Development of a one-axis manipulator for laser-assisted machining

Laser-assisted machining has been considered as a new alternative machining method of difficult-to-cut materials. A laser module with one-axis manipulator is developed to focus on preheating laser beam effectively. First of all, the thermal characteristic analysis was performed to verify the importance of laser module location. Laser module should be moved within 1 mm. Analysis conditions of three positions in driving range of the one-axis manipulator are selected. And a C coupling is used as a connection device for spindle and laser module. An initial model has one C coupling, and the number of C coupling has been increased from 1 to 2 in an improved model. And the analysis is carried out again for the one-axis manipulator. The results of the static analysis, the maximum displacement and the maximum stress are decreased by 22% and 11%, respectively, for the improved model when the laser module is located at farthest away from the spindle unit. As a result of the modal analysis, the first natural frequency mode is increased by 13%, 18% and 12% at these positions of the improved model, respectively. The harmonic analysis of the improved model was performed by analyzing the results of the modal analysis. The maximum deformation was 0.33 mm in driving unit at 222 Hz. And the maximum compliance of the ISO axis direction was 0.23 mm/N. Finally, the one-axis manipulator has been fabricated successfully using the analysis result.

Laser-assisted water jet machining of high quality micro-trap structures on stainless steel surfaces

Secondary electron emission(SEE)has emerged as a critical issue in next-generation accelerators.Mitigating SEE on metal surfaces is crucial for enhancing the stability and emittance of particle accelerators while extending their lifespan.This paper explores the application of laser-assisted water jet technology in constructing high-quality micro-trap structures on 316L stainless steel,a key material in accelerator manufacturing.The study systematically analyzes the impact of various parameters such as laser repetition frequency,pulse duration,average power,water jet pressure,repeat times,nozzle offset,focal position,offset distance between grooves,and processing speed on the surface morphology of stainless steel.The findings reveal that micro-groove depth increases with higher laser power but decreases with increasing water jet pressure and processing speed.Interestingly,repeat times have minimal effect on depth.On the other hand,micro-groove width increases with higher laser power and repeat times but decreases with processing speed.By optimizing these parameters,the researchers achieved high-quality pound sign-shaped trap structure with consistent dimensions.We tested the secondary electron emission coefficient of the"well"structure.The coefficient is reduced by 0.5 at most compared to before processing,effectively suppressing secondary electron emission.These results offer indispensable insights for the fabrication of micro-trap structures on material surfaces.Laser-assisted water jet technology demonstrates considerable potential in mitigating SEE on metal surfaces.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

Clinical efficacy of femtosecond laser-assisted phacoemulsification in diabetic cataract patients

BACKGROUND Diabetic patients with cataracts encounter specific difficulties during cataract surgery due to alterations in microcirculation,blood supply,metabolism,and the microenvironment.Traditional phacoemulsification may not fully tackle these issues,especially in instances with substantial preoperative astigmatism.The utilization of femtosecond laser-assisted phacoemulsification,in conjunction with Toric intraocular lens(IOL)implantation,offers a potentially more efficient strategy.This research seeks to evaluate the efficacy and possible complications of this approach in diabetic cataract patients.AIM To investigate the clinical efficacy and complications of femtosecond laser-assisted phacoemulsification combined with Toric IOL implantation in diabetic cataract patients,comparing it with traditional phacoemulsification methods.METHODS This retrospective study enrolled 120 patients with diabetes cataract from May 2019 to May 2021.The patients were divided into two groups:the control group underwent traditional phacoemulsification and Toric IOL implantation,while the treatment group received Len Sx femtosecond laser-assisted treatment.Outcome measures included naked eye vision,astigmatism,high-level ocular phase difference detection,clinical efficacy,and complication.RESULTS There were no significant preoperative differences in astigmatism or naked eyesight between the two groups.However,postoperative improvements were observed in both groups,with the treatment group showing greater enhancements in naked eye vision and astigmatism six months after the procedure.High-level corneal phase difference tests also indicated significant differences in favor of the treatment group.CONCLUSION This study suggests that femtosecond laser-assisted phacoemulsification combined with Toric IOL implantation appears to be more effective in enhancing postoperative vision in diabetic cataract patients compared to traditional methods offering valuable insights for clinical practice.

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.

Field-assisted machining of difficult-to-machine materials

Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining technologies often struggle to achieve ultra-precision with DMMs resulting from poor surface quality and low processing efficiency.In recent years,field-assisted machining (FAM) technology has emerged as a new generation of machining technology based on innovative principles such as laser heating,tool vibration,magnetic magnetization,and plasma modification,providing a new solution for improving the machinability of DMMs.This technology not only addresses these limitations of traditional machining methods,but also has become a hot topic of research in the domain of ultra-precision machining of DMMs.Many new methods and principles have been introduced and investigated one after another,yet few studies have presented a comprehensive analysis and summarization.To fill this gap and understand the development trend of FAM,this study provides an important overview of FAM,covering different assisted machining methods,application effects,mechanism analysis,and equipment design.The current deficiencies and future challenges of FAM are summarized to lay the foundation for the further development of multi-field hybrid assisted and intelligent FAM technologies.

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning

Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Machine learning in solid organ transplantation:Charting the evolving landscape

BACKGROUND Machine learning(ML),a major branch of artificial intelligence,has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation.ML provides revolutionary opportunities in areas such as donorrecipient matching,post-transplant monitoring,and patient care by automatically analyzing large amounts of data,identifying patterns,and forecasting outcomes.AIM To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.METHODS On July 18,a thorough search strategy was used with the Web of Science database.ML and transplantation-related keywords were utilized.With the aid of the VOS viewer application,the identified articles were subjected to bibliometric variable analysis in order to determine publication counts,citation counts,contributing countries,and institutions,among other factors.RESULTS Of the 529 articles that were first identified,427 were deemed relevant for bibliometric analysis.A surge in publications was observed over the last four years,especially after 2018,signifying growing interest in this area.With 209 publications,the United States emerged as the top contributor.Notably,the"Journal of Heart and Lung Transplantation"and the"American Journal of Transplantation"emerged as the leading journals,publishing the highest number of relevant articles.Frequent keyword searches revealed that patient survival,mortality,outcomes,allocation,and risk assessment were significant themes of focus.CONCLUSION The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation.This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes.Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

An Investigation of Laser Assisted Machining of Al_2O_3 Particle Reinforced Aluminum Matrix Composite

The Al 2O 3 particles reinforced aluminum matrix composite (Al 2O 3p/Al) are more and more widely used for their excellent physical and chemical properties. However, their poor machinability leads to severe tool wear and bad machined surface. In this paper laser assisted machining is adopted in machining Al 2O 3p/Al composite and good result was obtained. The result of experiment shows in machining Al 2O 3p/Al composites the cutting force is reduced in 30%～50%, the tool wear is reduced in 20%～30% and machined surface quality is improved in laser assisted machining as compared with conventional cutting. The physical model of the cutting process is set up and explains the reason why the cutting forced are reduced. The state of the particles is the main influence of the change. When the material of cutting zone is heating by laser, the aluminum matrix becomes softer and easier in plastic deformation, which leads to the reduction of the pushing force from the tool to the machined surface. The soften aluminum matrix is more easy to be squeezed out from the machined surface, and it leads the concentration of the Al 2O 3 particles in the surface layer of machined surface. The softening effect of laser heating on aluminum matrix reduces the pushing forces of the Al 2O 3 particles on the clearance face of cutting tool, which is just the reason for the severe cutting tool wear in conventional machining of Al 2O 3p/Al composite. Because the Al 2O 3 particles were pushed in during the cutting process, the particles increased in the surface layer. Because of the difference in thermal conductivity and thermal expansion between the Al-matrix and Al 2O 3 particle, residual stress is changed in the matrix after machining due to the extrusion of the tool, deformation of the matrix and displacement of the Al 2O 3 particle in the matrix. Temperature gradient comes into the cutting zone and the work-piece surface layer, it will lead to the increase of thermal stress and misfit dislocation in the matrix. The residual stress is compressive in the laser assisted hot cutting surface, the compressive stress is nearly triple times than that in the conventional cutting surface. Some analysis on the mechanism of laser heat assisted machining of Al 2O 3p/Al composite is given in the paper too.

Non-traditional Machining Techniques for Fabricating Metal Aerospace Filters

Thanks to recent advances in manufacturing technology, aerospace system designers have many more options to fabricate high-quality, low-weight, high-capacity, cost-effective filters. Aside from traditional methods such as stamping, drilling and milling, many new approaches have been widely used in filter-manufacturing practices on account of their increased processing abilities. How- ever, the restrictions on costs, the need for studying under stricter conditions such as in aggressive fluids, the complicity in design, the workability of materials, and others have made it difficult to choose a satisfactory method from the newly developed processes, such as, photochemical machining （PCM）, photo electroforming （PEF） and laser beam machining （LBM） to produce small, inexpensive, lightweight aerospace filters. This article appraises the technical and economical viability of PCM, PEF, and LBM to help engineers choose the fittest approach to turn out aerospace filters.

Challenges in laser-assisted milling of titanium alloys

Several detailed studies have comprehensively investigated the benefits and limitations of laser-assisted machining(LAM)of titanium alloys.These studies have highlighted the positive impact of the application of laser preheating on reducing cutting forces and improving productivity but have also identified the detrimental effect of LAM on tool life.This paper seeks to evaluate a series of the most common cutting tools with different coating types used in the machining of titanium alloys to identify whether coating type has a dramatic effect on the dominant tool wear mechanisms active during the process.The findings provide a clear illustration that the challenges facing the application of LAM are associated with the development of new types of cutting tools which are not subjected to the diffusion-controlled wear processes that dominate the performance of current cutting tools.

Aviation-oriented Micromachining Technology—Micro-ECM in Pure Water

This article proposes a precise and ecofriendly micromachining technology for aerospace application called electrochemical machining in pure water （PW-ECM）. On the basis of the principles of water dissociation, a series of test setups and tests are devised and performed under different conditions. These tests explain the need for technological conditions realizing PW-ECM, and further explore the technological principles. The results from the tests demonstrate a successful removal of electrolytic slime by means of ultrasonic vibration of the workpiece. To ensure the stability and reliability of PW-ECM process, a new combined machining method of PW-ECM assisted with ultrasonic vibration （PW-ECM/USV） is devised. Trilateral and square cavities and holes as well as a group of English alphabets are worked out on a stainless steel plate. It is confirmed that PW-ECM will be probably an efficient new aviation precision machining method.

MACHINING OF METAL MATRIX COMPOSITES

Two types of aluminium-based composites reinforced respectively with 20 vol short fibre alumina and with a hybrid of 15 vol SiC particle and 5 vol short alumina fibre are machined with different tool materials:cemented carbide,ceramic,cubic boron nitride(CBN)and polycrystalline diamond(PCD).The analysis on tool wear shows that the various tool materials exhibite different tool wear behaviours,and the tool wear mechanisma are discussed.Apparently,PCD tools do not necessarily guarantee dimensional stability but they can provide the most economic means for machining all sorts of composites.Consequently,a suitable tool material is suggested for machining each metal matrix composite(MMC) from the standpoints of tool wear and machined surface finish.

INTERNET-BASED MACHINING PARAMETER OPTIMIZATION AND MANAGEMENT SYSTEM FOR HIGH-SPEED MACHINING

The theory and its method of machining parameter optimization for high-speed machining are studied. The machining data collected from workshops, labs and references are analyzed. An optimization method based on the genetic algorithm (GA) is investigated. Its calculation speed is faster than that of traditional optimization methods, and it is suitable for the machining parameter optimization in the automatic manufacturing system. Based on the theoretical studies, a system of machining parameter management and optimization is developed. The system can improve productivity of the high-speed machining centers.

Machining Line Planner输出STEP-NC数控程序的研究

介绍了STEP-NC的概念、数据模型及其结构特点,然后通过对比MLP(Machining Line Planner)和STEP-NC数控程序对特征和操作的不同定义方法,分析了在MLP中特征及加工工艺与STEP-NC的对应关系,探讨了在MLP中实现输出STEP-NC格式的零件加工程序的方法。