Techniques and Methods for Optimizing 3D Printing Parameters

ABSTRACTIntroductionEvidence syntheses are crucial in healthcare and elsewhere but are resource‐intensive, often taking years to produce. Artificial intelligence and machine learning (AI/ML) tools may improve production efficiency in certain review phases, but little is known about their impact on entire reviews.MethodsWe performed prespecified analyses of a convenience sample of eligible healthcare‐ or welfare‐related reviews commissioned at the Norwegian Institute of Public Health between August 1 2020 (first commission to use AI/ML) and January 31 2023 (administrative cut‐off). The main exposures were AI/ML use following an internal support team's recommendation versus no use. Ranking (e.g., priority screening), classification (e.g., study design), clustering (e.g., documents), and bibliometric analysis (e.g., OpenAlex) tools were included, but we did not include or exclude specific tools. Generative AI tools were not widely available during the study period. The outcomes were resources (person‐hours) and time from commission to completion (approval for delivery, including peer review; weeks). Analyses accounted for nonrandomized assignment and censored outcomes (reviews ongoing at cut‐off). Researchers classifying exposures were blinded to outcomes. The statistician was blinded to exposure.ResultsAmong 39 reviews, 7 (18%) were health technology assessments versus systematic reviews, 19 (49%) focused on healthcare versus welfare, 18 (46%) planned meta‐analysis, and 3 (8%) were ongoing at cut‐off. AI/ML tools were used in 27 (69%) reviews. Reviews that used AI/ML as recommended used more resources (mean 667 vs. 291 person‐hours) but were completed slightly faster (27.6 vs. 28.2 weeks). These differences were not statistically significant (relative resource use 3.71; 95% CI: 0.36–37.95; p = 0.269; relative time‐to‐completion: 0.92; 95% CI: 0.53–1.58; p = 0.753).ConclusionsAssociations between AI/ML use and the outcomes remains uncertain. Multicenter studies or meta‐analyses may be needed to determine if these tools meaningfully reduce resource use and time to produce evidence syntheses.

Creating and curating new data to augment heuristics is a forthcoming approach to materials science in the future. Highly improved properties are advantageous even with "commodity polymers" that do not need to undergo new synthesis, high-temperature processes, or extensive reformulation. With artificial intelligence and machine learning (AI/ML), optimizing synthesis and manufacturing methods will enable higher throughput and innovative directed experiments. Simulation and modeling to create digital twins with statistical and logic-derived design, such as the design of experiments (DOE), will be superior to trial-and-error approaches when working with polymer materials. This paper describes and demonstrates protocols for understanding hierarchical approaches in optimizing the polymerization and copolymerization process via AI/ML to target specific properties, using model monomers such as styrene and acrylate. The key is self-driving continuous flow chemistry reactors with sensors (instruments) and real-time ML with an online monitoring set-up that allows a feedback loop mechanism. We provide initial results using ML refinement of the classical Mayo-Lewis equation (MLE), time-series data, and an autonomous flow reactor system build-up as a future data-generating station. More importantly, it lays the ground for precision control of the copolymerization process. In the future, it should be possible to undertake collaborative human-AI-guided protocols for the autonomous fabrication of new polymers guided by literature and available data sources targeting new properties.

Additive manufacturing of cementitious composites: Materials, methods, potentials, and challenges

Fused Deposition Modeling is an additive manufacturing technology that is used to create a wide range of parts and applications. Along with its benefits, there are some challenges regarding the printed parts’ mechanical properties, which are associated with printing parameters like layer thickness, printing speed, infill density, printing temperature, bed temperature, infill pattern, chamber temperature, and printing orientation. One of the most crucial challenges in additive manufacturing technology is the residual stress, which significantly affects the parts like fatigue life, cracks propagation, distortions, dimensional accuracy, and corrosion resistance. Residual stress is hard to detect in the components and sometimes is costly to investigate. Printing specimens with different parameters costs money and is time-consuming. In this work, numerical simulation using Digimat-AM software was employed to predict and minimize the residual stress in printed Acrylonitrile Butadiene Styrene material using Fused Deposition Modeling technology. The printing was done by choosing six different printing parameters with three values for each parameter. The results showed a significant positive correlation between residual stress and printing temperature and infill percentage and a negative correlation with layer thickness and printing speed. At the same time, we found no effect of the bed temperature on the residual stress. Finally, the minimum residual stress was obtained with a concentric infill pattern.

The widespread application of additive manufacturing (AM) technologies, commonly known as three-dimensional (3D) printing, in industrial and home-business sectors, and the expected increase in the number of workers and consumers that use these devices, have raised concerns regarding the possible health implications of 3D printing emissions. To inform the risk assessment and management processes, this review evaluates available data concerning exposure assessment in AM workplaces and possible effects of 3D printing emissions on humans identified through in vivo and in vitro models in order to inform risk assessment and management processes. Peer-reviewed literature was identified in Pubmed, Scopus, and ISI Web of Science databases. The literature demonstrated that a significant fraction of the particles released during 3D printing could be in the ultrafine size range. Depending upon the additive material composition, increased levels of metals and volatile organic compounds could be detected during AM operations, compared with background levels. AM phases, specific job tasks performed, and preventive measures adopted may all affect exposure levels. Regarding possible health effects, printer emissions were preliminary reported to affect the respiratory system of involved workers. The limited number of workplace studies, together with the great variety of AM techniques and additive materials employed, limit generalizability of exposure features. Therefore, greater scientific efforts should be focused at understanding sources, magnitudes, and possible health effects of exposures to develop suitable processes for occupational risk assessment and management of AM technologies.

PurposeThis paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.Design/methodology/approachThe experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.FindingsThe influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.Originality/value3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

One of the major advantages of 3D food printing is the customizability in terms of structure, design, and nutritional content. However, printability of the ingredients and the quality of the 3D printed food products are dependent on several product and printing parameters. In this study, nutrient dense cookies were developed with underutilized ingredients including jackfruit seed powder and finger millet powder as base materials using 3D food printing. The hardness, rheological behavior, and microstructure of 3D printed cookies with different products (e.g., water butter ratio) and printing (e.g., fill density and temperature) parameters were analyzed. The 3D printed cookies were developed by extruding at 27 and 30 °C with fill density values of 50%, 70%, 90%, and 100% and water butter ratios of 3:10 and 6:5. The 3D-printed cookie dough exhibited a more elastic behavior with higher storage modulus values than the loss modulus. The hardness of the baked cookies was influenced by printing temperature, fill density, and water butter ratio of 3D printed cookie dough and their interactions. The closed porosity of 3D printed cookies increased while the open porosity decreased with an increase in fill density. The baking times required were longer for 3D-printed cookies with higher fill density values. Overall, this study shows the importance of considering the specific ingredient and printing parameters to develop high quality 3D-printed cookies.

Purpose This paper aims to report on the findings of an investigation to compare three different three-dimensional printing (3DP) or additive manufacturing technologies [i.e. fused deposition modeling (FDM), stereolithography (SLA) and material jetting (MJ)] and four different equipment (FDM, SLA, MJP 2600 and Object 260) in terms of their dimensional process capability (dimensional accuracy and surface roughness). It provides a comprehensive and comparative understanding about the level of attainable dimensional accuracy, repeatability and surface roughness of commonly used 3DP technologies. It is expected that these findings will help other researchers and industrialists in choosing the right technology and equipment for a given 3DP application. Design/methodology/approach A benchmark model of 5 × 5 cm with several common and challenging features, such as around protrusion and hole, flat surface, micro-scale ribs and micro-scale long channels was designed and printed repeatedly using four different equipment of three different 3DP technologies. The dimensional accuracy of the printed models was measured using non-contact digital measurement methods. The surface roughness was evaluated using a digital profilometer. Finally, the surface quality and edge sharpness were evaluated under a reflected light ZEISS microscope with a 50× magnification objective. Findings The results show that FDM technology with the used equipment results in a rough surface and loose dimensional accuracy. The SLA printer produced a smoother surface, but resulted in the distortion of thin features (<1 mm). MJ printers, on the other hand, produced comparable surface roughness and dimensional accuracy. However, ProJet MJP 3600 produced sharper edges when compared to the Objet 260 that produced round edges. Originality/value This paper, for the first time, provides a comprehensive comparison of three different commonly used 3DP technologies in terms of their dimensional capability and surface roughness without farther post-processing. Thus, it offers a reliable guideline for design consideration and printer selection based on the target application.

This paper investigates the feasibility of manufacturing hydraulic fittings using additive manufacturing (AM) technologies, specifically Fused Deposition Modeling (FDM) and Stereolithography (SLA). The study addresses the environmental challenge of material waste in conventional fitting production by exploring 3D printing as an alternative manufacturing method. Hydraulic fittings were designed using CAD software: SolidWorks 2022 and fabricated using FDM with PETG (Polyethene Terephthalate Glycol) material and SLA with UV-sensitive photopolymer resin. In present studies, on-destructive leak testing was conducted in accordance with PN-EN 1254-4 and PN-EN 1254, at pressures ranging from 0.1 to 1.0 bar. Dimensional accuracy analysis revealed shrinkage of approximately 1% for SLA-printed parts and 2% for FDM-printed parts. Microscopic examination at 50× and 80× magnification showed superior thread quality in SLA samples compared to FDM, which exhibited visible layer separation and material porosity. Leak testing demonstrated that while the brass reference fitting maintained complete seal integrity, both 3D-printed variants failed to achieve leak tightness under operational pressures, with structural failure occurring at 1.0 bar during tightening. The study showed that FDM with PETG material and SLA with UV-sensitive photopolymer resin, despite achieving acceptable dimensional tolerances (±1-2%), do not meet hydraulic leak tightness requirements at pressures exceeding 0.5 bar in their raw state after printing. The results suggest that alternative material formulations (e.g., carbon fiber-reinforced PEEK for FDM or epoxy engineering resins for SLA) warrant further investigation. Potential avenues for improvement include advanced surface treatment, optimization of printing parameters, and modifications to thread geometry to reduce interthread gaps.

With the development of wearable electronic devices, conductive polymer composites (CPCs) based flexible strain sensors are gaining tremendous popularity. In recent years, the applications of additive manufacturing (AM) technology (also known as 3D printing) in fabricating CPCs based flexible strain sensors have attracted the attention of researchers due to their advantages of mold-free structure, low cost, short time, and high accuracy. AM technology, based on material extrusion, photocuring, and laser sintering, produces complex and high-precision CPCs based wearable sensors through layer-by-layer stacking of printing material. Some high-performance CPCs based strain sensors are developed by employing different 3D printing technologies and printing materials. In this mini-review, we summarize and discuss the performance and applications of 3D printed CPCs based strain sensors in recent years. Finally, the current challenges and prospects of 3D printed strain sensors are also discussed to provide an insight into the future of strain sensors using 3D printing technology.

Rapid vacuum casting has been proven to be a successful method in producing high-quality parts in small series. However, a challenge lies in the selection of proper Additive Manufacturing (AM) technologies for the development of a master pattern for the vacuum casting process. Each AM technologies differ from one another in terms of dimensional accuracy, surface finish, cost and lead times. The aim of this study is to investigate the performance of casting mating parts based on different additive manufacturing patterns for small batch. Three types of AM-based patterns: Fused Deposition Modeling (FDM), Stereolithography (SLA) and Multi-Jet Fusion (MJF) were compared. The Taguchi method, Signal to Noise ratio (S/N), Analysis of Variance (ANOVA) and T-test were conducted in determining the optimized parameters. From the findings, curing time is shown to be a significant parameter for dimensional accuracy and surface finish. Dimensional deviation varies in different directions of parts. For surface finish, there was only a slight change from the master pattern whereas the surface roughness of casting parts decreased within the range 0.23% to 2.85%. Tolerance grades for the selected dimensions of the parts were in the permissible range, based on ISO286-1:2010. When using distinct kinds of AM patterns to create replacement components, design tolerance is needed. It was suggested to select AM technology similar to that had been used for the original components. Battery cover was selected as a case study to represent the mating application parts.

Fine structure impacts highly concentrated starch liquefaction process and product performance

Shedding light on 3D printing: Printing photo-crosslinkable constructs for tissue engineering

This study investigates the augmentation of the mechanical properties of Polylactic Acid (PLA) filaments by incorporating Orange Peel Powder (OPP) as an eco-friendly filler for 3D printing applications. The study delves into optimizing the printing parameters to achieve enhanced mechanical characteristics while maintaining printability. Various 3D printing process parameters were analyzed systematically to assess their impact on tensile strength, flexural strength, and impact resistance. A thorough investigation was conducted to determine the impact of layer height, infill density, and printing speed on the overall mechanical properties of the printed specimens. The findings demonstrate that adding 25% OPP significantly enhances the mechanical strength of the PLA composite without compromising printability, with optimal concentration and printing parameters identified. Thermo gravimetric study reveals that the PLA/OPP composite filament has a higher maximum thermal degradation temperature of 329 °C, compared to the PLA’s temperature of 316 °C. This research contributes to advancing the development of sustainable and mechanically robust materials for additive manufacturing, offering insights into the fabrication of PLA-based biodegradable composites tailored for specific applications in various industries.

Additive manufacturing (AM) is a disruptive technology that enables one to manufacture complex structures reducing both time and manufacturing cost. Among the materials commonly used for AM, thermoplastic elastomers (TPE) are of high interest due to their energy absorption capacity, energy efficiency, cushion factor or damping capacity. Previous investigations have exclusively focused on the optimization of the printing parameters of commercial TPE filaments and the structures to analyse the mechanical properties of the 3D printed parts. In the present paper, the chemical, thermal and mechanical properties for a wide range of commercial thermoplastic polyurethanes (TPU) filaments were investigated. For this purpose, TGA, DSC, 1H-NMR and filament tensile strength experiments were carried out in order to determine the materials characteristics. In addition, compression tests have been carried out to tailor the mechanical properties depending on the 3D printing parameters such as: infill density (10, 20, 50, 80 and 100%) and infill pattern (gyroid, honeycomb and grid). The compression tests were also employed to calculate the specific energy absorption (SEA) and specific damping capacity (SDC) of the materials in order to establish the role of the chemical composition and the geometrical characteristics (infill density and type of infill pattern) on the final properties of the printed part. As a result, optimal SEA and SDC performances were obtained for a honeycomb pattern at a 50% of infill density.