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.

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.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in 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.

Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder(MDD).However,few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity(FC).AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents.METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study.Using resting-state functional magnetic resonance imaging,the FC was compared between the adolescents with MDD and the healthy controls,with the bilateral amygdala serving as the seed point,followed by statistical analysis of the results.The support vector machine(SVM)method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD.RESULTS Compared to the controls and using the bilateral amygdala as the region of interest,patients with MDD showed significantly lower FC values in the left inferior temporal gyrus,bilateral calcarine,right lingual gyrus,and left superior occipital gyrus.However,there was an increase in the FC value in Vermis-10.The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls,achieving a diagnostic accuracy of 83.91%,sensitivity of 79.55%,specificity of 88.37%,and an area under the curve of 67.65%.CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

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.

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.

PRECISION OF HSK TOOLING SYSTEM IN HIGH SPEED MACHINING

Based on the theory of elastic mechanics and material mechanics, the orientation precision of the hohl schaft kegel（HSK） tooling system in static and dynamic states is theoretically and experimentally studied. The relation between the clamping force and the shank taper is obtained. And a proper clamping force is found to be essential to assure the axial and radial orientation precisions of the HSK tooling system in high speed machining （HSM）. Analytical results show that the reason why the HSK tooling system can keep high precision at the high rotational speed is that the actual axial clamping force keeps the two surfaces of the shank and the spindle in contact all the time.

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 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。

Optimization of micro milling electrical discharge machining of Inconel 718 by Grey-Taguchi method

The optimization of micro milling electrical discharge machining（EDM） process parameters of Inconel 718 alloy to achieve multiple performance characteristics such as low electrode wear,high material removal rate and low working gap was investigated by the Grey-Taguchi method.The influences of peak current,pulse on-time,pulse off-time and spark gap on electrode wear（EW）,material removal rate（MRR） and working gap（WG） in the micro milling electrical discharge machining of Inconel 718 were analyzed.The experimental results show that the electrode wear decreases from 5.6×10-9 to 5.2×10-9 mm3/min,the material removal rate increases from 0.47×10-8 to 1.68×10-8 mm3/min,and the working gap decreases from 1.27 to 1.19 μm under optimal micro milling electrical discharge machining process parameters.Hence,it is clearly shown that multiple performance characteristics can be improved by using the Grey-Taguchi method.

A NEW ALGORITHM BASED ON TRIANGULATION FOR 5 AXIS MACHINING OF SCULPTURED SURFACES

NC machining path of sculptured surfaces in CAD/CAM system plays an important role on manufacture. This paper describes a new algorithm for 5 axis machining of sculptured surfaces and the algorithm is interference free. The approach includes: (1) the tesselation of the parametric surfaces into triangles; (2) building topological relations among triangles;(3) 5 axis tool path generation; (4) interference detection and tool position correction.

PLANNING METHOD OF TOOL ORIENTATION IN FIVE-AXIS NC MACHINING

The planning method of tool orientation in the five-axis NC machining is studied. The problem of the existing method is analyzed and a new method for generating the global smoothing tool orientation is proposed by introducing the key frame idea in the animation-making. According to the feature of the part, several key tool orientations are set without interference between the tool and the part. Then, these key tool orientations are inter- polated by the spline function. By mapping the surface parameter to the spline parameter, the spline function value is obtained and taken as the tool orientation when generating the CL file. The machining result shows that the proposed method realizes the global smoothing of the tool orientation and the continuity of the rotational speed and the rotational acceleration. It also avoids the shake of the machine tool and improves the machining quality.

Bioinspired Passive Tactile Sensors Enabled by Reversible Polarization of Conjugated Polymers

Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.

IMPROVEMENT OF MACHINING ACCURACY OF CANTILEVER BORING BAR SYSTEM USING PIEZOELECTRIC ACTUATOR

This paper is concerned with the work involved in improving the machining accuracy of a cantilever boring bar by on line compensation with a piezoelectric actuator. A boring bar is made into lever structure, with strain gauges attached to the bar for measuring its force induced deflections. The piezoelectric actuator is employed to compensate the deflections of the boring bar for accuracy improvement. Due to the mechanical advantage of the structure, the boring bar can be made into smaller size. The diameter of the bar implemented is 10 mm and the ratio of length to diameter (L/D) is larger than 8. It is found that the machining accuracy is improved considerably by using the piezoelectric actuator compensation system.

Recognition and quality mapping of traditional herbal drugs:way forward towards artificial intelligence

The use of traditional herbal drugs derived from natural sources is on the rise due to their minimal side effects and numerous health benefits.However,a major limitation is the lack of standardized knowledge for identifying and mapping the quality of these herbal medicines.This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs.It focuses on feature extraction methods,image processing techniques,and the preparation of herbal images for compatibility with machine learning models.The article discusses commonly used image processing tools such as normalization,slicing,cropping,and augmentation to prepare images for artificial intelligence-based models.It also provides an overview of global herbal image databases and the models employed for herbal plant/drug identification.Readers will gain a comprehensive understanding of the potential application of various machine learning models,including artificial neural networks and convolutional neural networks.The article delves into suitable validation parameters like true positive rates,accuracy,precision,and more for the development of artificial intelligence-based identification and authentication techniques for herbal drugs.This article offers valuable insights and a conclusive platform for the further exploration of artificial intelligence in the field of herbal drugs,paving the way for smarter identification and authentication methods.

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.

A Definition Formalism of Machining Features for DFM

Benefiting from advances in feature technology for design and manufacture can not be expected before a formal methodology is established. This paper makes attempt to establish a definition formalism of machining features in design for manufacturability from two aspects: formal definition and manufacturability analysis. Some definitions for machining feature based upon the selection and sequencing of material removal operations for component in accordance with the design geometry are presented and a framework of feature based design for manufacturability is outlined correspondingly. The proposed scheme contributes to several aspects of feature based CAD/CAM integration, especially to encourage potentially a more generic approach to the automation of design.