Enhanced prediction of anisotropic deformation behavior using machine learning with data augmentation

Mg alloys possess an inherent plastic anisotropy owing to the selective activation of deformation mechanisms depending on the loading condition.This characteristic results in a diverse range of flow curves that vary with a deformation condition.This study proposes a novel approach for accurately predicting an anisotropic deformation behavior of wrought Mg alloys using machine learning(ML)with data augmentation.The developed model combines four key strategies from data science:learning the entire flow curves,generative adversarial networks(GAN),algorithm-driven hyperparameter tuning,and gated recurrent unit(GRU)architecture.The proposed model,namely GAN-aided GRU,was extensively evaluated for various predictive scenarios,such as interpolation,extrapolation,and a limited dataset size.The model exhibited significant predictability and improved generalizability for estimating the anisotropic compressive behavior of ZK60 Mg alloys under 11 annealing conditions and for three loading directions.The GAN-aided GRU results were superior to those of previous ML models and constitutive equations.The superior performance was attributed to hyperparameter optimization,GAN-based data augmentation,and the inherent predictivity of the GRU for extrapolation.As a first attempt to employ ML techniques other than artificial neural networks,this study proposes a novel perspective on predicting the anisotropic deformation behaviors of wrought Mg alloys.

IMPROVE THE KINETIC PERFORMANCE OF THE PUMP CONTROLLED CLAMPING UNIT IN PLASTIC INJECTION MOLDING MACHINE WITH ADAPTIVE CONTROL STRATEGY

The kinetic characteristics of the clamping unit of plastic injection molding machine that is controlled by close loop with newly developed double speed variable pump unit are investigated. Considering the wide variation of the cylinder equivalent mass caused by the transmission ratio of clamping unit and the severe instantaneous impact force acted on the cylinder during the mold closing and opening process, an adaptive control principle of parameter and structure is proposed to improve its kinetic performance. The adaptive correlation between the acceleration feedback gain and the variable mass is derived. The pressure differential feedback is introduced to improve the dynamic performance in the case of small inertia and heavy impact load. The adaptation of sum pressure to load is used to reduce the energy loss of the system. The research results are verified by the simulation and experiment, The investigation method and the conclusions are also suitable for the differential cylinder system controlled by the traditional servo pump unit.

Machine Learning Models of Plastic Flow Based on Representation Theory

We use machine learning(ML)to infer stress and plastic flow rules using data from representative polycrystalline simulations.In particular,we use so-called deep(multilayer)neural networks(NN)to represent the two response functions.The ML process does not choose appropriate inputs or outputs,rather it is trained on selected inputs and output.Likewise,its discrimination of features is crucially connected to the chosen inputoutput map.Hence,we draw upon classical constitutive modeling to select inputs and enforce well-accepted symmetries and other properties.In the context of the results of numerous simulations,we discuss the design,stability and accuracy of constitutive NNs trained on typical experimental data.With these developments,we enable rapid model building in real-time with experiments,and guide data collection and feature discovery.

New possibility for PET plastic recycling by a tailored hydrolytic enzyme

Plastic waste puts a huge burden on the ecosystem due to the current lack of mature recycling technology.Poly(ethylene terephthalate)(PET)is one of the most produced plastics in the world.Enzymatic decomposition holds the promise of recovering monomers from PET plastic,and the monomers can be used to regenerate new PET products.However,there are still limitations in the activity and thermal stability of the existing PET hydrolases.The recent study by Lu et al.introduced a novel PET hydrolase via machine learning-aided engineering.The obtained PET hydrolase showed excellent activity and thermal stability in the hydrolysis of PET and is capable of directly degrading large amounts of postconsumer PET products.This approach provides an effective method for recycling PET waste and is expected to improve the current state of plastic pollution worldwide.

Quantitative structure-plasticity relationship in metallic glass:A machine learning study

The lack of the long-range order in the atomic structure challenges the identification of the structural defects,akin to dislocations in crystals,which are responsible for predicting plastic events and mechanical failure in metallic glasses(MGs).Although vast structural indicators have been proposed to identify the structural defects,quantitatively gauging the correlations between these proposed indicators based on the undeformed configuration and the plasticity of MGs upon external loads is still lacking.Here,we systematically analyze the ability of these indicators to predict plastic events in a representative MG model using machine learning method.Moreover,we evaluate the influences of coarse graining method and medium-range order on the predictive power.We demonstrate that indicators relevant to the low-frequency vibrational modes reveal the intrinsic structural characteristics of plastic rearrangements.Our work makes an important step towards quantitative assessments of given indicators,and thereby an effective identification of the structural defects in MGs.

Versatile Plastic Bottles Blowing Machine

The versatile plastic bottle blowing machine, developed and produced by the Anhui Tongda Science and Technology Development Corp. under the Sino-foreign joint venture Guobao Group, has been exported to Britain, Iceland, Paraguay, Thailand, Pakistan, Bangladesh and the Commonwealth of Independent States. At an

New Model Die Set for PET Plastic Jet-Moulding Machine

At the China International Food Packing Machinery Exhibition, the new model die set for PET plastic jet-mouldingmachine developed by the Zhejiang Province Taizhou Municipality Huangyan Sanyou Plastics Factory attracted the attention of numerous domestic and foreign clients. They rushed to the stand in great numbers for consultation and talks on ordering. According to the evaluation of the experts concerned, the die set is the most advanced one nationwide for PET plastic jet-moulding machinery.

Understanding microstructure-property relationships of HPDC Al-Si alloy based on machine learning and crystal plasticity simulation

Al-Si alloys manufactured via high-pressure die casting(HPDC)are suitable for a wide range of applications.However,the heterogeneous microstructure and unpredictable pore distribution of Al-Si high-pressure die castings result in significant variations in the mechanical properties,thus leading to a complicated microstructure-property relationship that is difficult to capture.Hence,a computational framework incorporating machine learning and crystal plasticity method is proposed.This framework aims to provide a systematic and comprehensive understanding of this relationship and enable the rapid prediction of macroscopic mechanical properties based on the microstructure.Firstly,we select eight variables that can effectively characterize the microstructural features and then obtain their statistical information.Subsequently,based on 160 samples obtained via the Latin hypercube sampling method,representative volume elements are constructed,and the crystal plasticity fast Fourier transformation method is executed to obtain the macroscopic mechanical properties.Next,the yield strength,elastic modulus,strength coefficient,and strain-hardening exponent are used to characterize the stress-strain curve,and Gaussian process regression models and microstructural variables are developed.Finally,sensitivity and univariate analyses based on these machine-learning models are performed to obtain insights into the microstructure-property relationships of the HPDC Al-Si alloy.The results show that the Gaussian process regression models exhibit high accuracy(R2 greater than 0.84),thus confirming the viability of the proposed method.The results of sensitivity analysis indicate that the pore size exerts the most significant effect on the mechanical properties.Furthermore,the proposed framework can not only be transferred to other alloys but also be employed for material design.

Improved prediction of clay soil expansion using machine learning algorithms and meta-heuristic dichotomous ensemble classifiers

Soil swelling-related disaster is considered as one of the most devastating geo-hazards in modern history.Hence,proper determination of a soil’s ability to expand is very vital for achieving a secure and safe ground for infrastructures.Accordingly,this study has provided a novel and intelligent approach that enables an improved estimation of swelling by using kernelised machines(Bayesian linear regression(BLR)&bayes point machine(BPM)support vector machine(SVM)and deep-support vector machine(D-SVM));(multiple linear regressor(REG),logistic regressor(LR)and artificial neural network(ANN)),tree-based algorithms such as decision forest(RDF)&boosted trees(BDT).Also,and for the first time,meta-heuristic classifiers incorporating the techniques of voting(VE)and stacking(SE)were utilised.Different independent scenarios of explanatory features’combination that influence soil behaviour in swelling were investigated.Preliminary results indicated BLR as possessing the highest amount of deviation from the predictor variable(the actual swell-strain).REG and BLR performed slightly better than ANN while the meta-heuristic learners(VE and SE)produced the best overall performance(greatest R2 value of 0.94 and RMSE of 0.06%exhibited by VE).CEC,plasticity index and moisture content were the features considered to have the highest level of importance.Kernelized binary classifiers(SVM,D-SVM and BPM)gave better accuracy(average accuracy and recall rate of 0.93 and 0.60)compared to ANN,LR and RDF.Sensitivity-driven diagnostic test indicated that the meta-heuristic models’best performance occurred when ML training was conducted using k-fold validation technique.Finally,it is recommended that the concepts developed herein be deployed during the preliminary phases of a geotechnical or geological site characterisation by using the best performing meta-heuristic models via their background coding resource.

Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics

This study has provided an approach to classify soil using machine learning.Multiclass elements of stand-alone machine learning algorithms(i.e.logistic regression(LR)and artificial neural network(ANN)),decision tree ensembles(i.e.decision forest(DF)and decision jungle(DJ)),and meta-ensemble models(i.e.stacking ensemble(SE)and voting ensemble(VE))were used to classify soils based on their intrinsic physico-chemical properties.Also,the multiclass prediction was carried out across multiple cross-validation(CV)methods,i.e.train validation split(TVS),k-fold cross-validation(KFCV),and Monte Carlo cross-validation(MCCV).Results indicated that the soils’clay fraction(CF)had the most influence on the multiclass prediction of natural soils’plasticity while specific surface and carbonate content(CC)possessed the least within the nature of the dataset used in this study.Stand-alone machine learning models(LR and ANN)produced relatively less accurate predictive performance(accuracy of 0.45,average precision of 0.5,and average recall of 0.44)compared to tree-based models(accuracy of 0.68,average precision of 0.71,and recall rate of 0.68),while the meta-ensembles(SE and VE)outperformed(accuracy of 0.75,average precision of 0.74,and average recall rate of 0.72)all the models utilised for multiclass classification.Sensitivity analysis of the meta-ensembles proved their capacities to discriminate between soil classes across the methods of CV considered.Machine learning training and validation using MCCV and KFCV methods enabled better prediction while also ensuring that the dataset was not overfitted by the machine learning models.Further confirmation of this phenomenon was depicted by the continuous rise of the cumulative lift curve(LC)of the best performing models when using the MCCV technique.Overall,this study demonstrated that soil’s physico-chemical properties do have a direct influence on plastic behaviour and,therefore,can be relied upon to classify soils.

Life Can’t Be Any Easier than This—Introduction of the Portable and Disposable V.A.C. Machines

The application of controlled levels of negative pressure on to a wound has been shown to accelerate evacuation of dead cells, debris and fluid which eventually encourages wound healing in a verity of surgical wounds. Vacuum Assisted Closure (V.A.C.) therapy—KCI Medical Limited, the terminology by which this is widely known, became popular, especially among the plastic surgery professionals in America and soon gained recognition worldwide. It is now widely used in the UK to manage and assist healing in a wide variety of wounds. Although KCI’s V.A.C. machines were the only ones on the market for a number of years, several wound management companies have now brought out their own machines and these are now known collectively as topical negative pressure therapy (TNPT). Traditional TNPT is often considered a relatively costly procedure. It is often used in patients with large wounds to facilitate dressing management and promote rapid cleaning and granulation. This may also allow them to be discharged to the community when they would otherwise remain inpatients, thereby saving bed days. Capital purchase of the machines is expensive and hospitals often rent or lease them on a short or long term basis. This can lead to difficulties in arranging the finances for discharge to the community. Subsequent dressing changes (recommended every 48 - 72 hrs) also incur high costs and involvement of the trained medical or nursing staff. As we all know;“Need is the mother of invention”. The disposable TNPT machine (V.A.C. ViaTM KCI Medical Ltd) has been introduced to help to solve these problems. It is a single use machine, inclusive of a dressing and canister and available off the shelf. It is very cost effective, easy to use and is used for small to moderate sized wounds. Senior author is using this machine which excellent results and illustrated the use of this machine with pictures in this paper.

Soil moisture content as a predictor of soil disturbance caused by wheeled forest harvesting machines on soils of the Western Carpathians

Limiting surface soil disturbance caused by forest harvesting machines is an important task and is influenced by the selection of efficient and reliable predictors of such disturbance. Our objective was to determine whether soil moisture content affects soil load bearing capacity and the formation of ruts. Measurements were conducted in six forest stands where various machines operated. We measured the formation of ruts along skid trails in connection with varying soil moisture content. Soil moisture content was determined through the gravimetric sampling method. Our results showed that severe（rut depth16–25 cm） to very severe disturbance（rut depth [26 cm）occurred in forest stands where the instantaneous soil moisture exceeded its plasticity limits defined through Atterberg limits. Atterberg limits of soil plasticity ranged from 26 to 32 % in individual stands. Regression and correlation analysis confirmed a moderately strong relationship（R = 0.52; p ／ 0.05） between soil moisture content and average rut depth. This confirmed that soil moisture is a suitable and effective predictor of soil disturbance.

Analysis and correction of the machining errors of small plastic helical gears by ball-end milling

Many small-size precise plastic helical involutes gears are used in electrical appliances to transmit rotary movements con- tinuously and smoothly. Ball-end milling is an effective method for trial manufacture or small batch production of this type of gear, but the precision of the gear is usually low. In this research, the main sources of the errors of the gear, machining errors of the tooth profile and trace of the gear obtained were analyzed. The correction amounts for these errors are then determined by using a CNC gear tester. They are used to generate a new 3D-CAD model for gear machining with better nrecision.

Computer aided design of free-machinability prehardened mold steel for plastic

In order to improve the machinability but not to impair other properties of the prehardened mold steel for plastic, the composition was designed by application of Thermo-Calc software package to regulate the type of non-metallic inclusion formed in the steel. The regulated non-metallic inclusion type was also observed by SEM and EDX. Then the machinability assessment of the steel with designed composition under different conditions was studied by the measurement of tool wear amount and cutting force. The results show that the composition of free cutting elements adding to mold steel for plastic can be optimized to obtain proper type of non-metallic inclusion in the aid of Thermo-Calc, compared with the large volume fraction of soft inclusion which is needed for promoting ductile fracture at low cutting speeds, the proper type of inclusion at high cutting speeds is glassy oxide inclusion. All those can be obtained in the present work.

塑性磨料气射流仿真与试验研究

塑性磨料气射流加工(PAJM)是一种先进的减材加工技术,是采用热固性塑性磨料代替传统硬磨料而发展的新型技术,该技术可有效去除表面涂层,且不损伤基材。基材无损主要是通过控制塑性磨料对基材的冲蚀应力,使得塑性磨料的冲蚀应力小于基材纤维的极限强度或纤维与树脂的结合强度。本研究采用有限元仿真和试验相结合的方法,对颗粒速度进行理论分析、计算流体动力学仿真模拟和试验研究,研究不同气体压力下的颗粒速度,计算结果与试验数据吻合较好。结果表明,随着磨料颗粒离开喷嘴,在距离喷嘴出口6.2 dN内(dN为喷嘴内径),颗粒速度增加;相反,距离喷嘴出口6.2 dN外,颗粒速度逐渐减小。当磨料粒径由20~30目变为40~50目时,最大颗粒速度由164.365 m/s增加到228.402 m/s。随着磨料粒径的减小,颗粒速度增加,且发散角增加。相比而言,数值模型能更好的预测塑性磨料的颗粒速度和分布。该研究突出了控制颗粒粒径和支座距离对射流场颗粒速度和发散角的影响。为控制颗粒对基材的冲蚀应力,避免基材损伤提供理论参考。

菠菜播种收割一体机结构设计与有限元分析

针对现有菠菜收割机留根、铲根深浅不一以及播种机撒播效率低、线绳播种成本高等问题,通过理论计算和SolidWorks建模分析,设计了一种犁地深度可调、整株收割、定量等距播种的菠菜播种收割一体机。采用Simulation模块对优化的机架和犁进行有限元分析发现,机架的大部分最大应力点位于机架前端的输料口处,在制造时需提高此处的焊接质量;犁须选用耐腐蚀性好的材料以减少其在腐蚀性土壤环境中的损坏。通过综合比较和有限元分析发现,机架所受最大应力均小于玻璃钢屈服极限,若通过合理成型设计弥补玻璃钢的成形性差等问题,有望使其成为未来农机具中犁制造的优选材料。研究结果可为菠菜播种收割一体机设计和关键部件优化提供一定理论参考。

铜对4Cr16Mo塑料模具钢切削性能的影响

将4Cr16MoCu、4Cr16Mo和SDP136钢在1030℃保温4 h,冷却至室温后回火两次,第一次为540℃×4 h,4Cr16MoCu钢的第二次回火工艺为590℃×3 h,4Cr16Mo和SDP136钢为565℃×3 h,使其硬度达到35~36 HRC。采用扫描电子显微镜检测钢的显微组织,采用数控车床进行切削性能试验。分别采用SY-3F型应变式三维测力传感器、红外测温仪、TR200粗糙度测量仪和超景深显微镜测定切削力、刀尖温度、工件粗糙度和磨损刀具的形貌。结果表明:在相同的切削条件下,铜的添加能延长刀具的使用寿命;切削4Cr16MoCu钢的刀具温度比切削4Cr16Mo钢的刀具温度低50℃,切削试验的前、中期4Cr16Mo钢的切削力比4Cr16MoCu钢大43~94 N,但后期4Cr16MoCu钢的切削力上升至839 N,比SDP136钢大211 N。

基于机器学习和遗传算法的非局部晶体塑性模型参数识别

非局部晶体塑性模型考虑了由非均匀变形引起的位错在空间上的重排,使得其本构模型变得复杂,可调节参数众多,因此采用常规的“试错法”难以准确确定这些参数.虽然遗传算法能够稳健地全局优化解决参数确定问题,但对于非局部晶体塑性模型,其计算成本相对较高.为解决这一问题,提出了一种耦合机器学习模型的遗传算法,以有效降低计算成本.针对含有冷却孔的镍基高温合金的拉伸响应问题,以单拉应力-应变曲线为目标,基于屈服应力和最终应力建立评价公式,使得优化结果与实验尽可能接近.在这一方法中,机器学习模型能够通过非局部晶体塑性模型的参数来预测相应的应力值,从而替代了遗传算法中原本需要的有限元计算过程.为了分析本构模型参数对单拉力学响应的影响,研究采用SHAP框架,并通过有限元结果进行验证.结果表明,通过该方法可以有效获取非局部晶体塑性模型参数,使得参数计算得到的应力-应变响应与实验结果吻合较好.此外, SHAP框架能够提供本构模型参数的重要程度分析,以及对屈服应力和最终应力的影响.

机器学习辅助光谱分析技术在环境微/纳塑料研究中的应用

微/纳塑料在环境中广泛存在,威胁着全球生态系统的平衡和稳定,因此有必要确定和评估微/纳塑料的环境赋存特征、环境行为和效应以及生态毒性效应。然而微/纳塑料具有的低浓度、小尺寸及容易吸附其他物质等特点,为其在复杂环境基质中的分析带来了巨大挑战。大多数分析方法在提供环境样本中微/纳塑料的定性定量信息方面存在成本高、准确性差、时间效率低等问题,而具有无损、高效、操作方便等优点的光谱分析技术可以弥补这些方法的不足。但采集的微/纳塑料光谱信号可能会受到环境样本中复杂成分的背景噪声干扰,亟需采用智能化手段提高分析的准确性和效率。机器学习具有强大的数据处理和自动化分析能力,准确性高、应用性广,适用于复杂光谱数据的分类和解析,将机器学习与光谱分析技术相结合有望成为微/纳塑料分析的可靠方法。首先对常用的机器学习辅助光谱分析技术进行综述,然后系统性地讨论了机器学习辅助光谱分析技术在微/纳塑料的环境赋存特征、环境行为和效应、生态毒理效应等研究中的应用,最后对该技术的发展前景进行了总结和展望。机器学习辅助光谱分析技术有望为环境中微/纳塑料的生态环境健康风险评估和污染防治提供重要数据支持。

残膜机械化回收技术专利信息分析

地膜覆盖技术在农业生产中得到广泛应用,但随着覆膜年限和面积不断增加,残留地膜给环境带来严重污染。目前,机械回收是治理残膜污染的有效技术手段。对现有残膜机械化回收技术专利信息进行归纳总结,从申请时间、申请地区、专利权人类型和技术特征等方面阐述残膜机械化回收技术发展趋势。从机械化角度分析残膜回收技术专利失效的主要原因,对不同申请主体的专利失效现象进行剖析,更好地理解专利失效背后的原因。结合现有专利技术热点和发展趋势,提出促进残膜机械化回收技术的发展建议。