Additive Manufacturing Case Research Articles

Improvement of the Thermal Performance of a Drilling Tool Case by Using Metal Additive Manufacturing

Metal Additive Manufacturing (AM) has been established as one of the most promising solutions when producing new components due to its advantages in creating complex geometries and adding new functionality to the parts. An example of the last, is the usage of conformal cooling in the tooling industry to improve the thermal behaviour of components in-service. The application of this solution is even more meaningful when using metallic materials with high thermal conductivities (e.g. Aluminium alloys). These are mainly selected when the final weight is a key factor in the final component performance, compared with other conductive materials like copper alloys. This study presents the improvement of the thermal behaviour of a drilling tool case manufactured in Aluminium. The selected application case lacks the possibility of evacuating the heat produced by an engine. A redesign of the component is presented, considering the advantages of Powder Bed Fusion – Laser/Metal (PBF-L/M). Both passive and active heat dissipation are analysed by including the reticular structure and internal cavities with forced air. The study is performed first at coupon level and later in the redesigned case manufactured by PBF-L/M. Infrared Thermography (IRT) inspections are conducted to investigate the thermal dissipation when heating the components, and also to monitor the cooling down process. A comparative thermal analysis between the initial case manufactured by the conventional process and the redesigned AM case is also presented.

A Review of Conventional versus Additive Manufacturing for Metals: Life-Cycle Environmental and Economic Analysis

Additive manufacturing (AM) is a recent emerging technology that is being adopted in various industry sectors and supply chains. Life cycle assessment (LCA) and life cycle costing (LCC) are powerful methods that can be used for assessing the environmental and economic performance of emerging manufacturing technologies. This study aims to evaluate the life cycle environmental impacts and cost of computerized numerical control-based (CNC) manufacturing and direct metal laser sintering technology (DMLS) through a cradle-to-gate life cycle analysis. This research has four main novel elements: (i) conducting a recent comprehensive review of metal AM and conventional manufacturing (CM) processes using a systematic method and meta-analysis (ii) comparing the conventional process “CNC machining” and the additive technology “direct metal laser sintering” from the environmental (LCA) and economic (LCC) perspectives, (iii) investigating the influence of geometry complexity and shape size factors on the environmental and cost performance of both manufacturing methods, and (iv) conducting a Monte Carlo simulation-based sensitivity analysis to tackle uncertainty in LCC input parameters. The midpoints and endpoints impact for CNC and AM processes were estimated using the Ecoinvent v3.8 database and ReCiPe (E) impact assessment method. The review revealed that global warming potential is one of the most widely studied environmental indicators; however, only 6% of the studies have investigated the life cycle economic impacts of AM technologies using sensitivity and uncertainty analysis. The results have shown that in terms of ReCiPe endpoints, DMLS has the highest environmental impact on human health while CM has more impact on the eco-system quality. Electricity consumption is the main contributor to environmental impact categories in both manufacturing technologies. This is due to the high electricity utilization for casting and milling conventionally manufactured parts and printing the AM parts. LCC net present values revealed that manufacturing all parts with AM costs 91% more compared to CNC. The LCC analysis has shown that AM is more suitable and cost-effective for parts with highly complex geometries. Whereas CNC machining was found to be economically feasible for large-sized and low-complexity parts. The Monte Carlo sensitivity analysis demonstrated that for the case of AM, the most significant parameter is the processing cost followed by material cost, which highlighted the importance of energy-efficient AM and dematerialization through design for circularity.

Stability in Reinforcement Learning Process Control for Additive Manufacturing

Reinforcement Learning (RL), as a machine learning paradigm, receives increasing attention in both academia and industry, in particular for process control. Its trial-and-error concept, along with its data-driven nature, make RL suitable for process control in complex tasks, where the control task and framework can be formulated flexibly. However, there are still challenges that need to be addressed in order for RL to be introduced into the control mainstream, in particular for critical processes. A major challenge in RL is that there is no guarantee for robust, stable RL process control. This is a key impediment to RL implementation on tasks for which stability is an important requirement. Additive Manufacturing (AM) is an example of such process control task, since the very high complexity of the manufacturing process makes it suitable for RL process control, while stable performance is a necessity. However, one has to firstly understand performance and stability as a key requirement. In this paper, we reflect on stability approaches in RL and we investigate the stability requirements for AM. Our AM case study provides intuition and encourages further research for stable RL that would unlock potential for adoption and implementation of RL in AM applications. Research in the proposed direction would also have the potential for impacts to other process control sectors, in critical applications, where appropriate utilisation of stable RL control could bring significant advantages.

Make‐or‐buy decisions for industrial additive manufacturing

AbstractMuch of the potential of industrial additive manufacturing (AM) is said to lie in the digital specification of components that can be transmitted seamlessly and unambiguously to partners fostering flexible outsourcing. In industry, we observe nuanced AM supply chain governance structures that result from make‐or‐buy decisions, with a tendency to implement AM in‐house. Thus, there is a discrepancy between what is discussed in the literature and implemented in practice. We apply a multiple‐case study approach to investigate why and how AM impacts the make‐or‐buy decision of manufacturing firms. We identify four decision profiles demonstrating the spectrum of specific governance structures and develop a framework to explain the underlying rationales. We find strong arguments for in‐house AM including firms’ perceived need to protect their digitally encapsulated intellectual property, reevaluation of their core competencies, commitment to internal learning, and senior management's enthusiasm for AM. By using transaction cost economics and the resource‐based view, we contribute to the understanding of how arguments of these general theories are modified by the digital and emerging traits of AM. We reveal contradicting guidance in the theories’ argumentation for the case of AM and provide managers a clear perspective on alternative strategies for their AM implementation process.

A Blockchain-Based G-Code Protection Approach for Cyber-Physical Security in Additive Manufacturing

Abstract As an emerging technology, additive manufacturing (AM) is able to fabricate products with complex geometries using various materials. In particular, cyber-enabled AM systems have recently become widely applied in many real-world applications. It significantly improves the flexibility and productivity of AM but poses the system under high risks of cyber-physical attacks. For example, cyber-physical attack could maliciously tamper the product design and process parameters, which, in turn, leads to significant alteration of the desired properties in AM products. Therefore, there is an urgent need in incorporating advanced technologies to improve the cyber-physical security for the cyber-enabled AM systems. In this study, two common types of cyber-physical attacks regarding the G-code security were investigated, namely, unintended design modifications and intellectual property theft. To effectively secure the G-code against these two attacks, a new methodology is developed in this study, which consists of a novel blockchain-based data storage approach and an effective asymmetry encryption technique. The proposed method was also applied to a real-world AM case for ensuring the cyber-physical security of the face shield fabrication, which is critical during the COVID-19 pandemic. Based on the proposed methodology, malicious tampering can be accurately detected in time, and meanwhile, the risk of unauthorized access of the G-code file is greatly eliminated as well.

Theoretical modelling and prediction of surface roughness for hybrid additive–subtractive manufacturing processes

ABSTRACTHybrid additive–subtractive manufacturing processes are becoming increasingly popular as a promising solution to overcome the current limitations of Additive Manufacturing (AM) technology and improve the dimensional accuracy and surface quality of parts. Surface roughness, as one of the most important surface quality measures, plays a key role in the fit of assemblies and thus needs to be thoroughly evaluated at the design and manufacturing stages. However, most of the studies on surface roughness modelling and analysis employ empirical approaches, and only consider the effect of a single manufacturing process. In particular, the existing surface roughness models are not applicable to hybrid additive–subtractive manufacturing processes in which a secondary process is involved. In this article, analytical models are established to predict the surface roughness of parts fabricated by AM as well as hybrid additive–subtractive manufacturing processes. A novel surface profile representation scheme is also proposed to increase the prediction accuracy. Case studies are performed to validate the effectiveness of the proposed models. An average of 4.25% error is observed for the AM case, which is significantly smaller than the prediction error of the existing models in the literature. Furthermore, in the hybrid case, an average of 91.83% accuracy is obtained.

Design Consideration for Additive Manufacturing: Fused Deposition Modelling

Additive Manufacturing (AM) technologies have progressed in the past few years and many of them are now capable of producing functional parts instead of mere prototypes. AM provides a multitude of benefits, especially in design freedom. However, it still lacks industrial relevance because of the absence of comprehensive design rules for AM. Although AM is usually advertised as being the solution for all traditional manufacturing design limitations, the fact is that AM only replaces these limitations with a different set of restrictions. To fully exploit the advantages of AM, it is necessary to understand these limitations and consider them early during the design process. The establishment of design considerations in AM enables parts and process optimization. This paper discusses the design considerations that will lead to optimize part quality. Specifically, the work discusses the Fused Deposition Modeling (FDM) due to its common use and availability. These considerations are drawn from literature and from experiments done by the authors. The experiments done by the authors include an investigation for the influence of elevated service temperature on the performance of FDM PLA parts, benchmarking the capability of FDM to print overhangs and bridges without supports, studying the influence of processing parameters over dimensional accuracy, and the effect of processing parameters on the final FDM samples modulus of elasticity. The work presents a case study investigating the correct clearances for FDM parts and finally a redesign for AM case study of a support bracket originally manufactured using traditional manufacturing methods taking into consideration the design considerations discussed in this paper.

Additive Manufacturing Applications in the Domain of Product Service System: An Empirical Overview

This paper is a focus study to introduce an alternative approach to the existing ones, which could be taken to implement Additive Manufacturing (AM) systems for industrial applications. This approach is mainly based on the use of Product Service Systems (PSS) concept in order to turn AM into a more relevant field for service-based purposes within industrial applications. To do so, an AM profile, which is characterized with the similar notions forming the conceptual basis of PSS is detailed, and then the profile is framed for the new service oriented approach to highlight AM's competences in this field. Having found the interconnecting links between these two concepts, the intended approach for implementation would then be discussed and further developed in more detail to include the best use cases of the previous AM case studies as a guide on how to exploit AM's capabilities in similar ways, only modified for a given industrial business which is based on service development. The use cases would also introduce a few of the most successful examples in the production-service systems which have already taken advantage of three-dimensional printing (3D printing) which is one the subcategories of AM in lower tier (desktop) applications. These customer scale applications of 3D printing alongside portfolio analysis of AM in the field of industrial services could shed light for implementing further service-centric research in the advanced manufacturing systems.