Coordinated scheduling of production and logistics for large-scale closed-loop manufacturing using Benders decomposition optimization

This paper studies coordinated scheduling of production and logistics for a large-scale closed-loop manufacturing system by integrating its manufacturing and recycling process. In addition to the forward manufacturing process, different recycling units in reverse recycling process are also studied. A decentralized network is designed to formulate the coordinated scheduling problem as a mixed integer programming model with both binary and integer variables. As the problem for closed-loop manufacturing is large-scale and computational-consuming in nature, the model is divided into integer variable sub-models and complex binary variable sub-models for preprocessing and reprocessing respectively. An iterative solution approach by Benders decomposition is developed to accelerate the solving efficiency in large-scale case by updating custom constraints. A case study is conducted to investigate the managerial implications of the decentralized network for the closed-loop manufacturing system. Computational experiments demonstrate the validity and efficiency of the proposed iterative solution approach for the large-scale scenarios.     

基于级联加性噪声模型的因果结构学习算法

现有级联非线性加性噪声模型可解决隐藏中间变量的因果方向推断问题,然而对于包含隐变量和级联传递因果关系的因果网络学习存在全局结构搜索、等价类无法识别等问题。设计一种面向非时序观测数据的两阶段因果结构学习算法,第一阶段根据观测数据变量间的条件独立性,构建基本的因果网络骨架,第二阶段基于级联非线性加性噪声模型,通过比较骨架中每个相邻因果对在不同因果方向假设下的边缘似然度进行因果方向推断。实验结果表明,该算法在虚拟因果结构数据集的不同隐变量数量、平均入度、结构维度、样本数量下均表现突出,且在真实因果结构数据集中的F1值相比主流因果结构学习算法平均提升了51%,具有更高的准确率和更强的鲁棒性。     

Wire + Arc Additive Manufacturing

Depositing large components (>10?kg) in titanium, aluminium, steel and other metals is possible using Wire + Arc Additive Manufacturing. This technology adopts arc welding tools and wire as feedstock for additive manufacturing purposes. High deposition rates, low material and equipment costs, and good structural integrity make Wire+Arc Additive Manufacturing a suitable candidate for replacing the current method of manufacturing from solid billets or large forgings, especially with regards to low and medium complexity parts. A variety of components have been successfully manufactured with this process, including Ti–6Al–4V spars and landing gear assemblies, aluminium wing ribs, steel wind tunnel models and cones. Strategies on how to manage residual stress, improve mechanical properties and eliminate defects such as porosity are suggested. Finally, the benefits of non-destructive testing, online monitoring and in situ machining are discussed.     

Elastic modulus of 316 stainless steel lattice structure fabricated via binder jetting process

This study mainly evaluates the elastic modulus of 316 stainless steel lattice structures fabricated via binder jetting process. In this present research, both solid and lattice samples are designed and fabricated by binder jetting process for two different types of mechanical tests. Besides experimental study, a numerical model based on energy approach has been proposed to predict the effective elastic modulus of fabricated lattice samples. By comparing the calculated results of the proposed numerical model with the experimental results, the established model is proved to be validated. This numerical model can be used to determine the parameters of lattice structures fabricated by binder jetting process for desired mechanical properties. At the end, both advantages and disadvantages of the lattice structures fabricated by binder jetting process are analysed. Based on this analysis, the potential application and future research work are pointed out.     

Feedstock powder processing research needs for additive manufacturing development

Additive manufacturing (AM) promises to redesign traditional manufacturing by enabling the ultimate in agility for rapid component design changes in commercial products and for fabricating complex integrated parts. By significantly increasing quality and yield of metallic alloy powders, the pace for design, development, and deployment of the most promising AM approaches can be greatly accelerated, resulting in rapid commercialization of these advanced manufacturing methods. By successful completion of a critical suite of processing research tasks that are intended to greatly enhance gas atomized powder quality and the precision and efficiency of powder production, researchers can help promote continued rapid growth of AM. Other powder-based or spray-based advanced manufacturing methods could also benefit from these research outcomes, promoting the next wave of sustainable manufacturing technologies for conventional and advanced materials.     

Metal Alloys for Fusion‐Based Additive Manufacturing

Metal additive manufacturing (AM) is an innovative manufacturing technique, which builds parts incrementally layer by layer. Thus, metal AM has inherent advantages in part complexity, time, and waste saving. However, due to its complex thermal cycle and rapid solidification during processing, the alloys well suit and commercially used for metal AM today are limited. Therefore, it is important to understand the alloying strategy and current progress with materials performance to consider alloy development for metal AM. This review presents the current range of alloys available for metal AM, including titanium, steel, nickel, aluminum, less common alloys (including Mg alloys, metal matrix composites alloys, and low melting point alloys), and compositionally complex alloys (including bulk metallic glasses and high entropy alloys) with a focus on the relationship between compositions, processing, microstructures, and properties of each alloy system. In addition, some promising alloy systems for metal AM are highlighted. Approaches for designing and optimizing new materials for metal AM have been summarized.

     

Progress in additive manufacturing on new materials: A review

Recent efforts and advances in additive manufacturing (AM) on different types of new materials are presented and reviewed. Special attention is paid to the material design of cladding layers, the choice of feedstock materials, the metallurgical behavior and synthesis principle during the AM process, and the resulted microstructures and properties, as well as the relationship between these factors. Thereafter, the trend of development in the future is forecasted, including: Effects of the particles size and size distribution of powders; Approaches for producing fine microstructures; Opportunities for creating new materials by AM; Wide applications in reconditioning of damaged components; Challenges for deep understanding and applications of the AMed new materials. The idea of “Develop Materials” or “Create Materials” by AM is highlighted, but a series of scientific, technological and engineering problems remain to be solved in future.     

Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens

This overview firstly introduces the state-of-the-art research progress in length scale-related fatigue performance of conventionally-fabricated metals evaluated by miniature specimens. Some key factors for size effects sensitive to microstructures including the specimen thickness, grain size and a ratio between them are highlighted to summarize some general rules for size effects. Then, ongoing research progress and new challenges in evaluating the fatigue performance of additive manufactured parts controlled by location-specific defects, microstructure heterogeneities as well as mechanical anisotropy using miniature specimen testing technique are discussed and addressed. Finally, a potential roadmap to establish a data-driven evaluation platform based on a large number of miniature specimen-based experiment data, theoretical computations and the ‘big data’ analysis with machine learning is proposed. It is expected that this overview would provide a novel strategy for the realistic evaluation and fast qualification of fatigue properties of additive manufactured parts we have been facing to.     

Applying Job Shop Scheduling to SMEs Manufacturing Platform to Revitalize B2B Relationship

A small and medium enterprises (SMEs) manufacturing platform aims to perform as a significant revenue to SMEs and vendors by providing scheduling and monitoring capabilities. The optimal job shop scheduling is generated by utilizing the scheduling system of the platform, and a minimum production time, i.e., makespan decides whether the scheduling is optimal or not. This scheduling result allows manufacturers to achieve high productivity, energy savings, and customer satisfaction. Manufacturing in Industry 4.0 requires dynamic, uncertain, complex production environments, and customer-centered services. This paper proposes a novel method for solving the difficulties of the SMEs manufacturing by applying and implementing the job shop scheduling system on a SMEs manufacturing platform. The primary purpose of the SMEs manufacturing platform is to improve the B2B relationship between manufacturing companies and vendors. The platform also serves qualified and satisfactory production opportunities for buyers and producers by meeting two key factors: early delivery date and fulfillment of processing as many orders as possible. The genetic algorithm (GA)-based scheduling method results indicated that the proposed platform enables SME manufacturers to obtain optimized schedules by solving the job shop scheduling problem (JSSP) by comparing with the real-world data from a textile weaving factory in South Korea. The proposed platform will provide producers with an optimal production schedule, introduce new producers to buyers, and eventually foster relationships and mutual economic interests.     

Methodology for capturing and formalizing DFM Knowledge

Design for manufacturing (DFM) practices lead to more competitive products from the point of view of cost, development time and quality. However, the success of considering manufacturing issues during design process would be higher if manufacturing information was more readily available and designers needed less experience to select information relevant to DFM.