Material Flow Analysis and Occupational Exposure Assessment in Additive Manufacturing End-of-Life Material Management.

Assessing cyber resilience of additive manufacturing supply chain leveraging data fusion technique: A model to generate cyber resilience index of a supply chain

Additive Manufacturing (AM) methods have become increasingly efficient and industrially viable in the past ten years. These methods offer the freedom of complexity to the designers and choices of localized and pull-based production system to the managers. These propositions of AM have been enabling custom manufacturing and are catalysts for rapid growth of additive manufacturing (AM). This paper analyzes the general characteristics of AM supply chain and proposes three AM supply chain models based on the specific nature of the industry. Our description of the models emphasizes on adopting an holistic view of the AM supply chain and therefore includes raw material, printer hardware and the virtual supply chain. Throughout the product life cycle of additively manufactured products, the interlacing of the virtual supply chain ( digital thread ) with the physical supply chain and their operations fundamentally make the AM process a cyber-physical system (CPS). Therefore, the technology brings along with it benefits of a CPS as well as a new class of attack vectors. We discuss the possible attacks (printer, raw material and design level), risks (reverse engineering, counterfeiting and theft) and provide an enhanced risk classification scheme. We contend that the traditional cybersecurity methods need to evolve to address the new class of attack vectors that threaten the AM supply chain and also discuss the nature of existing solutions that help in addressing the risks and attack threats. In providing an holistic view of the AM supply chain the interdependencies of the processes in the AM supply chain are presented and we elucidate the effects of local attack vectors on the entire supply chain. Further, we discuss the existing security measures to mitigate the risk and identify the existing gap in AM security that needs to be bridged.

Sustainable additive manufacturing supply chains with a plithogenic stakeholder analysis: Waste reduction through digital transformation

Hybrid manufacturing—Locating AM hubs using a two-stage facility location approach

This study proposes a novel optimization framework that simultaneously considers interdependence of flow networks, resource restrictions, and process-and-system level costs under a unified decision framework for the design and management of an integrated Additive Manufacturing (AM) supply chain network. A two-stage stochastic programming model is proposed that minimizes the facility location and capacity selection decisions at the first-stage prior to realizing any customer demand information. However, after the demand information is revealed, a number of second-stage decisions, such as optimal layer thickness for AM products, production, post-processing, procurement, storage, and transportation decisions, are made. Due to the need to solve our proposed optimization framework in a realistic-size network problem, a hybrid decomposition algorithm, combining the Sample Average Approximation algorithm with an Adaptive Large Neighborhood Search algorithm, is proposed. The performance of the proposed algorithm is validated by developing a case study using data from Alabama and Mississippi. Based on a set of numerical experiments, the effect of process-and-system level factors on the design and management of an AM supply chain network are analyzed. Numerous managerial insights, particularly on layer thickness, customer demand variability, mean demand variation, powder safety stock, and wastage rate on overall system performance, are gained which are crucial for the sustainment of this new manufacturing and supply chain paradigm.

Additive manufacturing (AM) technology has the potential to significantly improve supply chain dynamics, reduce shipping costs and shorten delivery lead times. Using AM technology, manufacturers can produce parts on demand and thus reduce the need of maintaining safety inventory. This is especially useful in the aircraft spare parts industry where currently there is a need to maintain a high level of safety inventory for high-cost long-lead time metallic parts. Therefore, more and more companies in the aerospace industry are interested in using AM technology. There are different approaches to configure the aircraft spare parts supply chain using AM technology. This paper evaluates the impact of AM in the aircraft spare parts supply chain based on the well-known supply chain operation reference model. Three supply chain scenarios are investigated; namely, conventional (as-is) supply chain, centralized AM supply chain and distributed AM supply chain. A case study is conducted based on data obtained in the literature. The result shows that the use of AM will bring various opportunities for reducing the required safety inventory of aircraft spare parts in the supply chain. A sensitivity analysis is performed and some key factors affecting the choice of AM scenarios are studied.

Specific energy consumption based comparison of distributed additive and conventional manufacturing: From cradle to gate partial life cycle analysis

This chapter aims to investigate the potential economic and environmental sustainability outcomes of additive manufacturing (AM) for spare parts logistics. System dynamic simulation was conducted to analyze the sustainability of producing a spare part used in a railways subsystem using a particular additive manufacturing (AM) technology (i.e., selective laser sintering [SLS]) compared to producing it using injection molding. The results of the simulation showed that using SLS for the chosen part is superior to the conventional one in terms of total variable costs as well as for carbon footprint. Compared to the conventional supply chain, for the AM supply chain, the costs of the supplier reduces by 46%, that of the railways company reduces by 71%, while the overall supply chain costs reduce by 61.9%. The carbon emissions in the AM supply chain marginally reduces by 2.89% compared to the conventional supply chain.

PurposeAdopting additive manufacturing (AM) can be challenging, especially in small- and medium-sized enterprises (SMEs) and as part of the supply chains of larger firms. The purpose of this paper is to explore SMEs’ perspectives on the adoption of AM in their specific supply chain positions. The paper develops new knowledge on the challenges SMEs face across the supply chain and the actions they need to promote the adoption of AM.Design/methodology/approachAn exploratory interview-based research design is used. In total, 17 interviews were conducted and analyzed in four types of SMEs in their specific positions in AM supply chains. The challenges of adopting AM were mapped, and actions to promote AM adoption were identified.FindingsSMEs in different supply chain positions experience different challenges when adopting AM. Strategic and operative actions are suggested as key solutions to overcome the challenges. The benefits of AM on a large scale will be achieved only if the broader supply chain adopts AM technology and experiences its benefits.Research limitations/implicationsThe research is limited by its single-country context, its focus on SMEs, and the selection of early-phase AM-adopter firms. The findings imply a need to understand AM adoption as a shared concern and systemic innovation in the supply chain, instead of just a firm-specific implementation task.Practical implicationsThe findings offer a framework for categorizing AM adoption challenges and propose ways to overcome the challenges of adoption.Originality/valueThe study reveals that AM adoption is not only a technology issue, but also an issue of strategic, organizational and operational challenges across the supply chain. It shows that when adopting AM, SMEs face particular challenges and require specific solutions according to their supply chain position.

Purpose This paper aims to investigate the current state, technological challenges, economic opportunities and future directions in the growing “indirect” hybrid manufacturing ecosystem, which integrates traditional metal casting with the production of tooling via additive manufacturing (AM) process including three-dimensional sand printing (3DSP) and printed wax patterns. Design/methodology/approach A survey was conducted among 100 participants from foundries and AM service providers across the USA to understand the current adoption of AM in metal casting as a function of engineering specifications, production demand, volume and cost metrics. In addition, current technological and logistical challenges that are encountered by the foundries are identified to gather insight into the future direction of this evolving supply chain. Findings One of the major findings from this study is that hard tooling costs (i.e. patterns/core boxes) are the greatest challenge in low volume production for foundries. Hence, AM and 3DSP offer the greatest cost-benefit for these low volume production runs as it does not require the need for hard tooling to produce much higher profit premium castings. It is evident that there are major opportunities for the casting supply chain to benefit from an advanced digital ecosystem that seamlessly integrates AM and 3DSP into foundry operations. The critical challenges for adoption of 3DSP in current foundry operations are categorized into as follows: capital cost of the equipment, which cannot be justified due to limited demand for 3DSP molds/cores by casting buyers, transportation of 3DSP molds and cores, access to 3DSP, limited knowledge of 3DSP, limitations in current design tools to integrate 3DSP design principles and long lead times to acquire 3DSP molds/cores. Practical implications Based on the findings of this study, indirect hybrid metal AM supply chains, i.e. 3DSP metal casting supply chains is proposed, as 3DSP replaces traditional mold-making in the sand casting process flow, no/limited additional costs and resources would be required for qualification and certification of the cast parts made from three-dimensional printed sand molds. Access to 3DSP resources can be addressed by establishing a robust 3DSP metal casting supply chain, which will also enable existing foundries to rapidly acquire new 3DSP-related knowledge. Originality/value This original survey from 100 small and medium enterprises including foundries and AM service providers suggests that establishing 3DSP hubs around original equipment manufacturers as a shared resource to produce molds and cores would be beneficial. This provides traditional foundries means to continue mass production of castings using existing hard tooling while integrating 3DSP for new complex low volume parts, replacement parts, legacy parts and prototyping.

Additive manufacturing (AM) offers unique opportunities for agile and flexible manufacturing; however, there are concerns that AM supply chains may present risks in the form of inferior materials, poor process control, or counterfeit parts. There is a need for new technologies capable of tracking and validating AM parts and materials. This study demonstrates a method to predict the source of AM parts using images captured by a smartphone and analyzed using deep learning models. For this study, 918 parts were manufactured using industrial fused deposition modeling (FDM) machines from six contract manufacturers. The parts include nine different designs representative of common industrial parts with diverse geometric features. A smartphone camera collected high-resolution photographs of each part with consistent lighting. The smartphone images show minimal visual differences between parts that are produced by different suppliers, and it is not possible for a human to identify the manufacturing source by viewing the images or inspecting the parts. A deep machine learning (ML) model was trained to predict the supplier that produced each part. The part images are 4000 × 3000 pixels in size, which is typical for a smartphone but much higher than typical computer vision deep learning models that use images that are 224 × 224 pixels. A novel sampling strategy allows the high-resolution data to be interpreted with deep learning. The source identification model achieves 99.9% accuracy identifying the origin of 459 testing parts across the six suppliers and the nine part designs. The model is data driven, recognizing patterns from raw smartphone images, removing the need for manually designed or specialized features extracted with computation geometry. The model also demonstrates design generalizability and can extrapolate to make predictions for part designs never previously seen by the model. When the training dataset consists of five part designs, the model achieves 98.8% accuracy when applied to four previously unseen part designs. The source identification model is also data efficient, enabling the rapid addition of new manufacturers into the system with minimal training data. When trained using only 10 parts per manufacturer, the model achieves over 90% accuracy. Increasing the training set to 30 parts per manufacturer allows the model to exceed 95% accuracy. The model can authenticate the source of parts without the cooperation of the supply chain enabling anti-counterfeiting methods by detecting if parts originate from different sources or if the supplier has altered the production process. This research demonstrates the potential of source identification using simple mobile imaging to track the quality and authenticity of AM parts.

Background: Additive manufacturing (AM) applications in producing spare parts are increasing day by day. AM is bridging the digital and physical world as a 3D computer-aided manufacturing (CAM) method. The usage of AM has made the supply chain of the aviation spare parts industry simpler, more effective, and efficient. Methods: This paper demonstrates the impacts of AM on the supply chain of the aircraft spare parts industry following a systematic literature review. Hence, centralized and decentralized structures of AM supply chains have been evaluated. Additionally, the attention has been oriented towards the supply chain with AM technologies and industry 4.0, which can support maintenance tasks and the production of spare parts in the aerospace industry. Results: This review article summarizes the interconnection of the industry findings on spare parts. It evaluates the potentiality and capability of AM in conceptualizing the overall supply chain. Moreover, MROs can adopt the proposed framework technologies to assist decision-makers in deciding whether the logistics hub with AM facilities is centralized or decentralized. Conclusions: Finally, this review provides an overall view to make critical decisions on the supply chain design of spare parts driven by new and disruptive technologies of industry 4.0. The next-generation supply chain may replace the logistics barriers by reducing waste and improving capability and sustainability by implementing AM technologies.

In recent years, additive manufacturing (AM) technology, also known as 3D printing, has revolutionized various industries by enabling the production of customized products through a layer-by-layer material addition process. This paper focuses on the collaborative optimization of distributed production and order delivery in AM supply chains. We introduce a mathematical model and a genetic algorithm to minimize production and transportation costs while improving efficiency and service levels. The model addresses order assignments and batch scheduling of 3D printers, also vehicle routing for order delivery. Numerical experiments demonstrate the effectiveness of our method, showing significant improvements in solving both small-scale and large-scale instances. The results confirm that the proposed method provides high-quality solutions, validating its applicability in real-world AM supply chain scenarios. Through the distributed production mode, enterprises can optimize order assignment and vehicle route planning to improve production and delivery efficiency.

The disruptive impact of additive manufacturing on supply chains: A literature study, conceptual framework and research agenda

Although separation of product design from manufacturing capabilities is a major advantage of Additive Manufacturing (AM), the impact of AM is not only limited to the design and manufacturing stages. In addition to the freedom of design such as elimination of design constraints, material saving, and free complexity, AM offers other potential benefits to the manufacturing industry as well. One of the most immediate potentials of AM is the possibility of more efficient logistics. This paper aims at describing the characteristics and requirement of a Supply Chain (SC) as well as the changes AM will bring into the current structure of supply chain. Insights are provided on the transformative effects of AM on traditional businesses, and how these changes impact the configuration of a supply chain. The potential for using simulation tools to evaluate AM supply chain have been discussed. Further, two examples of Agent Based Simulation (ABS) and System Dynamics (SD) have been provided to show the application of simulation models. The ABS results show the possibility of lead time reduction in AM based supply chain. In addition, the SD model illustrates the potential for less ‘pipeline’ effect in AM compared to traditional supply chain.