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.

Thermal-Mechanical Efect and Removal Mechanism of Ti-6Al-4V During Laser-Assisted Grinding

The low density and high corrosion resistance of titanium alloy make it a material with various applications in the aerospace industry. However, because of its high specifc strength and poor thermal conductivity, there are problems such as high cutting force, poor surface integrity, and high cutting temperature during conventional machining. As an advanced processing method with high efciency and low damage, laser-assisted machining can improve the machinability of titanium alloy. In this study, a picosecond pulse laser-assisted scratching (PPLAS) method considering both the temperature-dependent material properties and ultrashort pulse laser’s characteristics is frst proposed. Then, the efects of laser power, scratching depth, and scratching speed on the distribution of stress and temperature feld are investigated by simulation. Next, PPLAS experiments are conducted to verify the correctness of the simulation and reveal the removal behavior at various combinations of laser power and scratching depths. Finally, combined with simulated and experimental results, the removal mechanism under the two machining methods is illustrated. Compared with conventional scratching (CS), the tangential grinding force is reduced by more than 60% and the material removal degree is up to 0.948 during PPLAS, while the material removal is still primarily in the form of plastic removal. Grinding debris in CS takes the form of stacked fakes with a “fsh scale” surface, whereas it takes the form of broken serrations in PPLAS. This research can provide important guidance for titanium alloy grinding with high surface quality and low surface damage.

Effect of tool geometry on ultraprecision machining of soft-brittle materials:a comprehensive review

Brittle materials are widely used for producing important components in the industry of optics,optoelectronics,and semiconductors.Ultraprecision machining of brittle materials with high surface quality and surface integrity helps improve the functional performance and lifespan of the components.According to their hardness,brittle materials can be roughly divided into hard-brittle and soft-brittle.Although there have been some literature reviews for ultraprecision machining of hard-brittle materials,up to date,very few review papers are available that focus on the processing of soft-brittle materials.Due to the‘soft’and‘brittle’properties,this group of materials has unique machining characteristics.This paper presents a comprehensive overview of recent advances in ultraprecision machining of soft-brittle materials.Critical aspects of machining mechanisms,such as chip formation,surface topography,and subsurface damage for different machining methods,including diamond turning,micro end milling,ultraprecision grinding,and micro/nano burnishing,are compared in terms of tool-workpiece interaction.The effects of tool geometries on the machining characteristics of soft-brittle materials are systematically analyzed,and dominating factors are sorted out.Problems and challenges in the engineering applications are identified,and solutions/guidelines for future R&D are provided.

Laser machining fundamentals:micro,nano,atomic and close-to-atomic scales

With the rapid development in advanced industries,such as microelectronics and optics sectors,the functional feature size of devises/components has been decreasing from micro to nanometric,and even ACS for higher performance,smaller volume and lower energy consumption.By this time,a great many quantum structures are proposed,with not only an extreme scale of several or even single atom,but also a nearly ideal lattice structure with no material defect.It is almost no doubt that such structures play critical role in the next generation products,which shows an urgent demand for the ACSM.Laser machining is one of the most important approaches widely used in engineering and scientific research.It is high-efficient and applicable for most kinds of materials.Moreover,the processing scale covers a huge range from millimeters to nanometers,and has already touched the atomic level.Laser–material interaction mechanism,as the foundation of laser machining,determines the machining accuracy and surface quality.It becomes much more sophisticated and dominant with a decrease in processing scale,which is systematically reviewed in this article.In general,the mechanisms of laser-induced material removal are classified into ablation,CE and atomic desorption,with a decrease in the scale from above microns to angstroms.The effects of processing parameters on both fundamental material response and machined surface quality are discussed,as well as theoretical methods to simulate and understand the underlying mechanisms.Examples at nanometric to atomic scale are provided,which demonstrate the capability of laser machining in achieving the ultimate precision and becoming a promising approach to ACSM.

Comparison of visual performance with iTrace analyzer following femtosecond laser-assisted cataract surgery with bilateral implantation of two different trifocal intraocular lenses

AIM:To compare the postoperative binocular visual performance with an iTrace analyzer following femtosecond laser-assisted cataract surgery(FLACS)combined with bilateral implantation of two different types of diffractive trifocal intraocular lenses(IOL).METHODS:During this retrospective observational study,patients who received bilateral FLACS combined with implantation of two different types of diffractive trifocal IOLs were evaluated.According to the IOLs’different types and design,the patients were divided into AT LISA tri839MP group(tri839 group)and AcrySof PanOptix TFNT00 group(TFNT group).Study parameters included preoperative and postoperative uncorrected distance visual acuity(UDVA)at 5 m,uncorrected near visual acuity(UNVA)at 30 cm and 40 cm,uncorrected intermediate visual acuity(UIVA)at 60 cm and 80 cm,postoperative refractive status,objective visual qualities and total high order aberrations(HOAs)postoperatively.The postoperative complications were also recorded.RESULTS:Totally 56 eyes of 28 patients(tri839 group,n=26;TFNT group,n=30)were included.Preoperative baseline characteristics between groups were not statistically significantly different.UDVA was not significantly different between groups except for 1wk follow-up due to the postoperative corneal edema.TFNT group showed statistically significant better UNIA at 60 cm than tri839 group at the 1wk(0.05±0.19 vs 0.15±0.10 logMAR,P=0.013),1mo(0.05±0.12 vs 0.15±0.09 logMAR,P=0.001)and 3mo(0.04±0.12 vs 0.15±0.11 logMAR,P=0.001)follow-up,while tri839 group showed statistically significant better UNIA at 80 cm than TFNT group at the 1d(0.14±0.15 vs 0.20±0.14 logMAR,P=0.041)and 1mo(0.09±0.07 vs 0.14±0.10 logMAR,P=0.042)follow-up.Postoperative refractive status showed stable at every visit.Modulated transfer function(MTF)values and strehl ratio(SR)values were improved and HOAs were lower significantly after surgery.CONCLUSION:FLACS with bilateral implantations of both tri839 and TFNT00 can achieve satisfactory natural whole-course vision,high postoperative refractive stability and good visual quality but without significantly difference.iTrace aberration instrument can accurately evaluate the visual quality under different status.

Failure mode change and material damage with varied machining speeds:a review

High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band(ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM.

Assessment of Lubrication Property and Machining Performance of Nanofluid Composite Electrostatic Spraying(NCES)Using Different Types of Vegetable Oils as Base Fluids of External Fluid

The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic spraying(NCES)can enhance the performance of MQL more comprehensively.However,it is largely influenced by the base fluid of external fluid.In this paper,the lubrication property and machining performance of NCES with different types of vegetable oils(castor,palm,soybean,rapeseed,and LB2000 oil)as the base fluids of external fluid were compared and evaluated by friction and milling tests under different flow ratios of external and internal fluids.The spraying current and electrowetting angle were tested to analyze the influence of vegetable oil type as the base fluid of external fluid on NCES performances.The friction test results show that relative to NCES with other vegetable oils as the base fluids of external fluid,NCES with LB2000 as the base fluid of external fluid reduced the friction coefficient and wear loss by 9.4%-27.7%and 7.6%-26.5%,respectively.The milling test results display that the milling force and milling temperature for NCES with LB2000 as the base fluid of external fluid were 1.4%-13.2%and 3.6%-11.2%lower than those for NCES with other vegetable oils as the base fluids of external fluid,respectively.When LB2000/multi-walled carbon nanotube(MWCNT)water-based nanofluid was used as the external/internal fluid and the flow ratio of external and internal fluids was 2:1,NCES showed the best milling performance.This study provides theoretical and technical support for the selection of the base fluid of NCES external fluid.

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

Machine learning for predicting the outcome of terminal ballistics events

Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Laser-assisted Simulation of Dose Rate Effects of Wide Band Gap Semiconductor Devices

Laser-assisted simulation technique has played a crucial role in the investigation of dose rate effects of silicon-based devices and integrated circuits,due to its exceptional advantages in terms of flexibility,safety,convenience,and precision.In recent years,wide band gap materials,known for their strong bonding and high ionization energy,have gained increasing attention from researchers and hold significant promise for extensive applications in specialized environments.Consequently,there is a growing need for comprehensive research on the dose rate effects of wide band gap materials.In response to this need,the use of laser-assisted simulation technology has emerged as a promising approach,offering an effective means to assess the efficacy of investigating these materials and devices.This paper focused on investigating the feasibility of laser-assisted simulation to study the dose rate effects of wide band gap semiconductor devices.Theoretical conversion factors for laser-assisted simulation of dose rate effects of GaN-based and SiC-based devices were been provided.Moreover,to validate the accuracy of the conversion factors,pulsed laser and dose rate experiments were conducted on GaN-based and SiC-based PIN diodes.The results demonstrate that pulsed laser radiation andγ-ray radiation can produce highly similar photocurrent responses in GaN-based and SiC-based PIN diodes,with correlation coefficients of 0.98 and 0.974,respectively.This finding reaffirms the effectiveness of laser-assisted simulation technology,making it a valuable complement in studying the dose rate effects of wide band gap semiconductor devices.

Machine learning with active pharmaceutical ingredient/polymer interaction mechanism:Prediction for complex phase behaviors of pharmaceuticals and formulations

The high throughput prediction of the thermodynamic phase behavior of active pharmaceutical ingredients(APIs)with pharmaceutically relevant excipients remains a major scientific challenge in the screening of pharmaceutical formulations.In this work,a developed machine-learning model efficiently predicts the solubility of APIs in polymers by learning the phase equilibrium principle and using a few molecular descriptors.Under the few-shot learning framework,thermodynamic theory(perturbed-chain statistical associating fluid theory)was used for data augmentation,and computational chemistry was applied for molecular descriptors'screening.The results showed that the developed machine-learning model can predict the API-polymer phase diagram accurately,broaden the solubility data of APIs in polymers,and reproduce the relationship between API solubility and the interaction mechanisms between API and polymer successfully,which provided efficient guidance for the development of pharmaceutical formulations.

Machine learning applications on lunar meteorite minerals:From classification to mechanical properties prediction

Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value,nondestructive testing methods are essential.This translates into the application of microscale rock mechanics experiments and scanning electron microscopy for surface composition analysis.This study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical properties of DHOFAR 1084,JAH 838,and NWA 11444 lunar meteorites based solely on their atomic percentage compositions.Leveraging a prior-data fitted network model,we achieved near-perfect classification scores for meteorites,mineral groups,and individual minerals.The regressor models,notably the KNeighbor model,provided an outstanding estimate of the mechanical properties—previously measured by nanoindentation tests—such as hardness,reduced Young’s modulus,and elastic recovery.Further considerations on the nature and physical properties of the minerals forming these meteorites,including porosity,crystal orientation,or shock degree,are essential for refining predictions.Our findings underscore the potential of Machine Learning in enhancing mineral identification and mechanical property estimation in lunar exploration,which pave the way for new advancements and quick assessments in extraterrestrial mineral mining,processing,and research.