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.

Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates

The commonly used trial-and-error method of biodegradable Zn alloys is costly and blindness.In this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first time to predict the ultimate tensile strength(UTS)and immersion corrosion rate(CR)of biodegradable Zn alloys.A real-time visualization interface has been established to design Zn-Mn based alloys;a representative alloy is Zn-0.4Mn-0.4Li-0.05Mg.Through tensile mechanical properties and immersion corrosion rate tests,its UTS reaches 420 MPa,and the prediction error is only 0.95%.CR is 73μm/a and the prediction error is 5.5%,which elevates 50 MPa grade of UTS and owns appropriate corrosion rate.Finally,influences of the selected features on UTS and CR are discussed in detail.The combined application of UTS and CR model provides a new strategy for synergistically regulating comprehens-ive properties of biodegradable Zn alloys.

Ab Initio Design of Ni-Rich Cathode Material with Assistance of Machine Learning for High Energy Lithium-Ion Batteries

With the widespread use of lithium-ion batteries in electric vehicles,energy storage,and mobile terminals,there is an urgent need to develop cathode materials with specific properties.However,existing material control synthesis routes based on repetitive experiments are often costly and inefficient,which is unsuitable for the broader application of novel materials.The development of machine learning and its combination with materials design offers a potential pathway for optimizing materials.Here,we present a design synthesis paradigm for developing high energy Ni-rich cathodes with thermal/kinetic simulation and propose a coupled image-morphology machine learning model.The paradigm can accurately predict the reaction conditions required for synthesizing cathode precursors with specific morphologies,helping to shorten the experimental duration and costs.After the model-guided design synthesis,cathode materials with different morphological characteristics can be obtained,and the best shows a high discharge capacity of 206 mAh g^(−1)at 0.1C and 83%capacity retention after 200 cycles.This work provides guidance for designing cathode materials for lithium-ion batteries,which may point the way to a fast and cost-effective direction for controlling the morphology of all types of particles.

Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

Non-ionic deep eutectic solvents(DESs)are non-ionic designer solvents with various applications in catalysis,extraction,carbon capture,and pharmaceuticals.However,discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation.The search for DES relies heavily on intuition or trial-and-error processes,leading to low success rates or missed opportunities.Recognizing that hydrogen bonds(HBs)play a central role in DES formation,we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning(ML)models to discover new DES systems.We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics(MD)simulation trajectories.The analysis reveals that DES systems have two unique features compared to non-DES systems:The DESs have①more imbalance between the numbers of the two intra-component HBs and②more and stronger inter-component HBs.Based on these results,we develop 30 ML models using ten algorithms and three types of HB-based descriptors.The model performance is first benchmarked using the average and minimal receiver operating characteristic(ROC)-area under the curve(AUC)values.We also analyze the importance of individual features in the models,and the results are consistent with the simulation-based statistical analysis.Finally,we validate the models using the experimental data of 34 systems.The extra trees forest model outperforms the other models in the validation,with an ROC-AUC of 0.88.Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

Transformer for the Design of Automatic Control Systems with Prefilters

A machine learning model, using the transformer architecture, is used to design a feedback compensator and prefilter for various simulated plants. The output of the transformer is a sequence of compensator and prefilter parameters. The compensator and prefilter are linear models, preserving the ability to analyze the system with linear control theory. The input to the network is a window of recent reference and output samples. The goal of the transformer is to minimize tracking error at each time step. The plants under consideration range from simple to challenging. The more difficult plants contain closely spaced, lightly damped, complex conjugate pairs of poles and zeros. Results are compared to PID controllers tuned for a similar crossover frequency and optimal phase margin. For simple plants, the transformer converges to solutions which overly rely on the prefilter, neglecting the maximization of negative feedback. For more complex plants, the transformer designs a compensator and prefilter with more desirable qualities. In all cases, the transformer can start with random model parameters and modify them to minimize tracking error on the step reference.

Practice and Exploration of Ideological and Political Teaching in Landscape Design Courses for Environmental Design Majors

To implement the fundamental goal of moral education,based on the teaching practice of landscape design courses,we explore how to integrate value shaping,knowledge transfer,and skill cultivation in the teaching links of landscape design courses in environmental design majors,such as selecting design topics,assigning tasks,researching projects,designing works,designing explanations,and defense.

Research on the Course of Principles of Concrete Structure Design

Nowadays,education and teaching have become a hot topic,and teaching in colleges and universities is facing a brand-new development direction.Principles of Concrete Structure Design,as one of the main courses,transmits professional knowledge for students,enhances the students’professional ability,and further carries out in-depth research on the course to bring a better teaching effect for students.The article mainly focuses on the research of the principles of concrete structure design course,conducts an analysis of the teaching characteristics of the principles of concrete structure design course,and reasonably sets the teaching content from the optimization of the course teaching objectives;innovative course teaching methods can deepen the effect of knowledge understanding;reform of experimental practice teaching can lay down the effect of the internalization of knowledge,etc.The in-depth description and discussion of the relevant aspects of the research aim to provide guidelines for related research.

Multidimensional Perspectives on the Teaching and Practice of Typography Design

With the deepening of the curriculum teaching reform in colleges and universities,the teaching reform of art design professional courses is imperative.The study found that the current art design professional font design course teaching there are more solidified teaching methods,students operating ability is relatively homogenised,the teaching content to the"westernised"based,resulting in poor adaptability of students and other prominent problems.This paper starts from the"three effects"of"effect","efficiency"and"effectiveness",and adopts a multi-dimensional and emotional thinking perspective to carry out the curriculum of typeface design.This paper starts from the"three effects"of"effect","efficiency"and"effectiveness",and adopts a multi-dimensional and emotional perspective of thinking to carry out the teaching and practice of typeface design in order to strengthen the visual information of Chinese culture,and at the same time,to explore the creative potential of the students,so as to improve the students'innovation and creativity.

Integration Mechanism of Ideological and Political Education with Specialized Knowledge in the Course of Green Building Design

This paper aims to explore the integration of ideological and political education with specialized knowledge in the course of green building design.Through an analysis of the green building design course,combined with the current social development background and the need for ecological and environmental protection,the study investigates how to effectively integrate ideological and political education elements into course instruction to enhance the comprehensive quality and green design capabilities of architecture students.The paper analyzes the content and characteristics of the green building design course and proposes an integration mechanism for ideological and political education in green building design teaching,aiming to cultivate architecture talents with green design concepts and good ideological and political qualities.

Teaching Design and Implementation of the Electrical and Electronic Technology Course Based on Teaching Ability Competition

The teaching ability competition is a vital method for stimulating teachers’enthusiasm and enhancing their teaching skills.This approach has gained increasing attention from colleges and universities in recent years.The competition focuses on evaluating teachers’capabilities in designing and implementing lessons,encouraging the exchange of new educational ideas and high-quality teaching methods.Reforming the teaching of Electrical and Electronic Technology courses based on insights from teaching competitions can significantly improve teaching quality.This paper explores the teaching design and implementation of the Electrical and Electronic Technology course,drawing on the principles of teaching ability competitions.First,it analyzes the existing problems in teaching this course.Then,it highlights the significant benefits that teaching competitions bring to the course.Based on these insights,the paper proposes practical strategies aimed at enhancing students’interest and practical skills,thereby promoting innovative developments in teaching Electrical and Electronic Technology.

Recent progress in the machine learning-assisted rational design of alloys

Alloys designed with the traditional trial and error method have encountered several problems,such as long trial cycles and high costs.The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials,that is,machine learning-assisted design.In this paper,the basic strategy for the machine learning-assisted rational design of alloys was introduced.Research progress in the property-oriented reversal design of alloy composition,the screening design of alloy composition based on models established using element physical and chemical features or microstructure factors,and the optimal design of alloy composition and process parameters based on iterative feedback optimization was reviewed.Results showed the great advantages of machine learning,including high efficiency and low cost.Future development trends for the machine learning-assisted rational design of alloys were also discussed.Interpretable modeling,integrated modeling,high-throughput combination,multi-objective optimization,and innovative platform building were suggested as fields of great interest.

Kansei Engineering Applied to the Form Design of Injection Molding Machines

This study investigated the relationship between a subject’s evaluation of injection molding machines (IMMs) and formal design features using Kansei engineering. This investigation used 12 word pairs to evaluate the IMM configurations and employed the semantic differential method to explore the perception of 60 interviewees of 12 examples. The relationship between product feature design and corresponding words was derived by multiple regression analysis. Factor analysis reveals that the 12 examples can be categorized as two styles—advanced style and succinct style. For the advanced style, an IMM should use a rectangular form for the clamping-unit cover and a full-cover for the injection-unit. For the succinct style, the IMM configuration should use a beveled form for the safety cover and a vertical rectangular form for the clamping-unit cover. Quantitative data and suggested guidelines for the relationship between design features and interviewee evaluations are useful to product designers when formulating design strategies.

The Influence of a User-Centred Design Focus on the Effectiveness of a User Interface for an Agricultural Machine

As agricultural machines become more complex, it is increasingly critical that special attention be directed to the design of the user interface to ensure that the operator will have an adequate understanding of the status of the machine at all times. A user-centred design focus was employed to develop two conceptual designs (UCD1 & UCD2) for a user interface for an agricultural air seeder. The two concepts were compared against an existing user interface (baseline condition) using the metrics of situation awareness (Situation Awareness Global Assessment Technique), mental workload (Integrated Workload Scale), reaction time, and subjective feedback. There were no statistically significant differences among the three user interfaces based on the metric of situation awareness;however, UCD2 was deemed to be significantly better than either UCD1 or the baseline interface on the basis of mental workload, reaction time and subjective feedback. The research has demonstrated that a user-centred design focus will generate a better user interface for an agricultural machine.

Integration Path of Ideological and Political Education and Labor Education in Landscape Design Courses under the Background of Rural Revitalization

As an important part of rural construction, landscape design is the basic content of rural revitalization. Ideological and political education and labor education are both important contents of the education system in the new era, which are of great significance to the cultivation of outstanding college students. The integration of ideological and political education and labor education(hereafter referred to as “ideological, political and labor education”) in landscape design courses has not yet formed a complete education path. Based on the ideological, political and labor education research of landscape design courses under the background of rural revitalization, the study aims to cultivate first-class talent team for rural construction to help rural revitalization by improving the teaching quality of landscape design courses, and enhancing students' comprehensive quality.

Design and production of the network courses based on the database technology

Online teaching is an effective means and the inevitable trend of the development of modern education, its shared educational resources, to expand the scale of education to improve educational efficiency; build a lifelong education system plays an important role. In this paper, by analyzing the common feature of database system and network courses, it proposes a network-based database technology curriculum design and fabrication methods and principles, to make the curriculum from web centric processing teaching content production to around a shared database of teaching resource center changes.

Analyzing the Design and Promotion of Ideological and Political Content in Online Finance and Economics Courses

In the context of the new media age,the emergence and development of finance and economics courses along with online course resources have greatly enriched the presentation of teaching resources in colleges and universities.However,subjected to the flawed design and poor promotion of ideological and political contents,which are less popular,online finance and economics courses often do not reflect the meaning well or maximize the application of the courses’ideological and political reform.This paper explores the dilemma and reasons for the promotion of the courses’ideological and political content,in hope to provide suggestions for the design and promotion of ideological and political content in online finance and economics courses in colleges and universities.

Performance Evaluation and Comparison of Multi - Objective Optimization Algorithms for the Analytical Design of Switched Reluctance Machines

This paper systematically evaluates and compares three well-engineered and popular multi-objective optimization algorithms for the design of switched reluctance machines.The multi-physics and multi-objective nature of electric machine design problems are discussed,followed by benchmark studies comparing generic algorithms(GA),differential evolution(DE)algorithms and particle swarm optimizations(PSO)on a 6/4 switched reluctance machine design with seven independent variables and a strong nonlinear multi-objective Pareto front.To better quantify the quality of the Pareto fronts,five primary quality indicators are employed to serve as the algorithm testing metrics.The results show that the three algorithms have similar performances when the optimization employs only a small number of candidate designs or ultimately,a significant amount of candidate designs.However,DE tends to perform better in terms of convergence speed and the quality of Pareto front when a relatively modest amount of candidates are considered.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

A REVIEW OF THE HISTORY OF CHINA'S MACHINE DESIGN METHODS AND THE PROSPECT

This paper examines the history of China’s machine design methods and its status quo. First of all, it discusses machine design methods in ancient China. (1)The design idea of creation by the intelligent and exposition by the ingenious. (2)The design principle of the dependence of workmanship on criteria. (3)A macroscopic view of the object of design. (4)The design method of manufacture emphasizing shape. (5)Technological requirements on excellent material and consummate skill. Second, it discusses machine design ideas in ancient China. (1)The system idea in machine design, (2)The idea of machine design.(3)The idea of standardization in machine design. (4)The idea of automation in machine design. Finally, it discusses the status quo and a look ahead of the theory of machine design in China.

Machine learning for membrane design and discovery

Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.