者 Mohd Subail Haron Ho Tze Ming 《Asian Pacific Journal of Tropical Biomedicine》 CAS 2012年第3期223-227,共5页
Objective:To establish a polymerase chain reaction(PCR) technique based on cytochrome b {cytb) gene of mitochondria DNA(mtDNA) for blood meal identification.Methods:The PCR technique was established based on published... Objective:To establish a polymerase chain reaction(PCR) technique based on cytochrome b {cytb) gene of mitochondria DNA(mtDNA) for blood meal identification.Methods:The PCR technique was established based on published information and validated using blood sample of laboratory animals of which their whole gene sequences are available in CenBank.PCR was next performed to compile gene sequences of different species of wild rodents.The primers used were complementary to the conserved region of the cytb gene of vertebrate's mtDNA.A total of 100 blood samples,both from laboratory animals and wild rodents were collected und analyzed.The obtained unknown sequences were compared with those in the GenBank database using BLAST program to identify the vertebrate animal species.Results:Gene sequences of 11 species of wild animals caught in 9 localities of Peninsular Malaysia were compiled using the established PCR. The animals involved were Rattus(rattus) tanezumi,Rattus tiomanicus,Leopoldamys sabanus, Tupaia glis,Tupaia minor,Niviventor cremoriventor,Rhinosciurus laticaudatus,Calloseiurus caniseps,Sundamys muelleri,Rattus rajah,and Maxomys whitelwadi.The BLAST results confirmed the host with exact or nearly exact matches(>89%identity).Ten new gene sequences have been deposited in CenBank database since September 2010.Conclusions:This study indicates that the PCR direct sequencing system using universal primer sets for vertebrate cytb gene is a promising technique for blood meal identification. 展开更多
关键词 Molecular tool Blood meal identification Polymerase chain reaction Cytochrome b Blood meal Mitochondria DNA Gene sequence Vertebrate Primer GenBank database
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Solidification process of high-speed tool steel based on axial self-rotation of electroslag mold 认领 引用
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作者 Xiao-Fang Shi Shou-Yin Min +2 位作者 Shu-Hao Mei Ye-Liang He Li-Zhong Chang 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第2期387-397,共11页
Based on a self-developed mold axial self-rotation bifilar electroslag remelting system,the regulatory mechanisms of mold axial rotation on the solidification quality of M2 high-speed steel ingots were systematically ... Based on a self-developed mold axial self-rotation bifilar electroslag remelting system,the regulatory mechanisms of mold axial rotation on the solidification quality of M2 high-speed steel ingots were systematically investigated.Through comprehensive metallographic microscopy,metal original position analysis,and numerical simulation,the relationships between axial self-rotation rate and secondary dendrite morphology evolution and elemental segregation characteristics were revealed.The results demonstrated that axial self-rotation of mold significantly improves the metallurgical performance of the electroslag remelting process by enhancing heat transfer,but its effectiveness exhibits a nonlinear variation pattern with axial self-rotation rate.Increasing axial self-rotation rate effectively reduces metal pool depth and refines secondary dendritic structures(exhibiting a 18.18%decrease in dendritic arm spacing at 19 r/min compared to static conditions).However,exceeding the 13 r/min threshold induces qualitative transformation in melt flow patterns:below 13 r/min,mechanical stirring predominates in homogenizing slag pool temperature field,promoting uniform element distribution and enhancing compactness.At higher rotation rate(19 r/min),turbulent sorting effects destabilize solidification fronts,triggering dendrite fragmentation and localized solute aggregation that exacerbate elemental segregation while reducing compactness. 展开更多
关键词 Electroslag remelting Mold axial self-rotation Segregation Solidification High-speed tool steel
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An Adaptive Hybrid Edge-Cloud Collaborative Offloading Method for Large-Scale Computational Tasks of Intelligent Machine Tool:Low-Latency,Energy-Efficient,and Secure 认领 引用
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作者 Zhiwen Lin Kaien Wei +4 位作者 Yiqiao Wang Chuanhai Chen Jinyan Guo Qiang Cheng Zhifeng Liu 《Engineering》 SCIE EI CSCD 2026年第1期201-218,共18页
Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generat... Intelligent machine tools operating in continuous machining environments are commonly influenced by the coupled effects of multi-component degradation and updates in machining tasks.These factors result in the generation of vast multi-source sensor data streams and numerous computational tasks with interdependent data relationships.The stringent real-time constraints and intricate dependency structures present considerable challenges to traditional single-mode computational frameworks.Furthermore,there is a growing demand for computational offloading solutions in intelligent machine tools that extend beyond merely optimizing latency.These solutions must also address energy management for sustainable manufacturing and ensure security to protect sensitive industrial data.This paper introduces an adaptive hybrid edge-cloud collaborative offloading mechanism that combines single-edge-cloud collaboration with multi-edge-cloud collaboration.This mechanism is capable of dynamically switching between collaborative modes based on the status of computational nodes,task characteristics,dependency complexity,and resource availability,ultimately facilitating low-latency,energy-efficient,and secure task processing.A novel hybrid hyper-heuristic algorithm has been developed to address largescale task allocation challenges in heterogeneous edge-cloud environments,enabling the flexible allocation of computational resources and performance optimization.Extensive experiments indicate that the proposed approach achieves average enhancements of 27.36%in task processing time and 7.89%in energy efficiency when compared to state-of-the-art techniques,all while maintaining superior security performance.Validation through case studies on a digital twin gantry five-axis machining center illustrates that the mechanism effectively coordinates task execution across multi-source concurrent data processing,complex dependency task collaboration,high-computational machine learning workloads,and continuous batch task deployment scenarios,achieving a 37.03%reduction in latency and a 25.93%optimization in energy use relative to previous generation collaboration methods.These results provide both theoretical and technical backing for sustainable and secure computational offloading in intelligent machine tools,thereby contributing to the evolution of next-generation smart manufacturing systems. 展开更多
关键词 Intelligent machine tools Edge-cloud collaboration Task offloading Resilient resources Sustainable computing
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Recognition of tool wear in milling using Kolmogorov-Arnold networks and multi-sensor feature fusion 认领 引用
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作者 Min Wan Mingwei Cao +1 位作者 Shuai Wang Xiaowei Zheng 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期90-106,共17页
Existing methods for tool wear recognition using online signal processing and deep learning techniques typically utilize support vector machines(SVM)and fully connected layers(FCL).These methods inherently struggle wi... Existing methods for tool wear recognition using online signal processing and deep learning techniques typically utilize support vector machines(SVM)and fully connected layers(FCL).These methods inherently struggle with capturing complex nonlinear wear patterns due to their dependence on linear transformations and fixed-weight architectures.To overcome these limitations,this study introduces a novel tool wear recognition method based on empirical wavelet transform(EWT)and Kolmogorov-Arnold Network(KAN).Through EWT,tool wear-re-lated component signals are adaptively extracted from multi-sensor data such as cutting forces,accelerations and acoustic emission signals.To further enhance feature extraction,this study designs a multi-scale convolu-tional network(MSCN)and an efficient channel attention(ECA)mechanism.The MSCN is aimed at isolating wear-sensitive features,while the ECA mechanism highlights critical wear indicators,thereby avoiding issues such as modal mixing and energy leakage.Based on these advancements,KAN has finally been adopted to construct the tool wear recognition model.Experimental results prove the feasibility of extracting tool wear component signals through EWT.Leveraging this foundation,the proposed method achieves superior recogni-tion accuracy and feasibility compared to existing approaches.The average recognition accuracy of the proposed model exceeds 99.9%,confirming its effectiveness in tool wear recognition. 展开更多
关键词 Tool wear recognition Empirical wavelet transform Deep learning Attention mechanism Kolmogorov-Arnold network
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An improved FMECA method for CNC machine tools based on combined-variable weight and cloud model 认领 引用
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作者 Tianyu Zhou Hailong Tian +4 位作者 Chuanhai Chen Zhifeng Liu Zhaojun Yang Jia Liu Yuzhi Sun 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期171-180,共10页
As core equipment in modern manufacturing,the reliability of computer numerical control(CNC)machine tools di-rectly impacts factory production efficiency and product quality.Failure mode,effects and criticality analys... As core equipment in modern manufacturing,the reliability of computer numerical control(CNC)machine tools di-rectly impacts factory production efficiency and product quality.Failure mode,effects and criticality analysis(FMECA)is a commonly employed method for reliability analysis.However,the traditional FMECA method suffers from issues such as one-sided consideration of risk factors,equal weighting of risk factors,and high subjectivity in expert scoring.To address the first issue,this study incorporates maintainability(M)as a new risk factor,in addition to the traditional risk factors of severity(S),occurrence(O),and detectability(D).To tackle the second issue,this study adopts a combined weighting approach that integrates subjective weights obtained through the analytic hierarchy process(AHP)with objective weights derived from the maximizing deviation method(MDM).Subsequently,the weights of each risk factor are determined based on variable weight theory.To mitigate the third issue,this study utilizes the cloud model to score each risk factor,thereby reducing scoring subjectivity.Finally,this study employs the technique for order pre-ference by similarity to ideal solution(TOPSIS)method to calculate the risk priority number(RPN)of failure modes and rank the criticality of failure modes and subsystems.Through a case study on a certain type of CNC machine tool and a comparison of the results with those obtained using the traditional FMECA method,the rationality of the proposed approach is validated. 展开更多
关键词 Reliability analysis CNC machine tools FMECA Combined-variable weight Cloud model TOPSIS
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Cdual TAL:multi-domain tool wear prediction using a dualchannel Transformer and cross-attention network 认领 引用
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作者 Na LI Zhendong LIU +2 位作者 Xiao WANG Jiamin JIANG Yanjie WEI 《ENGINEERING Information Technology & Electronic Engineering》 SCIE EI CSCD 2026年第2期69-81,共13页
Accurate tool wear prediction is crucial for manufacturing efficiency,yet effectively using multi-domain sensor features is difficult due to redundant noise.There is a critical need to strategically leverage highly pr... Accurate tool wear prediction is crucial for manufacturing efficiency,yet effectively using multi-domain sensor features is difficult due to redundant noise.There is a critical need to strategically leverage highly predictive strong features and potentially informative weak features.To address this issue,we propose CdualTAL,an improved Transformer-based encoder-attention-decoder algorithm.Its name represents the model’s key components:a correlation-adaptive feature selection algorithm module,a dual-channel Transformer encoder,an attention mechanism,and a long short-term memory(LSTM)decoder.CdualTAL employs a dual-channel encoder to independently process the full set of multi-domain features,along with a subset of strong features selected using a designed correlation-adaptive feature selection algorithm.A custom cross-attention mechanism is then used to fuse these representations,sharpening focus on strong features while judiciously integrating information from weak ones.Finally,a hierarchical LSTM decoder captures deep temporal dependencies.Validated on tool wear datasets,CdualTAL outperforms 11 state-of-the-art methods,achieving superior prediction stability and accuracy with an average R2 of 0.983 and a root mean square error(RMSE)of 4.373. 展开更多
关键词 Multi-domain features Dual-channel Feature fusion Tool wear Attention mechanism Feature enhancement
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Individual Software Expertise Formalization and Assessment from Project Management Tool Databases 认领 引用
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作者 Traian-Radu Plosca Alexandru-Mihai Pescaru +1 位作者 Bianca-Valeria Rus Daniel-Ioan Curiac 《Computers, Materials & Continua》 SCIE EI 2026年第1期389-411,共23页
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods... Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results. 展开更多
关键词 Expertise formalization transformer-based models natural language processing augmented data project management tool skill classification
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Laser Surface Hardening of Tool Steels—Experimental and Numerical Analysis 认领 引用 被引量：3
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作者 Abdel-Monem El-Batahgy Ramadan Ahmed Ramadan Abdel-Rahman Moussa 《Journal of Surface Engineered Materials and Advanced Technology》 2013年第2期146-153,共8页
This research work is focused on both experimental and numerical analysis of laser surface hardening of AISI M2 high speed tool steel. Experimental analysis aims at clarifying effect of different laser processing para... This research work is focused on both experimental and numerical analysis of laser surface hardening of AISI M2 high speed tool steel. Experimental analysis aims at clarifying effect of different laser processing parameters on properties and performance of laser surface treated specimens. Numerical analysis is concerned with analytical approaches that provide efficient tools for estimation of surface temperature, surface hardness and hardened depth as a function of laser surface hardening parameters. Results indicated that optimization of laser processing parameters including laser power, laser spot size and processing speed combination is of considerable importance for achieving maximum surface hardness and deepest hardened zone. In this concern, higher laser power, larger spot size and lower processing speed are more efficient. Hardened zone with 1.25 mm depth and 996 HV surface hardness was obtained using 1800 W laser power, 4 mm laser spot size and 0.5 m/min laser processing speed. The obtained maximum hardness of laser surface treated specimen is 23% higher than that of conventionally heat treated specimen. This in turn has resulted in 30% increase in wear resistance of laser surface treated specimen. Numerical analysis has been carried out for calculation of temperature gradient and cooling rate based on Ashby and Easterling equations. Then, surface hardness and hardened depth have been numerically estimated based on available Design-Expert software. Numerical results indicated that cooling rate of laser surface treated specimen is high enough to be beyond the nose of the CCT diagram of the used steel that in turn resulted in a hard/martensitic structure. Numerically estimated values of surface temperature, surface hardness and hardened depth as a function of laser processing parameters are in a good agreement with experimental results. Laser processing charts indicating expected values of surface temperature, surface hardness and hardened depth as a function of different wider range of laser processing parameters are proposed. 展开更多
关键词 High Speed Tool Steels Laser Surface Hardening Ashby and Easterling Heat Transfer Equations Design Expert Software Laser Processing Charts
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Research Progress on Nurse-Driven Warning Tools for Early Recognition of ICU Sepsis 认领 引用
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作者 Qianqian Guan Xuan Shen +1 位作者 Yingzi Yuan Ying Zhang 《Journal of Clinical and Nursing Research》 2026年第4期93-100,共8页
Sepsis is the primary cause of deterioration and death in patients in the intensive care unit(ICU).Early identification and timely intervention are crucial for improving prognosis.Nurses,as frontline personnel providi... Sepsis is the primary cause of deterioration and death in patients in the intensive care unit(ICU).Early identification and timely intervention are crucial for improving prognosis.Nurses,as frontline personnel providing bedside continuous monitoring,play a pivotal role in the early recognition of sepsis.This article systematically reviews the evolution,research progress,current challenges and shortcomings of nurse-driven early warning tools for sepsis in the ICU,and looks forward to future development directions,aiming to provide a reference for related research and clinical practice. 展开更多
关键词 Sepsis Early recognition Intensive care Nurse-driven Warning tools Review
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Use of Environmental Sequence Stratigraphy (ESS) as an Environmental Forensic Tool to Identify Chlorinated Solvent Sources at a Complex Site in Silicon Valley, California 认领 引用
17
作者 Rick Cramer Jun Lu +2 位作者 Mike Shultz Colin Plank Herb Levine 《Journal of Environmental Protection》 2018年第5期554-566,共13页
This paper presents a case study for a complex contaminated groundwater site impacted by a historical release of chlorinated solvents in Silicon Valley, California. The original conceptual site model (CSM) inferred a ... This paper presents a case study for a complex contaminated groundwater site impacted by a historical release of chlorinated solvents in Silicon Valley, California. The original conceptual site model (CSM) inferred a contaminant migration pathway based on the groundwater gradient interpreted from groundwater elevation data, which is based on the underlying assumption that the subsurface conditions are homogeneous. However, the buried channel deposits render the underlying geology highly heterogeneous, and this heterogeneity plays a significant role in the subsurface migration of contaminants. Chemical fingerprinting evidence suggested that contamination at the downgradient property boundary was related to an off-site contaminant source. But, this alone was not a compelling argument. However, Environmental Sequence Stratigraphy (ESS), a geology-based environmental forensic technique, was applied to define the permeability architecture or the “plumbing” that controls subsurface fluid flow and contaminant migration. First, the geologic and depositional setting was synthesized based on regional geologic data, and representative facies models were identified for the site. Second, the existing CSM and site lithology data were reviewed and existing lithology data were graphically presented to display vertical grain-size patterns. This analysis focused on the nexus between the depositional environment and the site-specific subsurface data resulting in correlations/interpretations between and beyond data points that are based on established stratigraphic principles. The depositional environment results in buried river channels as the primary control on subsurface fluid flow, which defines hydrostratigraphic units (or HSUs). Finally, a hydrostratigraphic CSM that includes maps and cross sections was constructed to depict the HSUs present as a framework to integrate hydro-geology and chemistry data. This study demonstrates that: 1) Highly per-meable buried river channel deposits control subsurface fluid flow and contaminant transport, and have distinct chemical constituents and concentrations (i.e., they represent distinct HSUs), 2) Mapping of such HSUs is feasible with existing boring log data, 3) In settings such as the Santa Clara Valley where groundwater flow is governed by subsurface channel deposits, a hydrostratigraphic mapping approach is superior to a depth-based aquifer zonation approach, and 4) For heterogeneous subsurface, a detailed geology-based definition of the subsurface is an integral component of an environmental forensic analyses to determine contaminant source(s) and pathways. 展开更多
关键词 Environmental Sequence Stratigraphy Hydrostratigraphic Units HSU Contaminant Sources Source Identification Migration Pathways Depositional Environment Chlorinated Solvents Environmental Forensic Tool
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Adequate Advance Provided by a Familiar Fluid Dynamic Balance Principle to Tools and Techniques for Water Quality Interpretation: Experienced in Yaoundé(Cameroun) 2ndSupply Dam 认领 引用
18
作者 Kom Olivia Estelle Mbane Biouele César 《Open Journal of Marine Science》 2015年第1期158-168,共11页
In a fluid (liquid or gas) at rest, the isobars are horizontal surface. This fluid dynamic balance theorem provides adequate advance to tools and techniques for Water Quality Interpretation. We deal in this paper, wit... In a fluid (liquid or gas) at rest, the isobars are horizontal surface. This fluid dynamic balance theorem provides adequate advance to tools and techniques for Water Quality Interpretation. We deal in this paper, with an effective way of exploiting the familiar communicating containers’ principle. That formally consists on providing water samples from desired depths of rivers, oceans, retention dams, etc. The prevailing limiting factor to achieve this feat is the length of our sampling pipes named Mbane Bathymetric Tube (MBT) designed for this purpose when rivers or retention dams are very deep. Providing drinking water to urban growing populations is a challenge that no government can escape. Therefore, improving the tools and techniques for water quality interpretation is an adequate advance for drinking water managerial techniques because this allows the recovery of contaminated water which abounds on the earth by acquiring appropriate wastewater treatment stations. The aim of the manuscript is to provide a brief theoretical description of our designed sampling equipment to allow everyone who is going to use it to solve in advance problems brought by Archimedes’ pressure force when experiencing the sampling pipes. Archimedes’ pressure force acts mainly when moving the sampling pipes to water lower levels and then opening its protective cover which allows the communication with the supply dam. 展开更多
关键词 Communicating Containers’ Principle Water Samples from Desired Depths of Rivers Mbane Bathymetric Tube (MBT) Tools for Water Quality Interpretation
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A novel tool path planning algorithm of denture grinding based on iso-planar method 认领 引用
19
作者 Yongfei Wang Xingju Tang +3 位作者 Yong Zhang Chenxi Gao Tao Wu Chen Li 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期107-123,共17页
Dental restorations feature intricate surface contours,necessitating an effective path planning strategy of grinding tools to achieve precise outcomes.A novel tool path generation strategy based on offset surfaces was... Dental restorations feature intricate surface contours,necessitating an effective path planning strategy of grinding tools to achieve precise outcomes.A novel tool path generation strategy based on offset surfaces was proposed for grinding of complex ceramic denture crowns.This strategy employed a half-edge data structure to reconstruct the topology of the triangular mesh model and leveraged the topological relationships among facets to develop a division algorithm specifically for denture crowns.Based on the structural characteristics of denture crowns and machine tool performance,these crowns were categorized into four areas.The vertex offset method was utilized to create offset surfaces for grids in each area,while the iso-planar method calculated the initial tool path corresponding to these offset surfaces.Optimization algorithms were introduced to rectify issues such as redundancy in tool paths,self-intersections,Z-shaped trajectories,overcutting in ridge regions,and the need for special optimization in cavity regions present in the original tool path.Given that the offset surface mesh derives from an original model mesh through an offset process,its density correlates with feature complexity;this property enables fewer tool points while still meeting accuracy requirements.Furthermore,these optimization algorithms effectively addressed defects found in initial paths and facilitated efficient high-precision grinding of denture crowns.Experimental results indicated that surface accuracy and machining efficiency achieved by this algorithm meet medical standards. 展开更多
关键词 Tool path generation Iso-planar method Grinding Denture crown
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Offset fabrication of off-axis aspherical surfaces with large off-axis amounts by slow tool servo single-point diamond turning 认领 引用
20
作者 Menghui Lan Bing Li +1 位作者 Xiang Wei Xiuyuan Wu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期179-190,共12页
Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomic... Off-axis aspherical mirrors are widely used in optical systems and precision measuring instruments,whereas off-axis aspherical mirrors with large sizes and off-axis are used in large optical systems such as astronomical telescopes and radio telescopes.However,if the off-axis amount of an off-axis aspherical mirror exceeds the capability of the machine tool,traditional rotary-turning machining methods are not applicable,and advanced computerized numerical control(CNC)machining methods,such as the slow-tool-servo method,must be im-plemented.This article proposes a non-conventional offset(NCO)fabrication method based on slow-tool-servo single-point diamond turning for machining off-axis aspherical surfaces with large off-axis amounts.This method is theoretically applicable to the machining of off-axis aspherical surfaces with any off-axis amount.NCO fab-rication is a simpler and more efficient path-planning solution for machining individual off-axis parabolic sur-faces.In addition,corresponding solutions for other types of aspherical surfaces are proposed using the NCO method.The turning depths of workpieces with different off-axis amounts at the same machining position are analyzed and compared.A specific measurement scheme for the NCO method is presented,and the experimental results indicate that the PV and RMS form errors are 0.658μm and 60 nm,respectively.This work demonstrates that the NCO method can effectively deal with the machining challenges of off-axis aspherical structures with large off-axis amounts. 展开更多
关键词 Offset fabrication Off-axis aspheric Slow tool servo Single-point diamond turning
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