Search results

In binder jetting, the interaction between the liquid binder droplets and the powder particles defines the shape of the printed primitives. The purpose of this study is to explore the interaction of the relative size of powder particles and binder droplets and the subsequent effects on macro-scale part properties.

The effects of different particle size distribution (5–25 µm and 15–45 µm) of stainless steel 316 L powders and droplet sizes (10 and 30 pL) on part density, shrinkage, mechanical strength, pore morphology and distribution are investigated. Experimental samples were fabricated in two different layer thicknesses (50 and 100 µm).

While 15–45 µm samples demonstrated higher green density (53.10 ± 0.25%) than 5–25 µm samples (50.31 ± 1.06%), higher sintered densities were achieved in 5–25 µm samples (70.60 ± 6.18%) compared to 15–45 µm samples (65.23 ± 3.24%). Samples of 5–25 µm also demonstrated superior ultimate tensile strength (94.66 ± 25.92 MPa) compared to 15–45 µm samples (39.34 ± 7.33 MPa). Droplet size effects were found to be negligible on both green and sintered densities; however, specimens printed with 10-pL droplets had higher ultimate tensile strength (79.70 ± 42.31 MPa) compared to those made from 30-pL droplets (54.29 ± 23.35 MPa).

To the best of the authors’ knowledge, this paper details the first report of the combined effects of different particle size distribution with different binder droplet sizes on the part macro-scale properties. The results can inform appropriate process parameters to achieve desired final part properties.

The purpose of this study is to cover the influence of selected printing parameters at a macro and micro-geometrical level, focusing on the dimensions, geometry and surface of printed parts with short carbon fibers reinforced PLA. For this case study, a hollow cylindrical shape is considered, aiming to cover the gap detected in previous works analyzed.

Nowadays, additive manufacturing plays a very important role in the manufacturing industry, as can be seen through its numerous research and applications that can be found. Within the engineering industry, geometrical tolerances are essential for the functionality of the parts and their assembly, but the variability in three-dimensional (3D) printing makes dimensional control a difficult task. Constant development in 3D printing allows, more and more, printed parts with controlled and narrowed geometrical deviations and tolerances. So, it is essential to continue narrowing the studies to achieve the optimal printed parts, optimizing the manufacturing process as well.

Results present the relation between the selected printing parameters and the resulting printed part, showing the main deviations and the eligible values to achieve a better tolerance control. Also, from these results obtained, we present a parametric model that relates the geometrical deviations considered in this study with the printing parameters. It can provide an overview of the piece before printing it and so, adjusting the printing parameters and reducing time and number of printings to achieve a good part.

The main contribution is the study of the geometry selected under a 3D printing process, which is important because it considers parts that are created to fit together and need to comply with the required tolerances. Also, we consider that the parametric model can be a suitable approach to selecting the optimal printing parameters before printing.

Orange peel formation remains to be understood clearly because it is difficult to directly observe a laser-sintered process in a partcake. Therefore, this study aims to provide insight into the orange peel formation mechanism through the nondestructive observation of laser-sintered specimens and their surrounding powders.

This study observed polyamide 12 powder in the vicinity of a laser-sintered specimen via X-ray computed tomography (CT) scanning. The specimen for nondestructive observation was 3D modeled in a hollow box using 3D CAD software. The boxes built using a laser-sintering system contained unsintered surrounding powder and sintered specimens. The box contents were preserved even after the boxes were removed from the partcake. After X-ray CT scanning, the authors broke the boxes and evaluated the unevenness formed on the specimen surface (i.e. the orange peel evaluation).

Voids (not those in sintered parts) generated in the powder in the vicinity of the specimen triggered the orange peel formation. Voids were less likely to form in the build with a 178.5° powder bed than in the build with a 173.5° powder bed. Similarly, the increment in laser energy density effectively suppressed void formation, although there was a tradeoff with overmelting. Thin-walled parts avoided void growth and made the orange peel less noticeable.

To the best of the authors’ knowledge, this study is the first to observe and understand the relationship between voids generated in the powder in the vicinity of sintered parts and orange peel formation.

Fused filament fabrication (FFF) is a type of additive manufacturing (AM) based on materials extrusion. It is the most widely practiced AM route, especially used for polymer-based rapid prototyping and customized product fabrication in relation to aerospace, automotive, architecture, consumer goods and medical applications. During FFF, part quality (surface finish, dimensional accuracy and static mechanical strength) is greatly influenced by several process parameters. The paper aims to study FFF parametric influence on aforesaid part quality aspects. In addition, dynamic analysis of the FFF part is carried out.

Interpretive structural modelling is attempted to articulate interrelationships that exist amongst FFF parameters. Next, a few specimens are fabricated using acrylonitrile butadiene styrene plastic at varied build orientation and build style. Effects of build orientation and build style on part’s ultimate tensile strength, flexure strength along with width build time are studied. Prototype beams (of different thickness) are fabricated by varying build style. Instrumental impact hammer Modal analysis is performed on the cantilever beams (cantilever support) to obtain the natural frequencies (first mode). Parametric influence on natural frequencies is also studied.

Static mechanical properties (tensile and flexure strength) are greatly influenced by build style and build orientation. Natural frequency (NF) of prototype beams is highly influenced by the build style and beam thickness.

FFF built parts when subjected to application, may have to face a variety of external dynamic loads. If frequency of induced vibration (due to external force) matches with NF of the component part, resonance is incurred. To avoid occurrence of resonance, operational frequency (frequency of externally applied forces) must be lower/ higher than the NF. Because NF depends on mass and stiffness, and boundary conditions, FFF parts produced through varying build style may definitely correspond to varied NF. This aspect is explained in this work.

Polymer laser powder bed fusion (PBF-LB/P) is an additive manufacturing technology that is sustainable due to the possibility of recycling the powder multiple times and allowing the fabrication of gears without the aid of support structures and subsequent assembly. However, there are constraints in the process that negatively affect its adoption compared to other additive technologies such as material extrusion to produce gears. This study aims to demonstrate that it is possible to overcome the problems due to the physics of the process to produce accurate mechanism.

Technological aspects such as orientation, wheel-shaft thicknesses and degree of powder recycling were examined. Furthermore, the evolving tooth profile was considered as a design parameter to provide a manufacturability map of gear-based mechanisms.

Results show that there are some differences in the functioning of the gear depending on the type of powder used, 100% virgin or 50% virgin and 50% recycled for five cycles. The application of a groove on a gear produced with 100% virgin powder allows the mechanism to be easily unlocked regardless of the orientation and wheel-shaft thicknesses. The application of a specific evolutionary profile independent of the diameter of the reference circle on vertically oriented gears guarantees rotation continuity while preserving the functionality of the assembled mechanism.

In the literature, there are various studies on material aging and reuse in the PBF-LB/P process, mainly focused on the powder deterioration mechanism, powder fluidity, microstructure and mechanical properties of the parts and process parameters. This study, instead, was focused on the functioning of gears, which represent one of the applications in which this technology can have great success, by analyzing the two main effects that can compromise it: recycled powder and vertical orientation during construction.

The purpose of this study was conducted at an enterprise that produces fasteners and is one of the leading companies in the sector in terms of market share. Possible defects in the coating of bolts and nuts either lead to products being scrapped or all of the coating process being repeated from beginning to end. In both cases, the enterprise faces a waste of time and excessive costs. Through this project, the six sigma theory and its means were effectively used to improve the efficiency and quality management of the company. The selection of the six sigma project has also contributed to the creation of various documents to be used for project screening and evaluation of financial results.

Six sigma is an optimization strategy that is used to improve the profitability of businesses, avoid waste, scrap and losses, reduce costs and improve the effectiveness of all activities to meet or exceed customers’ needs and expectations. Six sigma’s process improvement model, known as Definition-Measurement-Analysis-Improvement-Control, contributes to the economic and technical achievements of businesses. The normal distribution of a process should be within ±3 sigma of the mean. This represents a scale of 99.7% certainty. However, improving the process through the utilization of the six sigma rule, which accepts normal variabilities of processes twice as strict, will result in an error rate of 3.4 per million instead of 2,700 per million for each product or service.

Using six sigma practices to reduce the costs associated with low quality and to increase economic added value became a cultural practice. With this, the continuation of six sigma practices throughout the Company was intended. The annual cost reduction achieved with the utilization of six sigma practices can be up to $21,780. When time savings are also considered, a loss reduction of about $30,000 each year can be achieved. The coating thickness efficiency increased from 85% to 95% after the improvements made through the six sigma project. There is a significant increase in the efficiency of coating thickness. In addition, the coating thickness efficiency is also close to the target value of 95%–97%.

The results of the study were optimized with the help of deep learning. The performance of the model created in deep learning was quite close to the actual performance. This result implicates the validity of the improvement work. The results may act as a guide for the use of deep learning in new projects.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

Selective laser melting and electron beam melting processes are well-known for the additive manufacturing of metal parts. Metal powder bed fusion (MPBF) is a common term for them. The MPBF process can empower the manufacturing of intricate shapes by reducing the use of special tools, shortening the supply chain and allowing small batches. However, the MPBF process suffers from many quality issues. In literature, several works are recorded for qualification of the MPBF part. The purpose of this study is to recollect those works done for quality control and report their helpful findings for further research and development.

A systematic literature review was conducted to highlight the major quality issues in the MPBF process and its root causes. Further, the works reported in the literature for mitigation of these issues are classified and discussed in five categories: experimental investigation, finite element method-based numerical models, physics-based analytical models, in-situ control using artificial intelligence (AI) and machine learning (ML) methods and statistical approaches. A comparison is also prepared among these strategies based on their suitability and limitations. Additionally, improvements in MPBF printers are pointed out to enhance the part quality.

Analytical models require less computational time to simulate the MPBF process and need a smaller number of experiments to confirm the results. They can be used as an efficient process parameter planning tool to print metal parts for noncritical applications. The AI-ML based quality control is also suitable for MPBF processes as it can control many processing parameters that may affect the quality of the MPBF part. Moreover, capabilities of MPBF printers like thinner layer thickness, smaller beam diameter, multiple lasers and high build temperature range can help in quality control.

This study converts the piecemeal data on MPBF part qualification methods into interesting information and presents it in tabular form under each strategy. This tabular information provides the basis for further quality improvement efforts in the MPBF process.

This study references researchers and practitioners on recent quality control efforts and their significant findings for a better quality of MPBF part.

Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a framework for estimating the printability of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application.

This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning approach. The input to the error prediction artificial neural network (ANN) is per vertex information extracted from the mesh of the model to be manufactured. The output of the ANN is the estimated average per vertex error for the fabricated object. This error is then used along with other global and per part information in a framework for estimating the printability of the model, that is, the probability of being fabricated correctly on a certain AM technology, for a specific application domain.

A thorough experimental evaluation was conducted on binder jetting technology for both the error prediction approach and the printability estimation framework.

This study presents a method for predicting dimensional errors with high accuracy and a completely novel approach for estimating the probability of a CAD model to be fabricated without significant failures or errors that make it inappropriate for a specific application.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.