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.

Hybrid Data-Driven and Mechanistic Modeling Approaches for Multiscale Material and Process Design

The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.

Review on biomass metallurgy:Pretreatment technology,metallurgical mechanism and process design

The metallurgy industry consumes a considerable amount of coal and fossil fuels,and its carbon dioxide emissions are increasing every year.Replacing coal with renewable,carbon-neutral biomass for metallurgical production is of great significance in reducing global carbon consumption.This study describes the current state of research in biomass metallurgy in recent years and analyzes the concept and scientific principles of biomass metallurgy.The fundamentals of biomass pretreatment technology and biomass metallurgy technology were discussed,and the industrial application framework of biomass metallurgy was proposed.Furthermore,the economic and social advantages of biomass metallurgy were analyzed to serve as a reference for the advancement of fundamental theory and industrial application of biomass metallurgy.

A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design

In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.

基于Material Design设计语言的隐喻设计研究

隐喻是一种语言上的修辞手法,它用人们熟知的事物去表现另一事物,并在这两者中建立类比关系。现代视觉设计中通过使用这种"类似性"的手法将产品传达给用户,加强用户的操作体验,减少用户对于应用的学习成本。本文通过对Material Design中布局、动效、风格三个方面中隐喻设计的进行分析研究,以习惯养成应用Fabulous为例讨论隐喻设计对移动端设计的影响,旨在为使用者提供参考实践。

TOPOLOGY DESCRIPTION FUNCTION BASED METHOD FOR MATERIAL DESIGN

The purpose of this paper is to investigate the application of topology description function （TDF） in material design. Using TDF to describe the topology of the microstructure, the formulation and the solving technique of the design problem of materials with prescribed mechanical properties are presented. By presenting the TDF as the sum of a series of basis functions determined by parameters, the topology optimization of material microstructure is formulated as a size optimization problem whose design variables are parameters of TDF basis functions and independent of the mesh of the design domain. By this method, high quality topologies for describing the distribution of constituent material in design domain can be obtained and checkerboard problem often met in the variable density method is avoided. Compared with the conventional level set method, the optimization problem can be solved simply by existing optimization techniques without the process to solve the ＇Hamilton-Jacobi-type＇ equation by the difference method. The method proposed is illustrated with two 2D examples. One gives the unit cell with positive Poisson＇s ratio, the other with negative Poisson＇s ratio. The examples show the method based on TDF is effective for material design.

An Expert System in FRP Composite Material Design

An expert system prototype for fibre-reinforced plastic matrix (FRP) composite material design, ESFRP, has been developed. The system consists of seven main functional parts: a general inference engine, a set of knowledge bases, a material properties algorithm base, an explanation engine, various data bases, several function models and the user interface. The ESFRP can simulate human experts to make design scheme for fibre-reinforced plastics design, FRP layered plates design and FRP typical engineering components design. It can also predict the material properties and make strength analysis according to the micro and macro mechanics of composite materials. A satisfied result can be gained through the reiterative design.

论UI扁平化设计体验感的实现——以Material Design为例

互联网行业的飞速发展推动了UI设计领域的不断进步。近年来,UI设计风格主流趋势逐渐由靠近真实的拟物化设计发展为如今的扁平化设计,随着大众审美的改变和对用户体验标准的提升,扁平化设计也在逐步拓展。在各种拓展中,Material Design遵从了设计美学原则、视觉语言原则和功能表达原则,创造了全新的扁平化UI交互体验,这样的交互体验是如何实现的,值得深思。

GMA material design and construction quality control for asphalt pavement of steel box girder: Case study of the Hong Kong–Zhuhai–Macao Bridge

To improve the quality of the Hong Kong–Zhuhai–Macao Bridge paving project,a new paving layer material,Guss-mastic asphalt(GMA),was proposed in this paper by combining the advantages of two types of cast asphalt mixtures:mastic asphalt(MA)and Guss asphalt(GA).Based on the characteristics of GMA,to simulate its actual production process,this study developed a small-simulated cooker mixing equipment.Moreover,the flow degree,60C dynamic stability,and impact toughness were proposed to be used to evaluate the construction and ease,high temperature stability,and fatigue resistance of GMA cast asphalt mixtures,respectively.Moreover,the quality control standards for GMA paving materials by indoor tests,field trial mix GMA material performance tests,and accelerated loading tests were finalized.The study showed that the developed simulated cooker yielded consistent mixing results in the same working environment as the engineering cooker device.Increasing the coarse aggregate incorporation rate,coarsening the mastic epure(ME)gradation composition,and using a smaller oil to stone ratio can reduce the flowability of the GMA materials to varying degrees.The four-point bending fatigue life and impact toughness of the different GMA materials are correlated well.A mobility of<20 s,60C dynamic stability of 400–800 times/mm,15C impact toughness of400 N⋅mm,and cooker car mixing temperature control standard of 210C–230C form an appropriate control index system for the design and production of GMA cast asphalt mixtures.Simultaneously,accelerated loading tests verified the accuracy and reliability of the quality control index system that has been used in the GMA paving project of the Hong Kong–Zhuhai–Macao Bridge deck and has achieved good application results.

Material design at nano and atomic scale for electrocatalytic CO2 reduction

Electrocatalytic CO2 reduction (ECR) into value-added chemicals offers potential solution for renewable energy as well as global carbon footprint concerns. In this review we introduce the general methods and metrics that are commonly applied in ECR, followed by a discussion of current reaction mechanisms and different pathways. We highlight how size and structure of electrocatalysts affect ECR performance and review recent advances in metalfree and single-atom catalysts. The challenges of ECR are also discussed and optimistic perspectives are made for future work.

New Trends of Material Design in Indoor Environment

Material is the substance foundation and carrier of indoor environment, and it is the premise and guarantee to realize the excellent indoor environment. It contains physical and sensory properties. In recent years, the material design in indoor environment tends to be more rational, it not only pursues the external performance of material, but also pays more attention to the health and emotional of residents on the basis of scientific and rational use of its properties. This paper based on some scientific methods of literature study, field survey and case study combs the new trends of material design and analyses specific performance and application of materials in development. It is expected that the research can enrich the theory of contemporary material design and provide valuable reference for future material and interior environment design.

Hydroforming Process Design Using Finite Element Simulation for a Vehicle Instrument Panel Support Frame

Tubular hydroforming has attracted increased attention in the vehicle industry recently. This paper covers a complete hydroforming process design for an instrum ent panel frame by finite element simulation using the explicit code LS-DYNA. The manufacturing process for the instrument panel frame consists of tube pre-be nding and final hydroforming. To accomplish hydroforming process design successf ully, a thorough investigation of proper combination of process parameters such as internal hydraulic pressure and axial feeding is carried out by finite element simulation to predict the tube wall thickness and shape. An optimized process parameter combination is obtained and verified by the instrument panel frame hyd roforming experiment. The experiment shows that designed process parameters can be used in real production through FEA simulation, but tubular thinned amplitu de by FEA is less than that with the experiment.

Characterization of Feed Properties for Conceptual Process Design Involving Complex Mixtures, Such as Natural Extracts

The number of products used as agro-chemicals, food additives, flavors, aromas, pharmaceuticals and nutraceuticals which are made by fermentation or extraction from plants has increased significantly. Despite this growth, initial predictions for a potential product purification process for these complex mixtures remains entirely experimentally based. The present work represents an initial study to demonstrate the benefits of a systematic approach. For process development of chemically well-studied systems model based process design methods are already available. Therefore the proposed approach focuses on a method for the efficient characterization of the physical properties of the key components. Once this is adequately defined, unit operations and their potential to separate the feed components can be modeled. The current state of research is discussed. Based on this evaluation the most efficient method for conceptual process development has been identified and further developed. The resulting methodology consists of model-based cost accounting accompanied by experimental model-parameter determination. The latter is carried out at in miniaturized laboratory-scale measurement cells for each unit operation using the complete original feed. The model-based modelparameter determination from these experiments is accompanied by a comprehensive error analysis. The experimental plan currently includes the determination of thermodynamic equilibrium conditions in the mixture directly from the raw material mixture. Transport kinetics and fluid dynamic parameters are first estimated from known correlations or preexisting knowledge. Later on these parameters are determined exactly in mini-plant experiments. Furthermore, biological and botanical-based guidelines are developed to identify thermodynamically favored basic operations. Finally, the developed approaches are successfully validated using two plant extracts. Firstly, it could be proven that the botanical pre-selection can reduce the experimental plan significantly. Secondly, it was shown that the experimental equilibrium data of the kinetics and fluid dynamics can have a significant impact on the separation costs. Therefore, detailed rigorous modeling approaches have to be chosen instead of short-cut methods in order to make any valid process development conclusions or to further optimize the system.

Application of CAD redevelopment in process design of stepping-mode laser blanking

To design a stepping-mode laser blanking process,in this study,CAD software was redeveloped based on VBA and a computer interface was established in the process design system. The program employs the modularization method to perform functions including one-key initialization of the process planning environment,creation and deletion of blanking steps,automatic identification of belt coordinates,recognition of the number of blanking steps in each domain,and the output and import of process data. The difficult problem of recognizing CAD block references with the same name in the automatic acquisition of belt coordinates is solved using the selection sets method,which greatly improves the efficiency of the process design,while also guaranteeing rapid development of the flexible data mold in stepping-mode laser blanking.

Research and application of the digitalization of the production process design for plate steels

There are multiple processes and corresponding parameters in steel production, and combinations of these comprise various process routes.Different steel products require distinct process routes due to variations in performance targets.Thus, how to accurately set each key process parameter in certain process routes is an ongoing conundrum, because it not only requires a wealth of expert experience but also generates additional costs from the trial productions.In this paper, a new production design system for plate steels is proposed.The proposed system consists of methodology and function development.For methodology, multi-task Elastic Net, clustering, classification, and other methods are used to design process routes.Furthermore, the results are expressed in the form of parameter confidence intervals, which are close to practical application scenarios.For function development, the steel plate process route design function is developed on the Process Intelligent Data Application System(PIDAS) intelligent big data platform.The results demonstrate the method’s practical application value.

Optimization and Process Design Tools for Estimation of Weekly Exposure to Air Pollution Integrating Travel Patterns during Pregnancy

A growing number of international studies have highlighted that ambient air pollution exposures are related to different health outcomes. To do so, researchers need to estimate exposure levels to air pollution throughout everyday life. In the literature, the most commonly used estimate is based on home address only or taking into account, in addition, the work address. However, several studies have shown the importance of daily mobility in the estimate of exposure to air pollutants. In this context, we developed an R procedure that estimates individual exposures combining home addresses, several important places, and itineraries of the principal mobility during a week. It supplies researchers a useful tool to calculate individual daily exposition to air pollutants weighting by the time spent at each of the most frequented locations (work, shopping, residential address, etc.) and while commuting. This task requires the efficient calculation of travel time matrices or the examination of multimodal transport routes. This procedure is freely available from the Equit’Area project website: (https://www.equitarea.org). This procedure is structured in three parts: the first part is to create a network, the second allows to estimate main itineraries of the daily mobility and the last one tries to reconstitute the level of air pollution exposure. One main advantage of the tool is that the procedure can be used with different spatial scales and for any air pollutant.

Advanced Welding Technology for Highly Stressable Multi-Material Designs with Fiber-Reinforced Plastics and Metals

Organic sheets made out of fiber-reinforced thermoplastics are able to make a crucial contribution to increase the lightweight potential of a design. They show high specific strength- and stiffness properties, good damping characteristics and recycling capabilities, while being able to show a higher energy absorption capacity than comparable metal constructions. Nowadays, multi-material designs are an established way in the automotive industry to combine the benefits of metal and fiber-reinforced plastics. Currently used technologies for the joining of organic sheets and metals in large-scale production are mechanical joining technologies and adhesive technologies. Both techniques require large overlapping areas that are not required in the design of the part. Additionally, mechanical joining is usually combined with “fiber-destroying” pre-drilling and punching processes. This will disturb the force flux at the joining location by causing unwanted fiber- and inter-fiber failure and inducing critical notch stresses. Therefore, the multi-material design with fiber-reinforced thermoplastics and metals needs optimized joining techniques that don’t interrupt the force flux, so that higher loads can be induced and the full benefit of the FRP material can be used. This article focuses on the characterization of a new joining technology, based on the Cold Metal Transfer (CMT) welding process that allows joining of organic sheets and metals in a load path optimized way, with short cycle times. This is achieved by redirecting the fibers around the joining area by the insertion of a thin metal pin. The path of the fibers will be similar to paths of fibers inside structures found in nature, e.g. a knothole inside of a tree. As a result of the bionic fiber design of the joint, high joining strengths can be achieved. The increase of the joint strength compared to blind riveting was performed and proven with stainless steel and orthotropic reinforced composites in shear-tests based on the DIN EN ISO 14273. Every specimen joined with the new CMT Pin joining technology showed a higher strength than specimens joined with one blind rivet. Specimens joined with two or three pin rows show a higher strength than specimens joined with two blind rivets.

Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Impacts of thermal and electric contact resistance on the material design in segmented thermoelectric generators

Segmented thermoelectric generators(STEGs)can exhibit present superior performance than those of the conventional thermoelectric generators.Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg.This may significantly hinder performance improvement.In this study,a five-layer STEG with three pairs of thermoelectric(TE)materials was investigated considering the thermal and electrical contact resistances on the material contact surface.The STEG performance under different contact resistances with various combinations of TE materials were analyzed.The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning.Based on the genetic algorithm,for each contact resistance combination,the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency.To reveal the underlying mechanism that determines the heat-to-electrical performance,the total electrical resistance,output voltage,ZT value,and temperature distribution under each optimized scenario were analyzed.The STEG can augment the heat-to-electricity performance only at small contact resistances.A large contact resistance significantly reduces the performance.At an electrical contact resistance of RE=10^(-3) K⋅m^(2)⋅W^(-1) and thermal contact resistance of RT=10-8Ω⋅m^(2),the maximum electric power was reduced to 5.71 mW(90.86 mW without considering the contact resistance).And the maximum energy conversion efficiency is lowered to 2.54%(12.59%without considering the contact resistance).