A Preliminary Experimental Study of Additive Manufacturing Energy Consumption

Additive manufacturing (AM) has significant advantages, including design freedom, mass customization, and complex-structure manufacturing capabilities. As reducing the manufacturing energy is challenging in terms of the industrial sustainability, the AM energy consumption must be further explored. Existing energy consumption quantitation methods for AM require complex models that considering the energy characteristics of equipment. Therefore, we propose a feature-based energy consumption quantitation method for complex AM parts using simple models and suitable for different AM technologies. First, a feature segmentation method is proposed to divide complex AM parts into typical AM features. Then, the energy consumption model for each AMF is developed for energy consumption quantification during part fabrication. Finally, the energy consumption of a typical mechanical part manufactured via three different AM processes is investigated using the proposed method and measured experimentally. The results show that the proposed method can effectively and rapidly predict the energy consumption of AM processes with an accuracy of more than 95 %. Furthermore, the efficiency of the three AM processes is compared and discussed to address suitable efficiency improvement methods. In general, the proposed method can be integrated into three-dimensional AM models, providing a reference for the structural optimization of AM parts and sustainable manufacturing.

Over the past few years additive manufacturing (AM) technology has grown in popularity as companies explore its potential. Applying layer upon layer of polymers can create objects of almost any shape and geometry guided by design files, and now, recent developments have made it possible to print metal parts and components, making it a potentially disruptive innovation for the supply chain. AM has already had an impact on other industries such as aviation—Air-bus agreed in October to a deal to manufacture polymer parts for use on its A350 XWB aircraft—and now, as oil and gas companies look to adopt AM into their supply chain management, service companies are breaking through with new machines and processes that may facilitate larger-scale production of parts and components in the future. In addition, a new guideline has been established to help bridge the gap between the quality assurance of parts created by an AM process and those created through traditional manufacturing processes. Additive Manufacturing Qualification In November, DNV GL published its first classification guideline for the use of AM in maritime and oil and gas. The guideline was designed to bridge the gap between the quality assurance of parts and components created by an AM process and those created through traditional manufacturing processes by creating a clear pathway for AM certification. The guideline serves as an umbrella document that establishes the overall framework process for qualification and certification of AM products and further points to other DNV specific services/documents that are relevant in assessing parameters that will impact the final product, from the materials used to the actual printing and post-processing. Brice Le Gallo, DNV’s regional manager for Southeast Asia and Australia, said the lack of a guideline was one of the more significant hurdles for more widespread adoption of AM processes. Because there are currently only a handful of AM standards, companies often conduct their own testing to ensure integrity of the equipment, parts, and components. The cost and time needed to conduct these tests deters wider adoption. In addition, AM processes typically incur higher material and machining costs than conventional manufacturing. He said the new guideline will help provide a blueprint that may encourage further adoption. “Today what we see is that the level of adoption in these industries, maritime and offshore oil and gas, is still quite low. One of the areas where we can really help the industry get the level of adoption increased is on the qualification side,” Le Gallo said. “This is one of the most important hurdles. We think that by bringing appropriate qualification frameworks to the industry, we will see an important increase in the level of adoption. This would then lead to greater volume, which in turn would lead to costs going down.”

The goal of this work is to predict the effect of part geometry and process parameters on the instantaneous spatiotemporal distribution of temperature, also called the thermal field or temperature history, in metal parts as they are being built layer-by-layer using additive manufacturing (AM) processes. In pursuit of this goal, the objective of this work is to develop and verify a graph theory-based approach for predicting the temperature distribution in metal AM parts. This objective is consequential to overcome the current poor process consistency and part quality in AM. One of the main reasons for poor part quality in metal AM processes is ascribed to the nature of temperature distribution in the part. For instance, steep thermal gradients created in the part during printing leads to defects, such as warping and thermal stress-induced cracking. Existing nonproprietary approaches to predict the temperature distribution in AM parts predominantly use mesh-based finite element analyses that are computationally tortuous—the simulation of a few layers typically requires several hours, if not days. Hence, to alleviate these challenges in metal AM processes, there is a need for efficient computational models to predict the temperature distribution, and thereby guide part design and selection of process parameters instead of expensive empirical testing. Compared with finite element analyses techniques, the proposed mesh-free graph theory-based approach facilitates prediction of the temperature distribution within a few minutes on a desktop computer. To explore these assertions, we conducted the following two studies: (1) comparing the heat diffusion trends predicted using the graph theory approach with finite element analysis, and analytical heat transfer calculations based on Green’s functions for an elementary cuboid geometry which is subjected to an impulse heat input in a certain part of its volume and (2) simulating the laser powder bed fusion metal AM of three-part geometries with (a) Goldak’s moving heat source finite element method, (b) the proposed graph theory approach, and (c) further comparing the thermal trends predicted from the last two approaches with a commercial solution. From the first study, we report that the thermal trends approximated by the graph theory approach are found to be accurate within 5% of the Green’s functions-based analytical solution (in terms of the symmetric mean absolute percentage error). Results from the second study show that the thermal trends predicted for the AM parts using graph theory approach agree with finite element analyses, and the computational time for predicting the temperature distribution was significantly reduced with graph theory. For instance, for one of the AM part geometries studied, the temperature trends were predicted in less than 18 min within 10% error using the graph theory approach compared with over 180 min with finite element analyses. Although this paper is restricted to theoretical development and verification of the graph theory approach, our forthcoming research will focus on experimental validation through in-process thermal measurements.

Additive manufacturing (AM) has recently been accorded considerable interest by manufacturers. Many manufacturing industries, amongst others in the aerospace sector, are already using AM parts or are investing in such manufacturing methods. Important material properties, such as microstructures, residual stress, and surface topography, can be affected by AM processes. In addition, a subtractive manufacturing (SM) process, such as machining, is required for finishing certain parts when accurate tolerances are required. This finish machining will subsequently affect the surface integrity and topography of the material. In this research work, we focused on the surface integrity of Ti-6Al-4V parts manufactured using three different types of AM and finished using an SM step. The aim of this study was to gain an understanding on how each process affects the resulting surface integrity of the material. It was found that each AM process affects the materials’ properties differently and that clear differences exist compared to a reference material manufactured using conventional methods. The newly generated surface was investigated after the SM step and each combination of AM/SM resulted in differences in surface integrity. It was found that different AM processes result in different microstructures which in turn affect surface integrity after the SM process.

The unique tool-less and layer-upon-layer capabilities of additive manufacturing (AM) have largely lessened manufacturing constraints and significantly broadened design freedom. AM offers new opportunities for developing customized products by achieving significant improvements in product performances and lower overall costs. The AM-enabled customization opportunities inevitably challenge traditionally design theories and methodologies (DTMs) that lack design knowledge for AM products. Here, to counteract this limitation, this study presents a novel methodology for constructing knowledge supporting DTMs for AM-enabled customization: customized design for AM (CDfAM). The CDfAM supports two main phases of design for additive manufacturing (DfAM): (1) opportunistic DfAM in a conceptual design phase and (2) restrictive DfAM in an embodiment design phase. In terms of the knowledge construction for the opportunistic DfAM, it is essential to construct knowledge that supports to explore a customized design space enabled by AM’s design freedom from the conceptual design phase. To address the challenge, this study provides a novel method for capturing knowledge of customized AM product behaviors. First, this study proposes an affordance-based DfAM framework by integrating the concept of affordance into three link chain models. Second, an affordance structure is proposed to develop a formal method for representing the knowledge of the customized AM product behaviors utilizing finite state automata. In terms of the knowledge construction for the restrictive DfAM, this study presents a formal method for representing and reasoning customizable modular knowledge of design rules for AM. First, a novel structured modular hierarchy and associated formalisms of AM design rule elements are presented. Second, machine learning is employed to automatically learn design rules from AM data, which leverages the proposed design rule formalisms. A knowledge graph is designed and built to demonstrate the proposed methodology. Case studies with AM products such as a transtibial prosthesis leg and AM data such as ex-situ AM measurement data provide illustrations in this study. The case studies show that the proposed methodology explicitly constructs structured and correlated AM design knowledge of customized AM product behaviors, product geometries, and additive manufacturability constraints. It is also shown that qualified design knowledge in the knowledge graph enhances automation of DfAM decision-making and data-driven knowledge constructions. The proposed CDfAM methodology enables incorporating both customized AM product functionalities and additive manufacturability constraints into product designs. This study provides a foundation for AM knowledge construction and data- and knowledge-driven automation of DfAM decision-making. Validated design rules and representations are expected to contribute to developing international standards on AM design rules.

Digital twin (DT) and additive manufacturing (AM) technologies are key enablers for smart manufacturing systems. DTs of AM systems are proposed in recent literature to provide additional analysis and monitoring capabilities to the physical AM processes. This work proposes a DT framework for real-time performance monitoring and anomaly detection in fused deposition modeling (FDM) AM process. The proposed DT framework can accommodate AM process measurement data to model the AM process as a cyber-physical system with continuous and discrete event dynamics, and allow for the development of various applications. A new performance metric is proposed for performance monitoring and a formal specification based anomaly detection method is proposed for AM processes. Implementation of the proposed DT on an off-the-shelf FDM printer and experimental results of anomaly detection and process monitoring are presented at the end.

The additive manufacture (AM) of plastic components can be accomplished by a variety of methods which are classified according to how material layers are consolidated layer-by-layer to create physical objects from digital data. Two of the most widely used methods include Material Extrusion (MEX) and Vat Photopolymerization (VP). These methods include a range of commercialized AM processes often referred to by various trademarked terms such as Fused Deposition Modelling (FDM) and Stereolithography (SLA). Compared to conventional subtractive or formative manufacturing process, MEX and VP are able to manufacture complex parts with high ability for customization, as they impose few constraints on part geometry, and require low setup effort with no custom tooling. Furthermore, MEX and VP are both well-established additive manufacturing processes and through ongoing refinement have achieved compatibility with a broad range of materials and part geometries, and comparatively low operating costs. Due to their high versatility, MEX and VP have been widely used in a broad range of industries including applications in chemical sciences, biotechnology, aerospace, defense, and automotive engineering. However, despite their high versatility, AM processes such as MEX and VP are subject to unique technological characteristics associated with the manufacturing process, the material properties, part design and suitable application areas. This chapter provides an overview of MEX and VP processes characteristics critical to the effective application of these additive manufacturing technologies to high performance products.KeywordsAdditive manufacturingDesign for additive manufactureMaterial extrusionPhotopolymerizationPolymersThermoplasticsVat photopolymerization

Closed-loop control applications for additive manufacturing (AM) technologies introduce unique challenges in control-oriented modeling and controller development. There have been developments in current literature to model the temporal and spatial dynamics of AM processes. The temporal dynamics of AM processes are often modeled using tools from fluid dynamics and mechanics to represent the material deposition and the motion of the deposition system. Spatial dynamics are often modeled by representing the spreading dynamics of the deposited material in the layerwise spatial domain. An important challenge with the spatial dynamics of AM processes is to understand the performance of the layer-to-layer (L2L) spatial dynamics under spatial disturbances in the system. To this end, this article presents a linear layerwise spatially varying (LLSV) systems modeling framework to represent the L2L spatial height evolution of a generic AM process. The proposed unifying modeling framework can easily represent various AM processes. An L2L stability measure is provided as a performance measure for the spatial dynamic state of an AM process. L2L stability is a novel analysis tool to understand and analyze the spatial characteristics of AM processes. Fundamental L2L stability definitions for a nominal system model and a system model with known Gaussian spatial noise are presented. Theoretical robustness bounds for L2L stability are experimentally compared to a fused deposition modeling (FDM) process. The results show that the theoretical robustness bounds given in this work provide an important foundation for developing novel closed-loop L2L spatial control applications.

Additive manufacturing (AM) has gained notoriety for offering advantages over traditional manufacturing methods, such as increased design complexity and flexibility. However, it has not found widespread use beyond rapid prototyping. One hindrance to the acceptance of AM processes in industry is the time and cost of fabrication per component. While metal AM by itself can be inexpensive, extra manufacturing steps in the form of subtractive manufacturing (SM) may need to be performed to reach final part tolerances, leading to hybrid additive-subtractive manufacturing (HASM) of a part, which increases time and cost. A potential area to reduce cost is through increasing the efficiency of the HASM process by conducting additive and subtractive manufacturing simultaneously. Usually, HASM is performed in a process where AM is completed in one machine or cell and transferred to another machine or cell for SM in a sequential assembly line process. This efficiency decreases part cost, but high aspect ratio parts or parts with internal geometry that require interleaved additive deposition and machining cannot be produced. One unexplored solution to simultaneous HASM that allows for interleaved operations is to operate the deposition head and machining spindle concurrently within the same machine envelope, known as concurrent HASM (CHASM). In this type of process, both AM and SM occur simultaneously on a batch of small parts or a single large part, maintaining a high efficiency without sacrificing the full range of complex geometries that AM allows for. A potential approach to the single-machine method could be to combine a robot and mill within the same envelope. A challenge to this approach, however, is control of both systems. Most machine controllers have limited external communication or, if a robot has been integrated, only offer movement of either the robot or mill at any given time. As a result, systems must pause either the AM or SM process to switch between them rather than working simultaneously. The present work investigates the positional accuracy of such a CHASM system comprised of a robotic arm and a 3-axis mill. Open-loop tests with limited communication between machines are performed on the system to verify positional error during concurrent robot-mill movements. Under certain conditions, it is demonstrated that position error can stay within 2 mm for the duration of a single layer; however, these tests show that, generally, the open-loop positioning performance of the system is inadequate for CHASM without part-specific hand-tuning of parameters. Based on these results, a set of requirements for successful robot-CNC CHASM is proposed for future integrations.

Additive manufacturing, characterized by its inherent layer by layer fabrication methodology has been coined by many as the latest revolution in the manufacturing industry. Due to its diversification of Materials, processes, system technology and applications, Additive Manufacturing has been synonymized with terminology such as Rapid prototyping, 3D printing, free-form fabrication, Additive Layer Manufacturing, etc. A huge media and public interest in the technology has led to an innovative attempt of exploring the technology for applications beyond the scope of the traditional engineering industry. Nevertheless, it is believed that a critical factor for the long-term success of Additive Manufacturing would be its ability to fulfill the requirements defined by the traditional manufacturing industry. A parallel development in market trends and product requirements has also lead to a wider scope of opportunities for Additive Manufacturing. The presented paper discusses some of the key challenges which are critical to ensure that Additive Manufacturing is truly accepted as a mainstream production technology in the industry. These challenges would highlight on various aspects of production such as product requirements, process management, data management, intellectual property, work flow management, quality assurance, resource planning, etc. In Addition, changing market trends such as product life cycle, mass customization, sustainability, environmental impact and localized production will form the foundation for the follow up discussion on the current limitations and the corresponding research opportunities. A discussion on ongoing research to address these challenges would include topics like process monitoring, design complexity, process standardization, multi-material and hybrid fabrication, new material development, etc.

The design methodologies and part shape algorithms for additive manufacturing (AM) are rapidly growing fields, proven to be of critical importance for the uptake of additive manufacturing of parts with enhanced performance in all major industrial sectors. The current trend for part design is a computationally driven approach where the parts are algorithmically morphed to meet the functional requirements with optimized performance in terms of material distribution. However, the manufacturability restrictions of AM processes are not considered at the primary design phases but at a later post-morphed stage of the part’s design. This paper proposes an AM design method to ensure: (1) optimized material distribution based on the load case and (2) the part’s manufacturability. The buildability restrictions from the direct energy deposition (DED) AM technology were used as input to the AM shaping algorithm to grant high AM manufacturability. The first step of this work was to define the term of AM manufacturability, its effect on AM production, and to propose a framework to estimate the quantified value of AM manufacturability for the given part design. Moreover, an AM design method is proposed, based on the developed internal stresses of the build volume for the load case. Stress tensors are used for the determination of the build orientation and as input for the part morphing. A top-down mesoscale geometric optimization is used to realize the AM part design. The DED Design for Additive Manufacturing (DfAM) rules are used to delimitate the morphing of the part, representing at the same time the freeform mindset of the AM technology. The morphed shape of the part is optimized in terms of topology and AM manufacturability. The topology optimization and AM manufacturability indicator (TMI) is introduced to screen the percentage of design elements that serve topology optimization and the ones that serve AM manufacturability. In the end, a case study for proof of concept is realized.

Comparative study on life cycle assessment of components produced by additive and conventional manufacturing process

Real-time Process Authentication for Additive Manufacturing Processes based on In-situ Video Analysis

The adoption of various additive manufacturing techniques has witnessed significant growth across various industries due to its potential for material efficiency and design flexibility. However, exploring and understanding its environmental implications becomes crucial as technology advances. In this study, we investigate the relationship between energy consumption and surface quality associated with the Fused Deposition Modeling (FDM) 3D printing technique with the help of an FDM printer. Standard parts (triangular prisms) of varying base angles were printed using PLA while maintaining constant volume. Some angles were chosen to demonstrate congruency and the influence of print orientation on energy usage and surface roughness was studied. Data reveals the relationship between the 3D-printed part geometry, energy consumption, and surface quality. The study provides valuable insights for designers and manufacturers seeking to optimize energy consumption in additive manufacturing processes while meeting surface roughness standards. Further research in this area may lead to the development of software tools for minimizing energy consumption in additive manufacturing processes.

PurposeMaterial extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors.Design/methodology/approachA group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process.FindingsThe derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system.Originality/valueThe development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.