Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Reliable calculations of nuclear binding energies by the Gaussian process of machine learning

Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.

Predicting grain size-dependent superplastic properties in friction stir processed ZK30 magnesium alloy with machine learning methods

The aim of this work is to predict,for the first time,the high temperature flow stress dependency with the grain size and the underlaid deformation mechanism using two machine learning models,random forest(RF)and artificial neural network(ANN).With that purpose,a ZK30 magnesium alloy was friction stir processed(FSP)using three different severe conditions to obtain fine grain microstructures(with average grain sizes between 2 and 3μm)prone to extensive superplastic response.The three friction stir processed samples clearly deformed by grain boundary sliding(GBS)deformation mechanism at high temperatures.The maximum elongations to failure,well over 400% at high strain rate of 10^(-2)s^(-1),were reached at 400℃ in the material with coarsest grain size of 2.8μm,and at 300℃ for the finest grain size of 2μm.Nevertheless,the superplastic response decreased at 350℃ and 400℃ due to thermal instabilities and grain coarsening,which makes it difficult to assess the operative deformation mechanism at such temperatures.This work highlights that the machine learning models considered,especially the ANN model with higher accuracy in predicting flow stress values,allow determining adequately the superplastic creep behavior including other possible grain size scenarios.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Review on non-conventional machining of shape memory alloys

Shape memory alloys （SMAs） are the developing advanced materials due to their versatile specific properties such as pseudoelasticity, shape memory effect （SME）, biocompatibility, high specific strength, high corrosion resistance, high wear resistance and high anti-fatigue property. Therefore, the SMAs are used in many applications such as aerospace, medical and automobile. However, the conventional machining of SMAs causes serious tool wear, time consuming and less dimensional deformity due to severe strain hardening and pseudoelasticity. These materials can be machined using non-conventional methods such as laser machining, water jet machining （WJM） and electrochemical machining （ECM）, but these processes are limited to complexity and mechanical properties of the component. Electrical discharge machining （EDM） and wire EDM （WEDM） show high capability to machine SMAs of complex shapes with precise dimensions. The aim of this work is to present the consolidated references on the machining of SMAs using EDM and WEDM and subsequently identify the research gaps. In support to these research gaps, this work has also evolved the future research directions.

Machining characteristics of fine grained AZ91 Mg alloy processed by friction stir processing

AZ91Mg alloy was considered and friction stir processing(FSP)was adopted to achieve grain refinement to investigatethe effect of grain size and secondary phase on machining characteristics during drilling at various speeds and feeds.Super saturatedAZ91Mg alloy was obtained after FSP and the grain refinement was achieved from(166.5±8.7)μm to(21.7±13.5)μm.Surprisingly,hardness reduced for FSP AZ91Mg alloy(88.95±6.1)compared with AZ91alloy(108.2±15.6),which was attributed to the reducedsecondary phase.However,the mean cutting force for FSP-treated(FSPed)AZ91Mg alloy was marginally increased.The edgedamage of the drilled holes was lower for FSPed AZ91Mg alloy compared with unprocessed AZ91Mg alloy.Hence,it can beunderstood that the grain refinement may slightly increase the cutting forces during drilling but better edge finishing can be achievedin machining of AZ91Mg alloy.

WORKPIECE LOCATING AND POST PROCESSING SYSTEMS ON 6-DOF CNC MILLING MACHINE

A conventional non-computerized numerical control （CNC） machine is updated by mounting a six degree-of-free （DOF） parallel mechanism on it, thus obtaining a new CNC one. The structure of this CNC milling machine is introduced, and the workpiece locating system and the post processing system of the cutter location （CL） data file are analyzed. The new machine has advantages of low costs, simple structure, good rigidity, and high precision. It is easy to be transformed and used to process the workpiece with a complex surface.

An Example of Machine Vision Applied in Printing Quality Checking——Research on the Checking of Printing Quality by Image Processing

The traditional printing checking method always uses printing control strips,but the results are not very well in repeatability and stability. In this paper,the checking methods for printing quality basing on image are taken as research objects. On the base of the traditional checking methods of printing quality,combining the method and theory of digital image processing with printing theory in the new domain of image quality checking,it constitute the checking system of printing quality by image processing,and expound the theory design and the model of this system. This is an application of machine vision. It uses the high resolution industrial CCD(Charge Coupled Device) colorful camera. It can display the real-time photographs on the monitor,and input the video signal to the image gathering card,and then the image data transmits through the computer PCI bus to the memory. At the same time,the system carries on processing and data analysis. This method is proved by experiments. The experiments are mainly about the data conversion of image and ink limit show of printing.

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.

Machining Process Classification Based on Carbon Footprint Analysis

Despite spending considerable effort on the development of manufacturing technology during the production process,manufacturing companies experience resources waste and worse ecological influences. To overcome the inconsistencies between energy-saving and environmental conservation,a uniform way of reporting the information and classification was presented. Based on the establishment of carbon footprint( CFP) for machine tools operation,carbon footprint per kilogram( CFK) was proposed as the normalized index to evaluate the machining process.Furthermore,a classification approach was developed as a tracking and analyzing system for the machining process. In addition,a case study was also used to illustrate the validity of the methodology. The results show that the approach is reasonable and feasible for machining process evaluation,which provides a reliable reference to the optimization measures for low carbon manufacturing.

Experimental Research on Effects of Process Parameters on Servo Scanning 3D Micro Electrical Discharge Machining

Servo scanning 3D micro electrical discharge machining (3D SSMEDM) is a novel and effective method in fabricating complex 3D micro structures with high aspect ratio on conducting materials. In 3D SSMEDM process, the axial wear of tool electrode can be compensated automatically by servo-keeping discharge gap, instead of the traditional methods that depend on experiential models or intermittent compensation. However, the effects of process parameters on 3D SSMEDM have not been reported up until now. In this study, the emphasis is laid on the effects of pulse duration, peak current, machining polarity, track style, track overlap, and scanning velocity on the 3D SSMEDM performances of machining efficiency, processing status, and surface accuracy. A series of experiments were carried out by machining a micro-rectangle cavity (900 μm×600 μm) on doped silicon. The experimental results were obtained as follows. Peak current plays a main role in machining efficiency and surface accuracy. Pulse duration affects obviously the stability of discharge state. The material removal rate of cathode processing is about 3/5 of that of anode processing. Compared with direction-parallel path, contour-parallel path is better in counteracting the lateral wear of tool electrode end. Scanning velocity should be selected moderately to avoid electric arc and short. Track overlap should be slightly less than the radius of tool electrode. In addition, a typical 3D micro structure of eye shape was machined based on the optimized process parameters. These results are beneficial to improve machining stability, accuracy, and efficiency in 3D SSMEDM.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalography

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Machining performance on hybrid process of abrasive jet machining and electrical discharge machining

To develop a hybrid process of abrasive jet machining (AJM) and electrical discharge machining (EDM),the effects of the hybrid process parameters on machining performance were comprehensively investigated to confirm the benefits of this hybrid process.The appropriate abrasives delivered by high speed gas media were incorporated with an EDM in gas system to construct the hybrid process of AJM and EDM,and then the high speed abrasives could impinge on the machined surface to remove the recast layer caused by EDM process to increase the efficiency of material removal and reduce the surface roughness.In this study,the benefits of the hybrid process were determined as the machining performance of hybrid process was compared with that of the EDM in gas system.The main process parameters were varied to explore their effects on material removal rate,surface roughness and surface integrities.The experimental results show that the hybrid process of AJM and EDM can enhance the machining efficiency and improve the surface quality.Consequently,the developed hybrid process can fit the requirements of modern manufacturing applications.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

A Web-based machining process monitoring system for E-manufacturing implementation

Recently, with the rapid growth of information technology, many studies have been performed to implement Web-based manufacturing system. Such technologies are expected to meet the need of many manufacturing industries who want to adopt E-manufacturing system for the construction of globalization, agility, and digitalization to cope with the rapid changing market requirements. In this research, a real-time Web-based machine tool and machining process monitoring system is developed as the first step for implementing E-manufacturing system. In this system, the current variations of the main spindle and feeding motors are measured using hall sensors. And the relationship between the cutting force and the spindle motor RMS (Root Mean Square) current at various spindle rotational speeds is obtained. Thermocouples are used to measure temperature variations of important heat sources of a machine tool. Also, a rule-based expert system is applied in order to decide the machining process and machine tool are in normal conditions. Finally, the effectiveness of the developed system is verified through a series of experiments.

Improved Quality Prediction Model for Multistage Machining Process Based on Geometric Constraint Equation

Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process（MMP）. However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit mapping between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.

Automated deep learning system for power line inspection image analysis and processing: architecture and design issues

The continuous growth in the scale of unmanned aerial vehicle (UAV) applications in transmission line inspection has resulted in a corresponding increase in the demand for UAV inspection image processing. Owing to its excellent performance in computer vision, deep learning has been applied to UAV inspection image processing tasks such as power line identification and insulator defect detection. Despite their excellent performance, electric power UAV inspection image processing models based on deep learning face several problems such as a small application scope, the need for constant retraining and optimization, and high R&D monetary and time costs due to the black-box and scene data-driven characteristics of deep learning. In this study, an automated deep learning system for electric power UAV inspection image analysis and processing is proposed as a solution to the aforementioned problems. This system design is based on the three critical design principles of generalizability, extensibility, and automation. Pre-trained models, fine-tuning (downstream task adaptation), and automated machine learning, which are closely related to these design principles, are reviewed. In addition, an automated deep learning system architecture for electric power UAV inspection image analysis and processing is presented. A prototype system was constructed and experiments were conducted on the two electric power UAV inspection image analysis and processing tasks of insulator self-detonation and bird nest recognition. The models constructed using the prototype system achieved 91.36% and 86.13% mAP for insulator self-detonation and bird nest recognition, respectively. This demonstrates that the system design concept is reasonable and the system architecture feasible .

Adaptive control of machining process based on extended entropy square error and wavelet neural network

Combining information entropy and wavelet analysis with neural network,an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error(EESE)and wavelet neural network(WNN).Extended entropy square error function is defined and its availability is proved theoretically.Replacing the mean square error criterion of BP algorithm with the EESE criterion,the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter,translating parameter of the wavelet and neural network weights.Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network.The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions,thus improving the machining efficiency and protecting the tool.

Study on Licker-In and Flat Speeds of Carding Machine and Its Effects on Quality of Cotton Spinning Process

Spinning has a significant influence on all textile processes. Combinations of all the capital equipment display the process’ critical condition. By transforming unprocessed fibers into carded sliver and yarn, the carding machine serves a critical role in the textile industry. The carding machine’s licker-in and flat speeds are crucial operational factors that have a big influence on the finished goods’ quality. The purpose of this study is to examine the link between licker-in and flat speeds and how they affect the yarn and carded sliver quality. A thorough experimental examination on a carding machine was carried out to accomplish this. The carded sliver and yarn produced after experimenting with different licker-in and flat speed combinations were assessed for important quality factors including evenness, strength, and flaws. To account for changes in material qualities and machine settings, the study also took into consideration the impact of various fiber kinds and processing circumstances. The findings of the investigation showed a direct relationship between the quality of the carded sliver and yarn and the licker-in and flat speeds. Within a limited range, greater licker-in speeds were shown to increase carding efficiency and decrease fiber tangling. On the other hand, extremely high speeds led to more fiber breakage and neps. Higher flat speeds, on the other hand, helped to enhance fiber alignment, which increased the evenness and strength of the carded sliver and yarn. Additionally, it was discovered that the ideal blend of licker-in and flat rates varied based on the fiber type and processing circumstances. When being carded, various fibers displayed distinctive behaviors that necessitated adjusting the operating settings in order to provide the necessary quality results. The study also determined the crucial speed ratios between the licker-in and flat speeds that reduced fiber breakage and increased the caliber of the finished goods. The results of this study offer useful information for textile producers and process engineers to improve the quality of carded sliver and yarn while maximizing the performance of carding machines. Operators may choose machine settings and parameter adjustments wisely by knowing the impacts of licker-in and flat speeds, which will increase textile industry efficiency, productivity, and product quality.