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.

Influence of the configuration of pin on the quality of plastics FSW joints

An investigation on the quality of PVC joints welded by friction stir welding （ FSW ） with different shape of pin was carried out. The results show that when the rotating speed of stir tool is 1 660 r/min and the welding speed is 25 mm/min, the beads welded with upright taper pin are plump and joined well, the average tensile strength of which is 19. 1 MPa （the maximum is 20. 3 MPa）, being 49. 2% of that of parent material. The beads welded with cylindrical pin are also joined rather well plump and smooth, the average tensile strength of which is 17. 6 MPa, being 45.3% of that of parent material. The beads welded with inverted taper and cylindrical screw pin are only partially joined or disjoined. The optimum welding temperature range of PVC is 180 - 190℃. If the temperature beyond 200℃ the material will be burnt. If the temperature is under 170℃ the material will be joined partially or disjoined.

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.

Machine learning facilitated the modeling of plastics hydrothermal pretreatment toward constructing an on-ship marine litter-to-methanol plant

An onboard facility shows promise in efficiently converting floating plastics into valuable products,such as methanol,negating the need for regional transport and land-based treatment.Gasification presents an effective means of processing plastics,requiring their transformation into gasification-compatible feedstock,such as hydrochar.This study explores hydrochar composition modeling,utilizing advanced algorithms and rigorous analyses to unravel the intricacies of elemental composition ratios,identify influential factors,and optimize hydrochar production processes.The investigation begins with decision tree modeling,which successfully captures relationships but encounters overfitting challenges.Nevertheless,the decision tree vote analysis,particularly for the H/C ratio,yielding an impressive R2 of 0.9376.Moreover,the research delves into the economic feasibility of the marine plastics-to-methanol process.Varying payback periods,driven by fluctuating methanol prices observed over a decade(ranging from 3.3 to 7 yr for hydrochar production plants),are revealed.Onboard factories emerge as resilient solutions,capitalizing on marine natural gas resources while striving for near-net-zero emissions.This comprehensive study advances our understanding of hydrochar composition and offers insights into the economic potential of environmentally sustainable marine plastics-to-methanol processes.

SIMULATION ON HEATING PROCESS IN ULTRASONIC WELDING OF PLASTICS

The energy conversion during ultrasonic plastic welding is analyzed on the basis of the theory of vis- coelastic mechanics,.The temperature field and the temperature change of the ABS specimen with ener- gy director during ultrasonic welding is simulated with finite element method(FEM). In the simu- lation process,the melting of the energy energy is also considered. The calculation results are in good agreement with the temperature measurement results, which proves that the simulation results are reli- able.

Effect of ultrasonic vibration on the friction stir weld quality of aluminium alloy

A novel variant of friction stir welding process, referred as ultrasonic vibration enhanced friction stir welding, is developed to transmit ultrasonic vibration energy directly into the localized area of the workpiece near and ahead of the rotating tool. Experiments are conducted on 6061-T4 aluminium alloy plates by this new process and the conventional friction stir welding process, respectively. The morphology and macrograph of the welds under both conditions are observed and contrasted. The experimental results show that ultrasonic vibration enhanced friction stir welding can improve the weld formation quality and increase the welding efficiency. And it just needs a smaller axial downward force. Because that the added action of ultrasonic vibration energy may enhance the localized softening extent and the plastic flow around the tool. In addition, it also improves the mechanical properties of the welded joints.

Effect of retained austenite and nonmetallic inclusions on the thermal/electrical properties and resistance spot welding nuggets of Si-containing TRIP steels

Five advanced high-strength transformation-induced plasticity(TRIP) steels with different chemical compositions were studied to correlate the retained austenite and nonmetallic inclusion content with their physical properties and the characteristics of the resistance spot welding nuggets. Electrical and thermal properties and equilibrium phases of TRIP steels were predicted using the JMatPro? software. Retained austenite and nonmetallic inclusions were quantified by X-ray diffraction and saturation magnetization techniques. The nonmetallic inclusions were characterized by scanning electron microscopy. The results show that the contents of Si, C, Al, and Mn in TRIP steels increase both the retained austenite and the nonmetallic inclusion contents. We found that nonmetallic inclusions affect the thermal and electrical properties of the TRIP steels and that the differences between these properties tend to result in different cooling rates during the welding process. The results are discussed in terms of the electrical and thermal properties determined from the chemical composition and their impact on the resistance spot welding nuggets.

Research on key control technologies of all-position automatic welding machine

The pipeline all-position automatic welding machine system is a special welding system for automatically welding circumferential joint of pipeline on site, which has been widely used to the long-distance pipeline construction projects due to the advantages of automatic control for welding parameters at all-position, moving speed of bugs and operating. In this paper, the key control technologies of PAWM all-position automatic welding machine （ developed by Pipeline Research Institute of CNPC） such us the automatic control system, control software, personal digital assistant （PDA） software and complex programmable logic device （ CPLD ） program as well us the control method of welding parameter have been described detailedly. With the higher welding quality, higher welding effwiency and lower labor intensity, PA WM all-position automatic welding machine has been successfully applied in many famous pipeline construction projects.

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.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

Effects of Metal Absorber Thermal Conductivity on Clear Plastic Laser Transmission Welding

In our previous study, metals have been used as absorbers in the clear plastic laser transmission welding. The effects of metal thermal conductivity on the welding quality are investigated in the present work. Four metals with distinctly different thermal conductivities, i.e., titanium, nickel, molybdenum, and copper, are selected as light absorbers. The lap welding is conducted with an 808 nm diode laser and simulation experiments are also conducted. Nickel electroplating test is carried out to minimize the side-effects from different light absorptivities of different metals. The results show that the welding with an absorber of higher thermal conductivity can accommodate higher laser input power before smoking, which produces a wider and stronger welding seam.The positive role of the higher thermal conductivity can be attributed to the fact that a desirable thermal field distribution for the molecular diffusion and entanglement is produced from the case with a high thermal conductivity.

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.

Application of Support Vector Machine in Weld Defect Detection and Recognition of X-ray Images

Support vector machines（SVM） received wide attention for its excellent ability to learn, it has been applied in many fields. A review of the application of SVM in weld defect detection and recognition of X-ray image is been presented. We will show some commonly used methods of weld defect detection and recognition using SVM, and the advantages and disadvantages of each method will be discussed. SVM appears to be promising in weld defect detection and recognition, but future research is needed before it fully mature in this filed.

Understanding the mechanisms of friction stir welding based on computer simulation using particles

Friction stir welding(FSW) is a novel technique for joining different materials without melting. In FSW the welded components are joined by stirring the plasticized material of the welded edges with a special rotating pin plunged into the material and moving along the joint line. From the scientific point of view,the key role of the FSW processes belongs to formation of the special plasticized conditions and activation of physical mechanisms of mixing the materials in such conditions to produce the strong homogeneous weld. But it is still a lack of complete understanding of what are these conditions and mechanisms.This paper is devoted to understanding the mechanisms of material mixing in conditions of FSW based on a computer simulation using particles. The movable cellular automaton method(MCA), which is a representative of the particle methods in mechanics of materials, was used to perform all computations.Usually, material flow including material stirring in FSW is simulated using computational fluid mechanics or smoothed particle hydrodynamics, which assume that the material is a continuum and does not take into account the material structure. MCA considers a material as an ensemble of bonded particles. Breaking of inter-particle bonds and formation of new bonds enables simulation of crack nucleation and healing, as well as mass mixing and micro-welding.The paper consists of two main parts. In the first part, the simulations in 2 D statements are performed to study the dynamics of friction stir welding of duralumin plates and influence of different welding regimes on the features of the material stirring and temperature distribution in the forming welded joints. It is shown that the ratio of the rotational speed to the advancing velocity of the tool has a dramatic effect on the joint quality. A suitable choice of these parameters combined with additional ultrasonic impact could considerably reduce the number of pores and microcracks in the weld without significant overheating of the welded materials.The second part of the paper considers simulation in the 3 D statement. These simulations showed that using tool pins of different shape like a cylinder, cone, or pyramid without a shoulder results in negligible motion of the plasticized material in the direction of workpiece thickness. However, the optimal ratio of the advancing velocity to the rotational speed allows transporting of the stirred material around the tool pin several times and hence producing the joint of good quality.

Modeling of underwater wet welding seam forming based on support vector machines

Underwater welding is developing fast because of the exploration of marine resources, and underwater wet welding automation is urgently needed because of the rigorous environment. To control the welding process automatically, the model of the process should first be built to predict the current welding process status. In this paper, arc and visual sensors were used simultaneously to obtain the electrical and visual information of underwater wet welding, and support vector machines （SVM） were used to model the process, experiment results showed that the method could effectively use the information obtained and give precise prediction results.

Effects of strength matching and crack depth on the fracture parameters for welded joints

Fracture parameters of welded joints with different strength matching and crack depth in weld metal are investigated by using the methods of elastoplastic finite element analysis and three point bend specimen test. The results show that for shallow crack, the plastic zone turns large in loading process, and the fracture toughness turns high. The extent of the plastic zone of overmatched joint is larger than that of undermatched joint because it will extends to parent metal from the weld metal in loading process for the same CTOD value. The plastic zone of undermatched joint is restricted within the weld, and the size of that is small. Overmatched joint shows the fracture behaviour of shallow crack may more easily than the undermatched joint, while the two sorts of joint specimens have the same crack depth. Therefore, the fracture-resistant capability of overmatched weld is better than that of undermatched weld. when the toughness of weld metals is similar for both overmatched and undermatched joints.

Development of an automatic weld surface appearance inspection system using machine vision

In this paper, an automatic inspection system for weld surface appearance using machine vision has been developed to recognize weld surface defects such as porosities, cracks, etc. It can replace conventional manual visual inspection method, which is tedious, time-consuming, subjective, experience-depended, and sometimes biased. The system consists of a CCD camera, a self-designed annular light source, a sensor controller, a frame grabbing card, a computer and so on. After acquiring weld surface appearance images using CCD, the images are preprocessed using median filtering and a series of image enhancement algorithms. Then a dynamic threshold and morphology algorithms are applied to segment defect object. Finally, defect features information is obtained by eight neighborhoods boundary chain code algorithm. Experimental results show that the developed system is capable of inspecting most surface defects such as porosities, cracks with high reliability and accuracy.