Alloy design via additive manufacturing: Advantages,challenges, applications and perspectives

Additive manufacturing (AM) has rapidly changed both large- and small-scale production environments across many industries. By re-envisioning parts from the ground up, not limited to the challenges presented by traditional manufacturing techniques, researchers and engineers have developed new design strategies to solve large-scale materials and design problems worldwide. This is particularly true in the world of alloy design, where new metallic materials have historically been developed through tedious processes and procedures based primarily on casting methodologies. With the onset of directed energy deposition (DED) and powder bed fusion (PBF)-based AM, new alloys can be innovated and evaluated rapidly at a lower cost and considerably shorter lead time than has ever been achieved. This article details the advantages, challenges, applications, and perspectives of alloy design using primarily laser-based AM. It is envisioned that researchers in industry and academia can utilize this work to design new alloys leveraging metallic AM processes for various current and future applications.     

Optimal cost design of flow lines with reconfigurable machines for batch production

Modular reconfigurable machines offer the possibility to efficiently produce a family of different parts. This paper formalises a cost optimisation problem for flow lines equipped with reconfigurable machines which carry turrets, machining modules and single spindles. The proposed models take into account constraints related to: (i) design of machining modules, turrets, and machines, (ii) part locations, and (iii) precedence relations among operations. The goal is to minimise equipment cost while reaching a given output and satisfying all the constraints. A mixed integer programming model is developed for the considered optimisation problem. The approach is validated through an industrial case study and extensive numerical experiments.     

Comparative investigations into microstructural and mechanical properties of as-cast and laser powder bed fusion (LPBF) fabricated duplex steel (1.4517)

A methodology is presented that compares the microstructural and mechanical properties of as-cast and additive-made ferritic-austenitic duplex steel 1.4517. Microstructure of approximately equal amounts of ferrite and austenite measured in as-cast material could not be replicated in post heat-treated laser powder bed fusion samples after 30 min and 60 min of post heat treatment. This is attributed to nitrogen loss during powder atomization which left fewer austenite formers. Post-heat treated laser powder bed fusion samples of duplex structure had its austenite content repeatedly adjusted between 38 % and 40 %. As-built laser powder bed fusion tensile specimens which had a ferritic microstructure recorded high tensile and yield strength but had very poor elongation. Post heat-treated duplex laser powder bed fusion tensile specimen built in both horizontal and vertical orientations had good tensile and yield strength comparable to conventional casting processes; Tensile strength – 739 MPa (horizontal), 759 MPa (vertical); Yield strength (R_p0.2) – 489 MPa (horizontal), 525 MPa (vertical). The horizontally built duplex specimen had a very high elongation of 32 % than the vertical (11 %) or conventionally reported (22 %). This work establishes the 1.4517 duplex steel as a good candidate with good mechanical properties when processed by additive manufacturing.     

[数据驱动的智能服务]数据驱动的复杂产品智能服务技术与应用

随着传感器、数据采集装置和其他具备感知能力的模块在复杂产品服务运行阶段的应用，复杂产品运维系统的数字化和智能化程度越来越高，具有实时、多源、异构、海量等特性的数据成为提高复杂产品系统可靠和低成本运行的决策依据，数字孪生技术提供了一种有效途径。介绍了数据驱动的复杂产品智能服务研究进展；分析了数据驱动的智能服务基本特征与框架模型；提出了数据驱动的复杂产品智能服务方法，主要包括面向服务的复杂产品建模与仿真方法、数据驱动的服务需求获取与精准分析预测方法、基于数字孪生的设备故障识别与动态性能预测方法、数据驱动的装备视情维修与备件库存联合多目标决策优化方法、基于数字孪生的复杂产品辅助维修技术、多要素协同的复杂装备能效精准分析预测方法、基于数据挖掘的复杂产品运行优化控制方法等；给出了智能服务系统的应用案例。所提出的框架和方法可为现代制造服务的智能化转型升级提供参考。     

原子及近原子尺度制造——制造技术发展趋势

探讨了制造技术发展趋势，论述了制造历史发展的三个阶段，指出原子及近原子尺度制造（ACSM）是下一代制造技术的主流发展趋势，对未来科技发展和高端元件制造具有重要意义。详细分析阐述了ACSM的主要研究内容及关键科学问题，并提出了相应的建议和措施。     

Offshore manufacturing contract design based on transfer price considering green tax: a bilevel programming approach

The problem of designing offshore manufacturing contract resulting in optimal transfer price is troubling multinational companies over the past few years. This paper proposes designing offshore manufacturing contracts based on the transfer price in the form of bilevel programming problems after considering green tax. In these contract designs, a firm in a developed country sells a single product in its market. The same product is simultaneously being manufactured by another firm in a developing country with lower manufacturing cost. After anticipating the consumer demand, the seller places an order, based on which the manufacturer manufactures the ordered quantity, and offers a transfer price which in turn maximises its net profit after paying green tax to its government. While setting the transfer price, the manufacturer considers the manufacturing cost, the export duty payable to its government and the cost of shipping the product to the developed country. After buying the product from the manufacturer at the transfer price, the seller then sets the retail price which maximises its net profit after paying the import duty to its government; the retail price, however, must not be more than the maximum retail price applicable to the market. Thus, offshore manufacturing contract results in optimal after-tax profits for both the firms. An experimental study has been carried out to discuss the practical aspects of the results developed, where a US firm is offshoring its manufacturing activity to a Chinese firm in order to draw maximum profit.     

Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS

It is of great practical significance to optimise the decision-making of cloud manufacturing service providers, which can ensure the efficient operation of the cloud manufacturing services. In order to effectively optimise, this paper constructs the evaluation index system, sorts out 7 important evaluation indexes. A method is proposed on the basis of the TOPSIS method and Grey Correlation Analysis (GRA), which is a decision-making method combining static distance and dynamic trend, and make the evaluation results more reasonable. In addition, in order to reflect the true weight of the high dimensional index, the evaluation model of projection pursuit index is established. The improved Particle Swarm Optimisation (PSO) algorithm is used to optimise the projection index function and model parameters, and to obtain the objective weights of evaluation indexes. Then the objective weights combine with the subjective weights obtained by the Analytic hierarchy process (AHP), which makes the weights of the evaluation indexes achieve the unity of subjective and objective. Finally, 20 sets of simulation examples are given to illustrate the feasibility and effectiveness of the proposed method.     

钛合金表面多磁极耦合旋转磁场光整加工特性

面向激光增材制造钛合金表面的光整加工需求，设计出一种多磁极耦合旋转磁场光整加工装置来研究光整加工特性。基于ANSYS Maxwell仿真软件分析了光整加工装置的磁场强度分布。搭建了实验光整平台，分析了主轴转速、C轴转速和加工间隙对表面质量的影响。结果表明，在主轴转速500 r/min、C轴转速160 r/min和加工间隙0.7 mm的加工条件下，表面粗糙度Ra由5.991 μm下降至0793 μm。扫描电子显微镜(SEM)观测表明，光整后的钛合金表面沉积层消失，表面质量得到显著改善。     

[数据驱动的智能服务]面向大规模个性化的交互式云制造模式

结合云制造以及大规模个性化的特点，分析大规模个性化的应用发展以及阐述用户需求的现状，提出了一种面向大规模个性化的交互式云模式。设计了云环境下大规模个性化智能交互过程以及需求交互转换过程模型；建立了大规模个性化的交互式云体系架构以及工作流程；进行交互式云平台搭建，以实现用户定制需求的个性化、大规模、低成本的快速提供与生产。以汽车定制为例，对需求分析及转换过程进行论述，验证了该平台的可行性和有效性。     

Convolutional and generative adversarial neural networks in manufacturing

Manufacturing is undergoing transformation driven by the developments in process technology, information technology, and data science. A future manufacturing enterprise will be highly digital. This will create opportunities for machine learning algorithms to generate predictive models across the enterprise in the spirit of the digital twin concept. Convolutional and generative adversarial neural networks have received some attention of the manufacturing research community. Representative research and applications of the two machine learning concepts in manufacturing are presented. Advantages and limitations of each neural network are discussed. The paper might be helpful in identifying research gaps, inspire machine learning research in new manufacturing domains, contribute to the development of successful neural network architectures, and getting deeper insights into the manufacturing data.