A Centralized Framework for System-Level Control and Management of Additive Manufacturing Fleets

PurposeThis study aims to evaluate and compare the macroscopic properties of commercial acrylonitrile-butadiene-styrene (ABS) processed by two different types of additive manufacturing (AM) machines. The focus is also on the effect of multiple closed-loop recycling of ABS.Design/methodology/approachA conventional direct-drive, Cartesian-type machine and a Bowden, Delta-type machine with an infrared radiant heating system are used to manufacture test specimens molded in ABS. Afterward, multiple closed-loop recycling cycles are conducted, involving consecutive AM (four times) and recycling (three times). The rheological, mechanical, morphological and physicochemical properties are investigated.FindingsThe type of machine affects the quality of the produced parts. The machine containing an infrared radiant system in a temperature-controlled chamber produces parts showing higher mechanical properties and filling fraction, although it increases the yellowing. Closed-loop recycling of ABS for AM is applicable for at least two cycles, inducing a slight increase in tensile modulus (ca. 5%) and in tensile strength (ca. 13%) and a decrease in the impact strength (ca. 14%) and melt viscosity. An increase in the filling fraction of the AM parts promotes an increase in tensile strength and tensile modulus, although it does not influence the impact strength. Furthermore, multiple closed-loop recycling does not affect the overall chemical structure of ABS.Practical implicationsControlling the environmental temperature and using infrared radiant heating during AM of ABS improves the quality of the produced parts. Closed-loop recycling of ABS used in AM is feasible up to at least two recycling steps, supporting the implementation of a circular economy for polymer-based AM.Originality/valueThis study shows original results regarding the assessment of the effect of different types of AM machines on the main end-use properties of ABS parts and the influence of multiple closed-loop recycling on the characteristics of ABS fabricated by the most suited AM machine with an infrared radiant heating system and a temperature-controlled environment.

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.

In situ process monitoring includes any technologies that monitor or inspect during an additive manufacturing (AM) process. This article presents the types, process considerations, and challenges of in situ monitoring technologies that can be implemented during an AM process. The types include system health monitoring, melt pool monitoring, and layer monitoring. The article discusses data analysis, and provides an overview of the integration of sensors into AM machines.

Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes.

Additive manufacturing of biomedical implants: A feasibility assessment via supply-chain cost analysis

Additive manufacturing (AM) can enable complex and novel designs that are otherwise infeasible with traditional metal manufacturing techniques. In low-volume production scenarios, particularly for specialized applications which can benefit from customized designs, traditional metal manufacturing techniques may be limited by costs associated with tooling. The ability to produce novel designs is particularly interesting in heat exchanger (HX) design where performance is often largely based on the achievable geometry. However, consequences of the AM process such as surface roughness, deviation from specified dimensions, and defects such as cracks and voids could also affect HX performance. These effects may vary between identically designed AM parts based on AM machine settings. The goal of this work is to gain a better understanding of the performance variations across several different implementations of the same heat exchanger design. More specifically, the objective of this work is to experimentally compare the thermal and hydraulic performances of a traditionally manufactured, stamped-aluminum aircraft oil cooler and three geometrically equivalent, additively manufactured counterparts. Compared to the traditionally manufactured heat exchanger, the AM HXs exhibited significantly higher air-side pressure loss and higher heat transfer despite having nominally similar geometries. Between AM HXs, there were slight differences in surface roughness characteristics based on optimal profilometry measurements. In addition, the thickness of the air-side fins varied as much as 15 percent between the AM HXs. The net effect, without the contribution of each cause clear, was higher air-side pressure loss and slightly higher heat transfer for the AM HX with thicker fins. This study indicates that functional heat exchangers built using AM vary in performance even when the same digital model is used to print them, and that AM HXs as a group perform considerably differently than their traditional counterparts. Thus, there is a need to account for anticipated surface roughness, geometric deviations, and potential defects when designing HXs. Proper consideration could result in improved thermal performance for future heat exchangers.

Additively manufactured metallic biomaterials.

PurposeThe purpose of this paper is to communicate a method to perform simultaneous topology optimization of component and support structures considering typical metal additive manufacturing (AM) restrictions and post-print machining requirements.Design/methodology/approachAn integrated topology optimization is proposed using two density fields: one describing the design and another defining the support layout. Using a simplified AM process model, critical overhang angle restrictions are imposed on the design. Through additional load cases and constraints, sufficient stiffness against subtractive machining loads is enforced. In addition, a way to handle non-design regions in an AM setting is introduced.FindingsThe proposed approach is found to be effective in producing printable optimized geometries with adequate stiffness against machining loads. It is shown that post-machining requirements can affect optimal support structure layout.Research limitations/implicationsThis study uses a simplified AM process model based on geometrical characteristics. A challenge remains to integrate more detailed physical AM process models to have direct control of stress, distortion and overheating.Practical implicationsThe presented method can accelerate and enhance the design of high performance parts for AM. The consideration of post-print aspects is expected to reduce the need for design adjustments after optimization.Originality/valueThe developed method is the first to combine AM printability and machining loads in a single topology optimization process. The formulation is general and can be applied to a wide range of performance and manufacturability requirements.

PurposeThis paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.Design/methodology/approachA systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.FindingsThe findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.Research limitations/implicationsThis paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.Originality/valueThis paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.

Recently, additive manufacturing (AM) has been a useful technology to fabricate complex parts. However, more consideration and technological developments are required for the expansion of the AM process in various industries. In particular, verification for repeatability and reproducibility of AM parts are very difficult issues when the AM parts are used in specific industries, such as nuclear and aerospace. In this respect, the change in the mechanical properties according to the relationship between AM parameters, machines, and powders is a very important area of research. In this study, four different L-PBF machines and three different powders from different suppliers were used to fabricate stainless steel 316L AM materials using same or similar AM parameters. In addition, tensile tests were conducted on all AM materials to obtain mechanical properties. The test results confirmed that differences in the mechanical properties were observed in the same AM process parameters of different machines and powder conditions.

Comparison of dimensional tolerance grades for metal AM processes

New developments in the field of artificial intelligence (AI) are increasingly finding their way into industrial areas such as additive manufacturing (AM). Generative AI (GAI) applications in particular offer interesting possibilities here, for example, to generate texts, images or computer codes with the help of algorithms and to integrate these as useful supports in various AM processes. This paper examines the opportunities that GAI offers specifically for additive manufacturing. There are currently relatively few publications that deal with the topic of GAI in AM. Much of the information has only been published in preprints. There, the focus has been on algorithms for Natural Language Processing (NLP), Large Language Models (LLMs) and generative adversarial networks (GANs). This summarised presentation of the state of the art of GAI in AM is new and the link to specific use cases is this first comprehensive case study on GAI in AM processes. Building on this, three specific use cases are then developed in which generative AI tools are used to optimise AM processes. Finally, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is carried out on the general possibilities of GAI, which forms the basis for an in-depth discussion on the sensible use of GAI tools in AM. The key findings of this work are that GAI can be integrated into AM processes as a useful support, making these processes faster and more creative, as well as to make the process information digitally recordable and usable. This current and future potential, as well as the technical implementation of GAI into AM, is also presented and explained visually. It is also shown where the use of generative AI tools can be useful and where current or future potential risks may arise.

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.

MILP models to minimise makespan in additive manufacturing machine scheduling problems

On the environmental impacts of 3D printing technology