Characterizing the effects of additive manufacturing process settings on part performance using approximation-assisted multi-objective optimization

Photopolymers have been applied in many Additive Manufacturing (AM) systems and mostly are cured by UV light. Biodegradable photo-curable polymers are very limited and are not commercially available. DLP-projected maskless AM systems become more and more popular nowadays, but its working area is limited if the part resolution is required. For larger working envelope purpose, liquid crystal display (LCD) panel has great potentials, and LCD’s resolution has been improved significantly in the past few years due to the smart phone application. Therefore, in this research, LCD panel is used to replace DLP for a maskless AM system to cure biodegradable materials, Polycarprolactone (PCL) and Poly(ethylene glycol) diacrylate (PEG-DA). Due to the characteristics of LCD panel, the material systems should be sensitive and photo-polymerized in visible-light range, particularly in RGB. In this study, various percentages of visiblelight photoinitiator, Irgacure 784, in the material systems were investigated. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were utilized to characterize cured biomaterials. Because of the use of photoinitiator, the biocompatibility of the cured materials was also concerned, and hence, MTT assay tests were performed. The preliminary tests of fabrication, using the LCD-projected maskless AM system, cured grid patterns to illustrate the feasibility. The visible-light-curable PCL and PEG-DA will be able to be adopted in tissue engineering scaffold applications in the future.

The performance characterization of the manufacturing processes for additive manufacturing (AM) systems is a significant task for their standardization and implementation in the industry. Also, there is a large diversity of materials used in different AM processes. In the present paper, a methodology is proposed to evaluate, in different directions, the performance of an AM process and material characterization in terms of surface quality. This methodology consists of eight steps, based on a new surface inspection artifact and basic artifact orientations. The proposed artifact with several design configurations fits different AM systems sizes and meets the needs of customers. The effects of main factors on the surface roughness of up-facing platens of the artifacts are investigated using the statistical design of experiments. The proposed methodology is validated by a case study focused on PolyJet material jetting technology. Samples are manufactured of photopolymer resins and post-processed. Three factors (i.e., artifact orientation, platen orientation, and finish type) are considered for the investigation. The case study results show that the platen orientation, finish type, and their interaction have a significant influence on the surface roughness (Ra). The best Ra roughness results were obtained for the glossy finish type in the range of 0.5–4 μm.

Designing efficient closed-loop control algorithms is a key issue in Additive Manufacturing (AM), as various aspects of the AM process require continuous monitoring and regulation, with temperature being a particularly significant factor. Here we study closed-loop control for the temperatures in the extruder of a Material Extrusion AM system, specifically a Big Area Additive Manufacturing (BAAM) system. Previous approaches for temperature control in AM either require the knowledge of exact model parameters, or involve discretisation of the state and action spaces to employ traditional data-driven control techniques. On the other hand, modern algorithms that can handle continuous state and action space problems require a large number of hyperparameter tuning to ensure good performance. In this work, we circumvent the above limitations by making use of a state space temperature model while focusing on both model-based and data-driven methods. We adopt the Linear Quadratic Tracking (LQT) framework and utilise the quadratic structure of the value function in the model-based analytical solution to produce a data-driven approximation formula for the optimal controller. We demonstrate these approaches using a simulator of the temperature evolution in the extruder of a BAAM system and perform an in-depth comparison of the performance of these methods. We find that we can learn an effective controller using solely simulated input–output process data. Our approach achieves parity in performance compared to model-based controllers and so lessens the need for estimating a large number of parameters of the often intricate and complicated process model. We believe this result is an important step towards achieving autonomous intelligent manufacturing.

As part of the cosmos of digital fabrication technology, Additive Manufacturing (AM) systems are able to manufacture three-dimensional components and products directly from raw material and 3D design data. The layer-by-layer operating process of these systems does not require the use of tools, moulds or dies.Technology observers speculate that AM will have a profound economic impact on the manufacturing sector and indeed on wider society. By constructing a model of production cost for two different AM systems used commercially for the manufacture of end-use metal parts, Electron Beam Melting (EBM) and Direct Metal Laser Sintering (DMLS), this paper performs an inter-process comparison of cost performance. High specific costs, measured at £2.39 and £6.18 per cm3 of material deposited respectively, are identified as a central impediment to more widespread technology adoption of such additive systems.The research demonstrates differing levels of system productivity, suggesting that the observed deposition rates are not sufficient for the adoption of EBM and DMLS in high volume manufacturing applications. Despite the absence of amortisable tooling costs, the analysis also reveals that economies of scale are achievable in AM. The results reached are further discussed in the light of the varying strategic requirements posed by the market-pull and technology-push modes of innovation which are both found in the AM industry.

A physical hash for preventing and detecting cyber-physical attacks in additive manufacturing systems

At the Johns Hopkins University Applied Physics Laboratory (APL), glass bead-filled polyamide (a.k.a. nylon) (GFN) is being used frequently for functional parts and systems, built using a laser-based powder bed fusion (PBF) additive manufacturing (AM) system. Since these parts have performance requirements, it is important to understand the mechanical properties of the additively-made GFN as a function of build orientation and build parameters. In addition, the performance of the AM system used to manufacture these parts must be evaluated in order to understand its capabilities, especially in order to determine the dimensional precision and repeatability of features built with this system. This paper summarizes recent APL efforts to characterize the GFN powder, the mechanical properties of parts made with GFN, and the performance of the laser PBF machine while running GFN using an AM test artifact.

Part filtering methods for additive manufacturing: A detailed review and a novel process-agnostic method

PurposeAmong the different methodologies used for performance control in precision manufacturing, the measurement of metrological test artefacts becomes very important for the characterization, optimization and performance evaluation of additive manufacturing (AM) systems. The purpose of this study is to design and manufacture several benchmark artefacts to evaluate the accuracy of the selective laser melting (SLM) manufacturing process.Design/methodology/approachArtefacts consist of different primitive features (planes, cylinders and hemispheres) on sloped planes (0°, 15°, 30°, 45°) and stair-shaped and sloped planes (from 0° to 90°, at 5° intervals), manufactured in 17-4PH stainless steel. The artefacts were measured optically by a structured light scanner to verify the geometric dimensioning and tolerancing of SLM manufacturing.FindingsThe results provide design recommendations for precision SLM manufacturing of 17-4PH parts. Regarding geometrical accuracy, it is recommended to avoid surfaces with 45° negative slopes or higher. On the other hand, the material shrinkage effect can be compensated by resizing features according to X and Y direction.Originality/valueNo previous work has been found that evaluates accuracy when printing inwards (pockets) and outwards (pads) geometries at different manufacturing angles using SLM. The proposed artefacts can be used to determine the manufacturing accuracy of different AM systems by resizing to fit the build envelope of the system to evaluate. Analysis of manufactured benchmark artefacts allows to determine rules for the most suitable design of the desired parts.

Fundamental scaling relationships in additive manufacturing and their implications for future manufacturing and bio-manufacturing systems

Recent advances in additive manufacturing (AM) have attracted significant industrial interest. Initially, AM was mainly associated with the fabrication of prototypes, but the AM advances together with the broadening range of available materials, especially for producing metallic parts, have broaden the application areas and now the technology can be used for manufacturing functional parts, too. Especially, the AM technologies enable the creation of complex and topologically optimised geometries with internal cavities that were impossible to produce with traditional manufacturing processes. However, the tight geometrical tolerances along with the strict surface integrity requirements in aerospace, biomedical and automotive industries are not achievable in most cases with standalone AM technologies. Therefore, AM parts need extensive post-processing to ensure that their surface and dimensional requirements together with their respective mechanical properties are met. In this context, it is not surprising that the integration of AM with post-processing technologies into single and multi set-up processing solutions, commonly referred to as hybrid AM, has emerged as a very attractive proposition for industry while attracting a significant R&D interest. This paper reviews the current research and technology advances associated with the hybrid AM solutions. The special focus is on hybrid AM solutions that combine the capabilities of laser-based AM for processing powders with the necessary post-process technologies for producing metal parts with required accuracy, surface integrity and material properties. Commercially available hybrid AM systems that integrate laser-based AM with post-processing technologies are also reviewed together with their key application areas. Finally, the main challenges and open issues in broadening the industrial use of hybrid AM solutions are discussed.

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 (AM) processes have revolutionized manufacturing industries by enabling the production of complex geometries with reduced material waste and lead times. However, ensuring the quality of AM parts remains a significant challenge due to the complexity of the process and inherent variability in material properties. This review investigates the use of artificial intelligence (AI) to enhance quality assurance in AM processes, focusing on specific machine learning techniques such as convolutional neural networks for defect detection, support vector machines for classification of material properties, and reinforcement learning for real-time process optimization. The AI-driven methodologies are applied to predict defects, optimize process parameters, and monitor real-time production quality, utilizing large datasets generated from sensors and in-situ monitoring systems. The study demonstrates significant improvements in the accuracy of defect detection, the reliability of material property classification, and the efficiency of process optimization. In addition, it addresses challenges such as data pre-processing, model interpretability, and integration with existing AM systems. The findings highlight the potential of AI to transform quality assurance in AM and outline future research directions for further integration and enhancement of AI techniques in AM.

The recent adoption of the term additive manufacturing (AM) to describe a broad range of digital ‘layer by layer’ fabrication techniques comes 20 years since its inception and more than 10 years since architectural practices engaged with the technology for making prototypes and models. Soon this technology will join existing CNC subtractive and formative processes within the volume and mass‐market sectors, where design complexity and increased functionality result in competitive advantage. It is also about a decade since researchers proposed additive manufacturing for construction. Rupert Soar and David Andreen introduce here the different construction‐scale additive manufacturing systems currently in development. If linked to physiomimetic computational design strategies, these technologies provide novel possibilities for addressing architecture's manufacturing challenges in the face of energy expenditure, material resources and environmental impact. Copyright © 2012 John Wiley & Sons, Ltd.

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.

This work presents a decision-making methodology that allows the merging of quantitative and qualitative decision variables for selecting the optimal metal Additive Manufacturing (AM) technology. The approach is applied on two competing technologies in the field of metal AM industry, i.e., the metal extrusion AM process (metal FFF) and the Laser Powder Bed Fusion process (LPBF) with single and multiple lasers, which represent the benchmark solution currently on the market. A comprehensive techno-economical comparison is presented where the two processes are analysed in terms of process capabilities (quality, easiness of use, setup time, range of possible materials, etc.) and costs, considering two different production scenarios and different parts’ geometries. In the first scenario, the AM system is assumed to be dedicated to one single part production while in this second scenario, the AM system is assumed to be saturated, as devoted to producing a wide mix of part types. For each scenario, two different part types made of 17–4 PH stainless steel are considered as a reference to investigate the effect of shape complexity, part size and production times to select the best technology when metal FFF and LPBF must be considered. The first part type refers to an extrusion die, to represent typical shapes of interest in the tooling industry, while the second part type is an impeller which can be used in many different industrial sectors, ranging from oil and gas to aerospace. In order to include quantitative and qualitative criteria, a decision-making model based on Analytic Hierarchy Process (AHP) is proposed as the enabler tool for decision making. The proposed approach allows to determine the most effective solution depending on the different production configurations and part types and can be used as a guideline and extended to include other technologies in the field of metal AM. On the other side, the critical discussion of the criteria selected, and the results achieved allow to highlight the pros and cons of the competing technologies, thus defining the existing limits to define directions for future research.