Additive manufacturing in the spare parts supply chain

As additive manufacturing (AM) evolves to become a common method of producing final parts, further study of this computer integrated technology is necessary. The purpose of this research is to evaluate the potential impact of additive manufacturing improvements on the configuration of spare parts supply chains. This goal has been accomplished through scenario modeling of a real-life spare parts supply chain in the aeronautics industry. The spare parts supply chain of the F-18 Super Hornet fighter jet was selected as the case study because the air-cooling ducts of the environmental control system are produced using AM technology. In total, four scenarios are investigated that vary the supply chain configurations and additive manufacturing machine specifications. The reference scenario is based on the spare parts supplier's current practice and the possible future decentralization of production and likely improvements in AM technology. Total operating cost, including downtime cost, is used to compare the scenarios. We found that using current AM technology, centralized production is clearly the preferable supply chain configuration in the case example. However, distributed spare parts production becomes practical as AM machines become less capital intensive, more autonomous and offer shorter production cycles. This investigation provides guidance for the development of additive manufacturing machines and their possible deployment in spare parts supply chains. This study contributes to the emerging literature on AM deployment in supply chains with a real-world case setting and scenario model illustrating the cost trade-offs and critical requirements for technology development.     

Design of agile supply chain assessment model and its case study in an Indian automotive components manufacturing organization

Agile Manufacturing (AM) paradigm is fast instilled in modern organizations. AM enables an organization to evolve products and services quickly and economically in response to the customers’ dynamic demands. The effectiveness of AM is largely determined by the performance of Agile Supply Chains (ASC). In order to assess their performance, an ASC assessment model was reported in this research paper. This model is encompassed with agile supply chain attributes whose performance levels need to be determined for assessing the overall ASC performance of the organization. The computation was performed using fuzzy logic approach. The working of this model was examined by conducting a case study in an Indian automotive components manufacturing organization. The experience gained by conducting this case study favored the use of a computerized system which will ensure accuracy of computations involving fuzzy logic.     

Stable matching of customers and manufacturers for sharing economy of additive manufacturing

With rapid advances in internet and computing technologies, sharing economy paves a new way for people to “share” assets and services with others that disrupts traditional business models across the world. Specifically, rapid growth of additive manufacturing (AM) enables individuals and small manufacturers to own machines and share under-utilized resources with others. Such a decentralized market calls upon the development of new analytical methods and tools to help customers and manufacturers find each other and further shorten the AM supply chain. This paper presents a bipartite matching framework to model the resource allocation among customers and manufacturers and leverage the stable matching algorithm to optimize matches between customers and AM providers. We perform a comparison study with Mix Integer Linear Programming (MILP) optimization as well as the first-come-first-serve (FCFS) allocation strategy for different scenarios of demand-supply configurations (i.e., from 50% to 500%) and system complexities (i.e., uniform parts and manufacturers, heterogeneous parts and uniform manufacturers, heterogeneous parts and manufacturers). Experimental results show that the proposed framework has strong potentials to optimize resource allocation in the AM sharing economy.     

Eco-friendly additive manufacturing of metals: Energy efficiency and life cycle analysis

Additive manufacturing (AM) of metal materials has attracted widespread attention and is shifting the conventional manufacturing landscape toward free-form processes. With increasing concerns about global sustainability, eco-consideration is highly encouraged to be integrated into AM processes. This review provides a comprehensive and timely discussion on the life cycle of metal parts fabricated through AM. The energy consumption required for raw metal material extraction and subsequent AM processes is analyzed. The eco-design and energy efficiency of metal AM are evaluated to reveal the role of manufacturing methods, machine subsystems, and post-processing modes in the eco-integration. AM-induced supply chain management, utilization, and recycling of the printed metal structure are also analyzed. Finally, a comprehensive life cycle assessment regarding the environmental, social, and economic impacts of metal AM is also addressed. Future directions of AM are also briefly discussed to provide insight and vision on the emerging field of additive eco-manufacturing.     

In-situ point cloud fusion for layer-wise monitoring of additive manufacturing

Additive manufacturing (AM) has received an increasing attention in the manufacturing sector, owing to its high-level design freedom and enhanced capability to produce parts with complex geometries. With advances in AM technologies, the role of AM has been shifting from rapid prototyping to viable production-worthy manufacturing of functional parts. However, AM processes are highly inconsistent, and the lack of quality assurance significantly hampers the broader adoption of AM. Most existing techniques for AM online monitoring focus on the detection of conspicuous defects, such as under-fills and cracks. They are limited in their ability to detect layer surface variations induced by miniature process shifts. The objective of this study is to develop a new layer-wise monitoring framework for AM quality assurance based on in-situ point cloud fusion. Specifically, online 3D structured-light scanning is used to capture the surface morphology from each printed layer. The collected point cloud is partitioned, and the morphological patterns in local regions are delineated with a new affinity measure to evaluate the conformity to the reference. A deep cascade model is further introduced to leverage the local affinities for the identification of abnormal patterns on the printed layers. Finally, a statistical control chart is constructed for process monitoring and the identification of miniature shifts. Simulation and real-world case studies using the fused filament fabrication (FFF) process are conducted, and experimental results have demonstrated the effectiveness of the developed framework. It has a great potential to be implemented in diverse AM processes with a wide variety of materials for mission-critical applications.     

Additively manufactured respirators: quantifying particle transmission and identifying system-level challenges for improving filtration efficiency

The COVID-19 pandemic has disrupted the supply chain for personal protective equipment (PPE) for medical professionals, including N95-type respiratory protective masks. To address this shortage, many have looked to the agility and accessibility of additive manufacturing (AM) systems to provide a democratized, decentralized solution to producing respirators with equivalent protection for last-resort measures. However, there are concerns about the viability and safety in deploying this localized download, print, and wear strategy due to a lack of commensurate quality assurance processes. Many open-source respirator designs for AM indicate that they do not provide N95-equivalent protection (filtering 95% of SARS-CoV-2 particles) because they have either not passed aerosol generation tests or not been tested. Few studies have quantified particle transmission through respirator designs outside of the filter medium. This is concerning because several polymer-based AM processes produce porous parts, and inherent process variation between printers and materials also threaten the integrity of tolerances and seals within the printed respirator assembly. No study has isolated these failure mechanisms specifically for respirators. The goal of this paper is to measure particle transmission through printed respirators of different designs, materials, and AM processes. The authors compare the performance of printed respirators to N95 respirators and cloth masks. Respirators in this study printed using desktop- and industrial-scale fused filament fabrication processes and industrial-scale powder bed fusion processes were not sufficiently reliable for widespread distribution and local production of N95-type respiratory protection. Even while assuming a perfect seal between the respirator and the user’s face, although a few respirators provided >90% efficiency at the 100−300 nm particle range, almost all printed respirators provided <60% filtration efficiency. Post-processing procedures including cleaning, sealing surfaces, and reinforcing the filter cap seal generally improved performance, but the printed respirators showed similar performance to various cloth masks. The authors further explore the process-driven aspects leading to low filtration efficiency. Although the design/printer/material combination dictates the AM respirator performance, the identified failure modes originate from system-level constraints and are therefore generalizable across multiple AM processes. Quantifying the limitations of AM in producing N95-type respiratory protective masks advances understanding of AM systems toward the development of better part and machine designs to meet the needs of reliable, functional, end-use parts.     

Attribute driven process architecture for additive manufacturing

In additive manufacturing (AM) process, the manufacturing attributes are highly dependent upon the execution of hierarchical plan. Among them, material deposition plan can frequently interrupt the AM process due to tool-path changes, tool start-stop and non-deposition time, which can be challenging during free-form part fabrication. In this paper, the layer geometries for both model and support structure are analyzed to identify the features that create change in deposition modality. First, the overhanging points on the part surface are identified using the normal vector direction of the model surface. A k-th nearest point algorithm is implemented to generate the 3d boundary support contour which is used to construct the support structure. Both model and support structures are sliced and contours are evaluated. The layer contour, plurality, concavity, number of contours, geometric shape, size and interior islands are considered to generate an AM deposition model. The proposed model is solved for minimizing the change in deposition modality by maximizing the continuity and connectivity in the material deposition plan. Both continuity and connectivity algorithms are implemented for model and support structure for free-form object. The proposed algorithm provides the optimum deposition direction that results in minimum number of tool-path segments and their connectivity while minimizing contour plurality effect. This information is stored as a generic digital file format named Part Attributable Motion (PAM). A common application program interface (API) platform is also proposed in this paper, which can access the PAM and generate machine readable file for different existing 3D printers. The proposed research is implemented on three free-form objects with complex geometry and parts are fabricated. Also, the build time is evaluated and the results are compared with the available 3d printing software.     

Process energy analysis and optimization in selective laser sintering

Additive manufacturing (AM) processes are increasingly being used to manufacture complex precision parts for the automotive, aerospace and medical industries. One of the popular AM processes is the selective laser sintering (SLS) process which manufactures parts by sintering metallic, polymeric and ceramic powder under the effect of laser power. The laser energy expenditure of SLS process and its correlation to the geometry of the manufactured part and the SLS process parameters, however, have not received much attention from AM/SLS researchers. This paper presents a mathematical analysis of the laser energy required for manufacturing simple parts using the SLS process. The total energy expended is calculated as a function of the total area of sintering (TAS) using a convex hull based approach and is correlated to the part geometry, slice thickness and the build orientation. The TAS and laser energy are calculated for three sample parts and the results are provided in the paper. Finally, an optimization model is presented which computes the minimal TAS and energy required for manufacturing a part using the SLS process.     

Cyber coordinated simulation for distributed multi-stage additive manufacturing systems

Additive Manufacturing (AM) processes have been increasingly used to manufacture energy storage products with dedicated material preparation and post-processing stages to enhance product properties. Most researchers focus on selecting materials and improving processes, yet the system modeling and management has not been investigated so far. This paper extends the conventional single-stage AM processes to multi-STage distRibutEd AM (STREAM) systems. In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the post-processing stage. Modeling and managing such complex systems have been challenging. We propose a novel framework for “cyber-coordinated simulation” to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where the single-stage, multi-stage, and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from Freeze Nano Printing (FNP) AM for the fabrication of energy storage products.     

Multi-source data analytics for AM energy consumption prediction

The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches.     

基于OpenGL的VRML文件格式的解析

对于三维CAD导出的VRML格式文件可分为显示几何模型数据和零件场景描述两部分,在对其详细分析的基础上,通过OpenGL对其解析,并重新构建产品模型,为虚拟制造后续各环节的研究和开发提供了三维平台。     

Additive manufacturing and the COVID-19 challenges: An in-depth study

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rapidly achieved global pandemic status. The pandemic created huge demand for relevant medical and personal protective equipment (PPE) and put unprecedented pressure on the healthcare system within a very short span of time. Moreover, the supply chain system faced extreme disruption as a result of the frequent and severe lockdowns across the globe. In such a situation, additive manufacturing (AM) becomes a supplementary manufacturing process to meet the explosive demands and to ease the health disaster worldwide. Providing the extensive design customization, a rapid manufacturing route, eliminating lengthy assembly lines and ensuring low manufacturing lead times, the AM route could plug the immediate supply chain gap, whilst mass production routes restarted again. The AM community joined the fight against COVID-19 by producing components for medical equipment such as ventilators, nasopharyngeal swabs and PPE such as face masks and face shields. The aim of this article is to systematically summarize and to critically analyze all major efforts put forward by the AM industry, academics, researchers, users, and individuals. A step-by-step account is given summarizing all major additively manufactured products that were designed, invented, used, and produced during the pandemic in addition to highlighting some of the potential challenges. Such a review will become a historical document for the future as well as a stimulus for the next generation AM community.     

A kinematics-aware part clustering approach for part integration using additive manufacturing

Part integration is to integrate parts to be a fabrication and assembly unit. It can effectively reduce the fabrication and assembly unit quantity of a product and has been deemed as an effective way to promote the productivity of manufacturing. Although additive manufacturing (AM) has great potential to further promote the part integration for any product (assembly) model, part integration works using AM at present are often ad hoc, human-dependent and time-consuming. One main cause for this problem is that determining which parts in an assembly model can be integrated to be a fabrication and assembly unit automatically is still very difficult, especially when the model has kinematics (inner relative motions embodied by kinematic joints). In this paper, a novel part clustering approach is proposed, based on which, an input assembly model can smartly cluster all its parts to fewer sub-assembly models (each of them fits being integrated to be a fabrication and assembly unit in AM) according to its kinematics. To ensure that the input model after part integration can effectively realize its kinematics using AM, the criteria for part clustering are first defined. Accompanying with the criteria, the methods to determine the kinematics-related fabrication orientation for each part are proposed based on heuristic rules. Then, to make an accurate and efficient part clustering, an attributed part kinematic graph is put forward according to the above criteria. After that, by breaking through the detection automation challenges in sealing support structure and assembly feasibility, an efficient optimization objective function is defined based on the above criteria and graph. Finally, integrating a new adaptive perturbation strategy into the particle swarm optimization algorithm to avoid premature convergence, a novel graph-based part clustering optimization method is designed to cluster all the parts of the input model to be a high-quality (optimized) set of the above-mentioned sub-assembly models. Experiments and analyses are presented to verify the advantages of the proposed approach. Besides, complying with the general guidelines in AM, the proposed approach provides great potential to maximize part integration using AM in a wider application.     

A qualification procedure to manufacture and repair aerospace parts with electron beam melting

This paper is focused on qualification procedures for metal parts manufactured using new additive manufacturing (AM) techniques in the aerospace industry. The main aim is to understand the interaction between these technologies and the stringent regulatory framework of this industry in order to develop correct quality assurance and quality control procedures in accordance with the certification process for the technology and spare parts. These include all the testing and validation necessary to implement them, as well as to maintain their capability throughout their life-cycle, specific procedures to manufacture or repair parts, work-flows and records, amongst others. An entire qualification procedure for electron beam melting (EBM) to reproduce and repair an aerospace part has been developed and it is presented in this paper. These will be part of the future quality assurance and quality management systems of those aerospace companies that implement AM in their supply chain.     

Intelligent Jig System to Automate Flexible Manufacturing System

Jig pallet systems are intended for the automatic, complete machining or assembly of parts families in the area of medium and large scale manufacturing. Their distinctive feature is that several machine tools, or assembly machines, are linked together to generate an overall system by means of common tool and workpiece supply with integrated computer control. A jig pallet system is considered to be intelligent, if its central processor is equipped with a knowledge-base and an inference-engine. A jig pallet system was structured. It consists of a central processor, tools supply system, workpiece supply system, manufacturing cell which includes four work stations and a local area network. A knowledge-base and inference-engine were developed to reason the next position of jig pallet systems. The jig pallet system, with its incorporated knowledge-base and inference-engine, was tested for a large variety of operational parameters to explore the ability of the central processor to control participant's operations. The conclusion which is derived from these tests is that the central processor can control and optimize participant's operation in real time with minor effects on the system efficiency.     

Sketch‐to‐Design: Context‐Based Part Assembly

Designing 3D objects from scratch is difficult, especially when the user intent is fuzzy and lacks a clear target form. We facilitate design by providing reference and inspiration from existing model contexts. We rethink model design as navigating through different possible combinations of part assemblies based on a large collection of pre‐segmented 3D models. We propose an interactive sketch‐to‐design system, where the user sketches prominent features of parts to combine. The sketched strokes are analysed individually, and more importantly, in context with the other parts to generate relevant shape suggestions via adesign galleryinterface. As a modelling session progresses and more parts get selected, contextual cues become increasingly dominant, and the model quickly converges to a final form. As a key enabler, we use pre‐learned part‐based contextual information to allow the user to quickly explore different combinations of parts. Our experiments demonstrate the effectiveness of our approach for efficiently designing new variations from existing shape collections.     

An integrated restoration methodology based on adaptive failure feature identification

Remanufacturing is an emerging eco-friendly industry because it consumes less energy, cost, and material to manufacture like-new parts with a warranty to match. However, restoration processes are ad-hoc and complex because the "raw" materials for remanufacturing are returned used parts, which exhibit significant uncertainties in failure features involving failure location, failure mode, failure volume, and failure degree. Thus, customized remanufacturing process planning (RPP) and restoration tool paths should be generated to restore the defects for each part. An integrated restoration methodology based on adaptive failure feature identification for remanufacturing is proposed to enable efficient and cost-effective remanufacturing. In this study, an adaptive failure feature identification algorithm is developed to identify the failure features on defective parts quickly. In this stage, the point clouds of the nominal model and defective model are used to extract defective regions through Boolean operations and then calculate the failure volume and degree. Based on the identified failure features, a knowledge reuse algorithm is proposed to retrieve the optimal RPP rapidly through mixed case-based reasoning (CBR) and rule-based reasoning (RBR). Finally, a tool path generation algorithm of hybrid Subtractive Manufacturing (SM) and Additive Manufacturing (AM) for the restoration of identified defects. The proposed methodology is verified by remanufacturing a defective blade with multi-defects and is approved to be flexible and effective.     

Coordinating product, process, and supply chain decisions: A constraint satisfaction approach

The importance of coordinating product, process, and supply chain (PPSC) decisions has received much attention and popularity in academia and industry alike. This paper formulates PPSC coordination as a factory loading allocation problem (FLAP) from a constraint satisfaction perspective. A domain-based FLAP reference model is proposed for the conceptualization of a multi-site manufacturing supply chain, considering multiple domains, network structures, product characteristics, decision variables, along with various constraints. A decision propagation structure (DPS) incorporating with a connectionist approach is developed based on the concept of constraint heuristic search to facilitate the exploration of solution spaces. A case study in a multi-national company is presented to illustrate the FLAP framework, which implies practical insights into PPSC coordination.     

MRP-controlled manufacturing environment disturbed by uncertainty

Most identified research on uncertainty in material requirements planning (MRP)-controlled manufacturing environment, while purporting to represent such environment, does not truly model a multi-level dependent demand system, with multi-product and controlled by planned order release (POR) schedule based on planned lead times. This research has developed an MRP-controlled batch manufacturing simulation model using ARENA simulation software to represent such environment. In this paper, the delivery performance of the environment, measured with parts delivered late (PDL) and finished products delivered late (FPDL), when disturbed by uncertainty is examined. Design of experiments and simulation studies are carried out for these uncertainties: external late supply, internal late supply, planned set-up time exceeded, machine breakdowns, labour unavailability, tooling unavailability, demand batch size enlargement and customer design changes. Analysis of Variance (ANOVA) results identify significant effects from individual uncertainty as well as combination of uncertainties. The knock-on and the compound effects cause more PDL and FPDL. The main finding is that the higher is the level of the significant uncertainties, the higher is the level of PDL and FPDL. It is suggested that MRP-controlled manufacturing enterprise must diagnose for the significant uncertainties, so that optimum use of buffer or slack can be implemented. Some practical implications of this work and required further work are also discussed.     

敏捷制造ERP中的关键优化问题及其数学模型

敏捷制造企业对供应链计划、动态联盟的组成与风险利润分配等多种功能的需要，带来了一系列新的优化问题，研究了对其中的几个相关的优化问题，建立了新产品投入时间计划，动态结盟的伙伴企业挑选、供应链的分肖网络设计，风险防范投资，分布式多工厂生产计划，以及分布式仓库存储控制等问题的数学模型，对模型可能的求解方法进行了探讨，模型与算法已成功应用到开发中的面向敏捷制造的ERP系统中。