Manufacture Parts Research Articles

Risk Assessment in the Selection of Parts for Additive Manufacturing

Risk Assessment in the Selection of Parts for Additive Manufacturing

Investigating the effects of powder feeding rate on microstructure transformation in linear laser beam additive manufactured thick-walled structures

The coarse columnar crystals that grow through multiple layers are commonly observed in laser additive manufactured structures, with fractures tending to occur in regions where there are abrupt changes in grain morphology. This can lead to a degradation of the mechanical properties of the samples. In this study on laser additive manufacturing, thick-walled structures made of 304 stainless steel were created using a linear beam spot with a rectangular powder feeding nozzle. By adjusting the powder feeding rate to influence the thermal cycling characteristics during the laser additive manufacturing process, a hierarchical grain structure that combines coarse and fine equiaxed grains was achieved, enhancing the forming efficiency and overall mechanical performance of the structure. The results from the thermal cycling characteristics and microstructure analysis indicate that increasing the powder feeding rate during the additive manufacturing process can decrease the hierarchical cooling rate, temperature gradient, and heat accumulation effect. This reduction in turn decreases the grain size and facilitates the transformation of columnar crystals into equiaxed crystals. Furthermore, the transformation of low-angle grain boundaries into high-angle grain boundaries in the interlayer region helps to reduce stress concentration, weaken anisotropic tendencies, and mitigate intergranular fracture tendencies, ultimately improving the mechanical properties of the overall structure. In actual engineering applications, the powder flow can be adjusted to control the temperature gradient and cooling rate of the deposition layer, thereby altering the grain morphology and optimizing the mechanical properties of additive manufacturing parts.

Drilling parameter optimisation for three-dimensional prints of acrylonitrile butadiene styrene

Abstract Over the past decade, additive manufacturing has transformed the industry, leading to a range of innovative products. In this study, the acrylonitrile butadiene styrene specimen was produced using the fused deposition modeling process. To assess the effectiveness of additive manufacturing parts in final product development, it’s crucial to establish the optimal drilling process parameters to minimize thrust force and torque. The investigation utilized two different drill geometries. Drilling with an end mill resulted in a 24.22% reduction in thrust force and a 12.37% decrease in torque compared to using a twist drill. Additionally, factors such as orientation, infill density, feed rate, and drill geometry have a significant impact on drilling forces.

Importance of RNase monitoring during large-scale manufacturing and analysis of mRNA-LNP based vaccines

Ribonucleases (RNases) are ubiquitous in nature, being able to cleave a wide range of polyribonucleotides. While the presence of microbial and viral contamination in sterile manufacturing is highly studied and controlled, there are no standardized practices for evaluating RNase in the production facility. Since the COVID-19 pandemic, mRNA-LNP based vaccines have become part of routine large-scale manufacturing. The unstable nature of mRNA poses new challenges to safeguard the working efficacy of mRNA – Lipid nanoparticle (LNP) based vaccines or therapeutics, where the presence of RNase in the formulation process could have a profound impact on the mRNA integrity. In this article, lessons learned are presented with respect to the evaluation of RNase contamination during LNP drug product formulation and analysis. Using sensitive detection methods, the potential presence of RNase in the manufacturing of mRNA-LNPs was investigated. Additionally, capillary gel electrophoresis (CGE) data, used to measure mRNA integrity, demonstrate the quality of the active mRNA substance and importance of suitable RNase control strategies. The results and cases presented in this paper should pave the way forward for evaluation and control strategies dedicated to mRNA-LNP based vaccines and therapeutics.

Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing

With the increase in the usage of industrial robots for manufacturing applications, there will be a corresponding rise in energy consumption. Especially, robotic additive manufacturing (AM) has demonstrated significant potential for working autonomously in hazardous or extraterrestrial settings, as well as for producing large-scale structures. Recent advancements have made it possible for teams of robots to collaborate for printing structures even larger than their sizes together. Utilizing robots efficiently for AM is essential for achieving energy sustainability goals. The literature on energy models for robots and AM processes lacks a comprehensive energy model for robotic AM. To address this gap, a multi-point trajectory-based energy model is proposed for robotic AM. The model is created to help users manufacture parts in an energy-efficient way. These simulations require a significant amount of computation time, which limits their usage to offline purposes only. Hence, we propose two energy surrogate models for real-time predictions: a pure data-driven model and a kinematics-guided data-driven model. The kinematics-guided approach is an enhanced version of pure data-driven model which learns from inverse kinematics solutions along the trajectory to understand the robot’s dynamics and kinematics. There were two test cases conducted. The first test case consisted of paths that were feasible, printable, and within the robot’s reach, while the second test case consisted of a mixture of paths that were both feasible and infeasible. In comparison to the pure data-driven approach, the kinematics-guided approach stood out in both testing cases. Two real-world case studies have been used to demonstrate that the proposed approach can be used in lieu of simulations for real-time applications. Especially in the second case, where the part was placed very close to the robot making a portion of the part infeasible to print, the proposed approach correctly identified that it was not possible to print the geometry due to the robot’s kinematics. The proposed kinematics-guided approach is 195 and 700 times faster than the simulation results, respectively, in two case studies, and thus supporting the use of the proposed approach for real-time applications.

A lightweight parallel attention residual network for tile defect recognition

In modern industrial production, permanent magnet motors are an indispensable part of industrial manufacturing. The quality of the magnetic tiles directly affects the working performance of the permanent magnet motors, making the detection of defects on the surface of magnetic tiles critically important. However, due to the small size of defects on the tile image and the reflectivity of the defective surface, the details of image characteristics are not prominently acquired.These problems bring a lot of difficulties for the recognition of magnetic tile defects. In this paper, a magnetic tile defect detection method is proposed for the probAlems of unclear image features and small defects. First, the image is processed using linear variation to enhance the image detail features. Then, by introducing the inverted bottleneck block structure in MobileNetV2, the Attention Parallel Residual Convolution Block (APR) is proposed, and the Lightweight Parallel Attention Residual Network (LPAR-Net) is built. In APR Block, 7 × 7 convolution is introduced so that the model can extract spatial features from a larger range, and weighted fusion of input images by residual structure. In addition, in this paper, CBAM is improved, split into two parts and inserted into APR Block. Finally, the mainstream image classification models and the LPAR-Net proposed in this paper are used for comparison, respectively. The experimental results show that the method achieves 93.63% accuracy on the adopted dataset, which is better than the existing mainstream image classification network models DenseNet, MobileNet, ConvNext and so on. In addition, this paper introduces a strip steel surface defect dataset and compares it with the above image classification model, which verifies that the detection method proposed in this paper still has strong recognition capability.

Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study

Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional (non-digital) manufacturing. In this paper, we explore the key elements required for successful implementation of a virtual warehouse for legacy parts based on a combination of part digitalization, on-demand manufacturing, and part validation. Our proposed framework for adoption of virtual warehouse includes development of a digital inventory which includes supply chain and manufacturability data, identification, and selection of suitable parts for on-demand manufacturing, selection of on-demand manufacturing technology, fit-for-purpose validation of the parts. Our framework is exemplified through a case study, and we conclude that the building of an effective virtual warehouse requires several enablers, including availability of digital data about technical and supply chain characteristics of parts, but also a suitable part identification tool. This part identification tool needs to be flexible to include comparison with reference parts already produced by different on-demand manufacturing technologies.

Effect of Hot Isostatic Pressing and Solution Heat Treatment on the Microstructure and Mechanical Properties of Ti-6Al-4V Alloy Manufactured by Selective Laser Melting

A powder-bed-based additive manufacturing process called electron beam melting (EBM) is defined by high temperature gradients during solidification, which produces an extremely fine microstructure compared to the traditional cast material. However, porosity and segregation defects are still present on a smaller scale which may lead to a reduction in mechanical properties. It is important to have a better knowledge of the influence of post-fabrication treatments on the microstructure and mechanical characteristics before the use of additive manufacturing parts in specific applications. In this study, the effects of solution heat treatment (SHT) and hot isostatic pressing (HIP) on the microstructure and mechanical properties of Ti-6Al-4V alloy fabricated by the EBM process have been investigated. The SHT and HIP treatments can significantly improve the ductility of EBM Ti-6Al-4V due to the coarsening of α laths and the formation of β grains.

Reactive formation of C3N4 as a by-product of AISI 1070 parts produced by laser powder bed fusion in N2 atmosphere

AbstractIn metal additive manufacturing (AM), inert gases are traditionally used to achieve a controlled atmosphere and mitigate the effects of residual reactive gases. However, the interaction between gases and laser processes, particularly in reactive laser powder bed fusion (RL-PBF) technology, offers the possibility of opening up new avenues for material synthesis. In this experimental work, the authors observed the presence of C3N4 in the residual powder during the manufacture of AISI 1070 steel parts by L-PBF, indicating a reactive process occurred during parts production. This investigation revealed the formation in the working chamber of a waste product containing C3N4 carbon nitride, due to the reaction between the carbon released from the steel and the nitrogen in the chamber. Remarkably, despite carbon depletion, the final product of AISI 1070 steel complies with the specifications of use. Hence, the L-PBF machine was modified to allow black powder sampling from various locations in the chamber. Authors attempted to enhance the production of the C3N4 material by increasing the SED up to 7143 J/mm2 to sublimate a pure graphite rod and concurrently manufacture parts in AISI 1070, in a nitrogen atmosphere. The results obtained at higher SED values showed that in both cases (graphite rod or AISI 1070 steel) a C3N4 compound in the black powder is formed in the investigated atmosphere by reaction of nitrogen atoms with the carbon atoms vaporized by the laser beam. Thus, the study highlights the novel achievement of synthesizing carbon nitride as a high-value by-product while producing functional AISI 1070 steel parts via L-PBF through reaction with nitrogen atmosphere.

Tribological Performance of Additive Manufactured PLA-Based Parts.

Polymer additive manufacturing has advanced from prototyping to producing essential parts with improved precision and versatility. Despite challenges like surface finish and wear resistance, new materials and metallic reinforcements in polymers have expanded its applications, enabling stronger, more durable parts for demanding industries like aerospace and structural engineering. This research investigates the tribological behaviour of FFF surfaces by integrating copper and aluminium reinforcement particles into a PLA (polylactic acid) matrix. Pin-on-disc tests were conducted to evaluate friction coefficients and wear rates. Statistical analysis was performed to study the correlation of the main process variables. The results confirmed that reinforced materials offer interesting characteristics despite their complex use, with the roughness of the fabricated parts increasing by more than 300%. This leads to an increase in the coefficient of friction, which is related to the variation in the material's mechanical properties, as the hardness increases by more than 75% for materials reinforced with Al. Despite this, their performance is more stable, and the volume of material lost due to wear is reduced by half. These results highlight the potential of reinforced polymers to improve the performance and durability of components manufactured through additive processes.

Mass reduction method for topology optimisation of a Ti6Al4V part for additive manufacturing

Abstract Additive manufacturing and topology optimization provide new possibilities to produce complex parts. They can be used separately but with joint applications as a mutually reinforcing solution in component development tasks. The results obtained using the design software can be refined even further depending on the specific goal set. This paper deals with mass reduction with stiffness-based topology optimization of a structural component. The effect of different design spaces, load cases, and design parameters were examined. Then, the new part was validated with FEA simulation. After the validation, the part was prepared for 3D metal printing. Based on the research results, we present a methodology that can be used as a solution considering the software’s limitations and the development of the specific component. Applying the methodology developed in the research makes it possible to achieve mass minimization on other parts with a similar method.

Testing Protocol Development for the Fracture Toughness of Parts Built with Big Area Additive Manufacturing.

The mechanical testing of additively manufactured parts has largely relied on the existing standards developed for traditional manufacturing. While this approach leverages the investment made in current standards development, it inaccurately assumes that the mechanical response of additive manufacturing (AM) parts is identical to that of parts manufactured through traditional processes. When considering thermoplastic, material extrusion AM, the differences in response can be attributed to an AM part's inherent inhomogeneity caused by porosity, interlayer zones, and surface texture. Additionally, the interlayer bonding of parts printed with large-scale AM is difficult to adequately assess, as much testing is performed such that stress is distributed across many layer interfaces; therefore, the lack of AM-specific standards to assess interlayer bonding is a significant research gap. To quantify interlayer bonding via fracture toughness, double cantilever beam (DCB) testing has been used for some AM materials, and DCB has been generally used for a variety of materials including metal, wood, and laminates. Mode I DCB testing was performed on thermoplastic matrix composites printed with Big Area Additive Manufacturing (BAAM). Of particular interest was the notch shape and deflection speed during testing. The results examine the differences when using two notch types and three deflection speeds. The testing method introduced by the following paper differentiates itself from the ones described in the standards used by modernizing the methodology. This was conducted with the introduction of Digital Image Correlation (DIC) to gather displacement and load data simultaneously without human intervention.

Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring.

Tool condition monitoring technology is an indispensable part of intelligent manufacturing. Most current research focuses on complex signal processing techniques or advanced deep learning algorithms to improve prediction performance without fully leveraging the end-to-end advantages of deep learning. The challenge lies in transforming multi-sensor raw data into input data suitable for direct model feeding, all while minimizing data scale and preserving sufficient temporal interpretation of tool wear. However, there is no clear reference standard for this so far. In light of this, this paper innovatively explores the processing methods that transform raw data into input data for deep learning models, a process known as an input paradigm. This paper introduces three new input paradigms: the downsampling paradigm, the periodic paradigm, and the subsequence paradigm. Then an improved hybrid model that combines a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) was employed to validate the model's performance. The subsequence paradigm demonstrated considerable superiority in prediction results based on the PHM2010 dataset, as the newly generated time series maintained the integrity of the raw data. Further investigation revealed that, with 120 subsequences and the temporal indicator being the maximum value, the model's mean absolute error (MAE) and root mean square error (RMSE) were the lowest after threefold cross-validation, outperforming several classical and contemporary methods. The methods explored in this paper provide references for designing input data for deep learning models, helping to enhance the end-to-end potential of deep learning models, and promoting the industrial deployment and practical application of tool condition monitoring systems.

Tailoring the vibration characteristics of carbon fiber-reinforced polylactic acid in fused filament fabrication process

The Fused Filament Fabrication (FFF) process has gained significant attention for its ability to manufacture parts using advanced polymeric materials, such as carbon fiber-reinforced polylactic acid (CF-PLA). By adjusting the printing process parameters of the CF-PLA, the optimized mechanical properties can be achieved with less chances of flaws. Structural integrity of the components plays an important role in analyzing the durability of the product which can be analyzed using modal analysis. This research study focuses on analyzing the effect of process parameters on the vibration damping characteristics of CF-PLA components produced by the FFF process. The Taguchi method is used to model the relationship between process parameters and vibration performance metrics. Based on the vibration study, the optimal process parameters were determined as 60% infill density, 0.08 mm slice thickness, and hexagonal pattern. This study shows that the natural frequency and damping increases with increase in infill percent but decreases with increase in slice thickness. The closely aligned values between the predicted and experimental results suggest that the developed model is effective in predicting the vibration characteristics of CF-PLA composites. By accurately adjusting the process parameters, the study’s findings—such as frequencies and damping ratio—provide manufacturers with important and dependable CF-PLA Fused Deposition Modeling components.

Developable forming of high-strength corrugated sheet metal

The classical theory of folded developables suggests that curved crease folding can be applied to sheet metal of high strength to manufacture parts with complex geometries. However, its application has been limited to sheet metals with low strength and requires grooving or perforation of the material at the fold lines. Applying the geometrical concept otherwise remains unexplored despite its potential in sheet metal-intensive industries like automotive, transportation, and construction. The current study introduces a novel sheet metal forming technique called developable forming. The theory of folded developables is applied to design a die tool for flange drawing a developable-shaped steel sheet. A developable connection is produced from high-strength material without resorting to thickness accommodation techniques. Experimental findings establish that the novel process does not follow the premises of the theory of folded developables. New shape effects are observed and the terms curving and coning are introduced to describe these. Additionally, significant redundant strains are identified in the flange, highlighting the different deformation modes in the developable forming process. A higher formability is achieved for a material with limited uniaxial elongation highlighting the need for further investigation on the mechanisms involved in the novel process.

Using virtual reality to orient parts for additive manufacturing and its effects on manufacturability and experiential outcomes

Additive manufacturing (AM) enables the fabrication of geometrically complex designs through layer-by-layer joining of material along single or multiple directions. To determine favorable design and manufacturing solutions, designers must navigate this 3D spatial complexity while ensuring the functionality and manufacturability of their designs. Evaluating the manufacturability of their solutions necessitates modalities that help naturally visualize AM processes and the designs enabled by them. Digitally non-immersive visualization can reduce this expense, but digital immersion has the potential to further improve the experience before building. This research investigates how differences in immersion between computer-aided (CAx) and virtual reality (VR) environments affect a designer’s approach to solving a build-with-AM (BAM) problem and its outcomes. First, it studies how immersion affects determining favorable build orientations when considering the additive manufacturability outcomes of designs of varying complexity. Second, it studies how immersion affects the participants’ experiential outcomes, including evaluation time, attempts made, and cognitive load when solving the BAM problem. Analysis reveals that as design complexity increases, visualizing and manufacturing designs in VR improves additive manufacturability outcomes by reducing build time and support material usage compared to CAx, reducing manufacturing costs by up to 4.61 % ($32) per part. Using immersive VR also helps designers determine favorable build orientations faster with fewer attempts and without increasing the cognitive load experienced. These findings present important implications for the role of immersive experiences in preparing designers to quickly produce lower-cost and sustainable manufacturing solutions with AM.

Magnetic field-assisted electrochemical additive manufacturing of nickel structure: Growth mechanism and microstructural evolution

The microstructure and crystal texture of magnetically assisted electrochemical additive manufacturing parts play crucial roles in their mechanical properties and applications. This paper elucidates the electrocrystallization process of electrochemical additive manufacturing by constructing a magnetically assisted setup and exploring both nucleation mechanisms and growth kinetics. The electrochemical behavior of the system was analyzed using electrochemical methods under different magnetic field strengths. Nickel structures with aspect ratios of 7.0 and 8.2 were fabricated using the experimental setup, and their morphology, crystal texture, and microstructure were characterized. The results showed that without applying a magnetic field, the nucleation mechanism followed three-dimensional instantaneous nucleation under diffusion control. Under magnetic field conditions, forced convection, double layer charging, and hydrogen evolution reactions were combined to modify the S-H nucleation model, demonstrating that the magnetic field can significantly enhance the nucleation rate and number of nuclei. The initial growth of the microstructure did not exhibit preferential solid orientation, primarily forming refined equiaxed grains. Subsequently, a characteristic pentagonal structure developed, followed by spiral dislocation growth, with a significant transformation of equiaxed grains into columnar crystals. The crystal orientation eventually evolved to <110>, with many twin boundaries, among which Σ3 grain boundaries accounted for more than 55 %. Applying a magnetic field improved processing efficiency, altered the morphology and texture of the material, refined the grains, and increased the types of twins. These results provide theoretical and experimental support for electrochemical additive manufacturing.

Adaptive Neuro-Fuzzy Inference System-Based Predictive Modeling of Mechanical Properties in Additive Manufacturing

Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process.

Novel sensorized additive manufacturing-based enlighted tooling concepts for aeronautical parts

This paper presents lightweight tooling concepts based on additive manufacturing, with the aim of developing advanced tooling systems as well as installing sensors for real-time monitoring and control during the anchoring and manufacturing of aeronautical parts. Leveraging additive manufacturing techniques in the production of tooling yields benefits in manufacturing flexibility and material usage. These concepts transform traditional tooling systems into active, intelligent tools, improving the manufacturing process and part quality. Integrated sensors measure variables such as displacement, humidity and temperature allowing data analysis and correlation with process quality variables such as accuracy errors, tolerances achieved and surface finish. In addition to sensor integration, additive manufacturing by directed energy arc and wire deposition (DED-arc) has been selected for part manufacturing. The research includes the mechanical characterisation of the material and the microstructure of the material once manufactured by DED-arc. Design for additive manufacturing" principles guide the design process to effectively exploit the capabilities of DED-arc. These turrets, equipped with sensors, allow real-time monitoring and control of turret deformation during clamping and manufacturing of aeronautical parts. As a first step, deformation monitoring is carried out within the defined tolerance of ± 0.15, which allows a control point to be established in the turret. Future analysis of the sensor data will allow correlations with process quality variables to be established. Remarkably, the optimised version of the turret after applying DED technology weighed only 2.2 kg, significantly lighter than the original 6 kg version. Additive manufacturing and the use of lightweight structures for fixture fabrication, followed by the addition of sensors, provide valuable information and control, improving process efficiency and part quality. This research contributes to the development of intelligent and efficient tool systems for aeronautical applications.

First Approach in Analysis of Tool Wear When Milling Additive Manufacturing (AM) Parts

Wire arc additive manufacturing (WAAM) and laser-based powder bed fusion (L-PBF) are additive manufacturing (AM) processes that allow the manufacturing of complex part geometries. The manufacturing of AM parts does not result in high-quality functional surfaces; therefore, postprocessing such as milling is usually required. For L-PBF parts, the support structures and, for WAAM parts, the undulating surface are usually removed after AM processes. These two application-related cases are investigated in this work, with the conclusion that support structure milling and the milling of the surface of WAAM parts lead to the dimensionally increased wear of milling tools in comparison to milling of solid material.