Defect-free Parts Research Articles

Drying Behaviour of 50 mm Thick Turkey Oak Lumber

Turkey oak lumber is difficult to dry, being prone to cracks even at low temperatures. The main objective of the research was to test different drying conditions in order to establish whether air-drying or kiln-drying from a green state was more suitable; if the lumber width had an influence on the drying quality; if prior steaming had a positive effect upon the kiln-drying time and quality; if end cracks could be avoided by protecting the board ends; and what the most appropriate kiln-drying conditions were, with conventional drying. The research results recommend kiln-drying as a better alternative than air-drying in terms of both time and quality, but very mild conditions must be applied. Wide lumber parts cracked both when air-dried and when kiln-dried. All defect-free lumber parts dried accurately, with no deterioration. Steaming had a positive effect upon the drying quality; no steamed part cracked. The end protection of the boards was efficient only in the case of kiln-drying.

Experimental investigation for multi-defect detection of industrial pipeline based on passive infrared thermal imaging analysis: A case study

Corrosion of pipe walls is a major hidden hazard for the safety of chemical enterprises; therefore, detection of the depth of pipe wall corrosion defects is extremely important. In this study, passive thermal imaging technology was used to detect and evaluate corrosion defects of different shapes in pipe walls and of different depths for the same shape. Three-dimensional finite element models of defects with different depths were established using COMSOL, and the thermal response mechanisms of defects with different depths and the effects of defect depth and shape on heat transfer were analyzed. The thermal response mechanism of defects with different shapes and depths (1–3 mm) was also analyzed experimentally and compared with that of defect-free parts to analyze the safety of the pipeline and verify the results of the numerical simulation analysis. The results indicated that the level of fluid temperature determined the accuracy of the test, and at the same temperature, the defects at a depth of 3 mm exhibited multiple peaks and a large peak area. However, defects with a depth of 2 mm exhibited the opposite behavior, and the heating rate of 3-mm deep defects during the temperature change was 1.08 times higher than that for 2 mm-deep defects. This study provides a reference for detecting leaks in chemical pipelines.

Comparison of microstructural characteristics and mechanical properties of the high-strength low-alloy steels fabricated by wire arc additive manufacturing versus conventional casting

A comparison study on the microstructure and mechanical property between the wire arc additive manufacture (WAAM) high-strength low-alloy (HSLA) steel and the as-cast one was carried out. A more optimized CMT-based WAAM parameter was selected to fabricate a nearly defect-free single-passed thin-wall part. It was found that the main body of the deposit at the middle zone exhibited a typical granular bainite (GB) duplex microstructure consisting of bainite ferrite (BF) and martensite/austenite (M/A) island. Also, the middle zone exhibited a band segregation between the two adjacent columnar prior-γ grains. The band segregation distance is apparently shorter compared with the cellular segregation distance in the as-cast steel. In addition, the as-cast steel has the bainitic microstructure consisting of larger grain-sized BF and nano-twinned martensite island. On a basis of the microstructure characteristics, the nonequilibrium solidification and solid-solid phase transition accompanying with the segregation behavior during the WAAM deposition of the molten pools were studied. Such the above structured as-deposited steel, in comparison to the as-cast one, was decreased in σy from 594.9 MPa to 426.2 MPa, but increased in σuts from 778.6 MPa to 858.4 MPa, and also greatly increased in εtotal from 10.9% to 20.7%. The apparently enhanced εtotal of the as-deposited steel was interpreted in connection with the fabrication processing-induced microstructural variation of the two steels.

Microstructure and mechanical properties of an ultra-high strength steel fabricated by laser hybrid additive manufacturing

35CrMnSiA is a type of ultra-high-strength steel that finds extensive application in key components characterized by geometrically regular structures and complex extensions, such as structural shells. Laser hybrid additive manufacturing (LHAM) is an advanced manufacturing technology that combines the benefits of traditional manufacturing and additive manufacturing. LHAM has the potential for rapid and cost-effective production of these components. However, the formation mechanism of the microstructure in different zones of 35CrMnSiA steel fabricated by LHAM and its impact on the overall tensile performance remain unclear, thereby tremendously restricting its application. To address this issue, this study employed wrought 35CrMnSiA steel as the base material and utilized directed energy deposition (DED) to fabricate defect-free LHAM parts. The microstructure of LHAM-fabricated 35CrMnSiA steel is location-related. The laser deposition zone comprised ferrite with internal and boundary-distributed carbides, whereas the substrate zone comprised martensite. The heat-affected zone exhibited a gradient microstructure between the two. The microstructure in the deposition zone was primarily governed by solidification behavior and thermal history during the deposition process, while the heat-affected zone was mainly influenced by recrystallization. The non-uniform characteristics of the microstructure significantly influenced its mechanical properties, resulting in a “cask effect,” namely, the tensile performance of LHAM-fabricated 35CrMnSiA steel depended on the weakest laser deposition zone. The ultimate tensile strength and elongation of LHAM-fabricated 35CrMnSiA steel are 959 MP and 15.2%, respectively. This study establishes a basis for controlling the microstructure and optimizing the performance of hybrid-manufactured ultra-high-strength steel.

Additive manufacturing process parameter determination for a new Fe-C-Cu alloy

As a potential replacement for stainless steel alloys commonly used to print parts with laser powder bed fusion, a new Cu precipitation strengthened ferrous alloy, with composition Fe-0.2C-6Cu (wt%), was recently developed. This material is Co- and Ni-free, printable, has mechanical properties comparable to that of high strength stainless steels (approximately 1300 MPa UTS), and is cost-advantaged relative to existing low-alloy steels for additive manufacturing of parts with complex shapes. To gain traction for broader application, optimal laser powder bed fusion (LPBF) processing conditions to produce nearly defect-free parts without compromising mechanical strength are needed. Here, an optimal processing window based upon laser speeds that achieve minimal porosity was quantified via comparisons of measured melt pool penetration, scan speeds, printed material density and laser beam energy density on a commercial LPBF unit operating at 350 W, 80 μm hatch spacing, and 50 μm layer thickness based upon optimal parameters for 17-4PH. The window is 300 to 500 mm/s to achieve >99.8 % dense Fe-0.2C-6Cu (wt%) specimens. Other defects such as burning, spatter, and lack of fusion were also avoided in this window. The window was validated with in-situ synchrotron X-ray imaging that enabled visualization of the vapor cavity, melting, and solidification during a single laser track scan on a miniature powder bed sample. In addition, in-situ infrared imaging provided temperature fields, cooling rate and solidification range, and confirmed minimal printing defects and spatter within the processing window.

Crack reduction in Inconel 939 with Si addition processed by laser powder bed fusion additive manufacturing

Inconel 939 (IN939) suffers from cracking in the laser powder bed fusion based additive manufacturing (LPBF-AM) process. Previous studies attempted to minimize the crack density by reducing the Si content. In contrast, this paper demonstrates that Si addition can substantially lower the crack density in LPBF-AM IN939 alloy parts. Si addition into IN939 shows a moderate impact on the crystallographic orientation and grain size of LPBF-AM parts, and promotes the formation of precipitates. Mechanical tests show that Si addition increases tensile strength and indentation hardness but decreases ductility. This work suggests that the dominant cracking mode in LPBF-AM IN939 alloy parts is solidification cracking, the suppression of which by Si addition can be qualitatively rationalized using Clyne’s model and Kou’s model. The present findings provide a different perspective on designing defect-free LPBF-AM Ni-based superalloy parts with balanced mechanical properties, one which can be extended to other alloy systems.

Spark plasma sintering of 13Ni-400 maraging steel: Enhancement of mechanical properties through surface modification

The present work aimed to reduce the amount of gas-atomized spherical powder, cost reduction, and reduction in the use of inert gas (N2, Ar) used during the selective laser melting process but to have similar properties and functionalities through the spark plasma sintering (SPS) process. In this work, 13Ni-400 maraging steel disks were fabricated using Spark Plasma Sintering (SPS) using optimized process parameters. The SPS samples are then subjected to surface melting and layered deposition processes. A detailed microstructural characterization and mechanical testing (hardness and compression properties) were carried out on these samples. The results indicated that the surface melted specimen exhibited the best combination of mechanical properties and microstructure, with a highly refined and uniform microstructure. This was attributed to the unique processing conditions employed during surface melting allowing the material to rapidly solidify. The surface melted samples show the highest yield strength among the three conditions (SPS, SPS + surface melting, and SPS + layer deposition). However, both the surface melting and layer deposited samples have similar ultimate compressive strength. Overall, the study offers a promising approach for producing defect-free parts with maximum densification and improved mechanical properties for newly developed 13Ni-400 maraging steel.

An insight into the effects of epoxy coating agent and sintering temperature on vat photopolymerization-printed Al2O3 parts

As an additive manufacturing technology, vat photopolymerization (VPP) can print ceramic parts with high resolution and good surface quality at a low cost. Therefore, VPP has been widely used to process a variety of ceramic materials. Despite these benefits, it is still a challenge to fabricate defect-free ceramic parts owing to ceramic materials’ inherent brittleness and low toughness. To overcome these issues, two types of coating agents (i.e., E44 and E51 coating agents) are selected to coat alumina (Al2O3) parts by the authors. Experimental results show that the parts coated with the coating agents demonstrate fewer defects, better physical properties, and enhanced mechanical properties (including flexural and compressive properties), compared with the uncoated Al2O3 parts. Particularly, the average surface roughness of coated Al2O3 parts can be reduced from 2 μm to 35–180 nm. In addition, the absorbed energy can be increased by around 17% when E51 coating agent is adopted. To study stress distribution changes and strengthening and toughening mechanisms induced by the coating agents, finite element modeling analysis and molecular dynamic simulations have been performed.

Literature Review on Thermomechanical Modelling and Analysis of Residual Stress Effects in Wire Arc Additive Manufacturing

The wire arc additive manufacturing (WAAM) process is a 3D metal-printing technique that builds components by depositing beads of molten metal wire pool in a layer-by-layer style. Even though manufactured parts commonly suffer from defects, the search to minimize defects in the product is a continuing process, for instance, using modeling techniques. In areas where thermal energy is involved, thermomechanical modeling is one of the methods used to determine the input thermal load and its effect on the products. In the WAAM fabrication process, the thermal load is the most significant cause of residual stress due to the extension and shrinkage of the molten pool. This review article explores the thermomechanical effect and stress existing in WAAM-fabricated parts due to the thermal cycles and other parameters in the process. It focuses on thermomechanical modeling and analysis of residual stress, which has interdependence with the thermal cycle, mechanical response, and residual stress in the process during printing. This review also explores some methods for measuring and minimizing the residual stress during and after the printing process. Residual stress and distortion associated with many input and process parameters that are in complement to thermal cycles in the process are discussed. This review study concludes that the thermal dependency of material characterization and process integration for WAAM to produce structurally sound and defect-free parts remain central issues for future research.

Density-Based Optimization of the Laser Powder Bed Fusion Process Based on a Modelling Framework

One of the main challenges encountered in the Laser-based Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) process is the fabrication of defect-free parts. The presence of defects severely degrades the mechanical performance of AM parts and especially their fatigue strength. The most popular and reliable method to assess the ability of the employed process parameters for the fabrication of full-density parts is the process windows map, also known as printability map. However, the experimental procedure for the design of the printability maps and the identification of the optimum-density process parameters is usually time-consuming and expensive. In the present work, a modelling framework is presented for the determination of a printability map and the optimization of the L-PBF process based on the prediction and characterization of melt-pool geometric features and the prediction of porosity of small samples of 316L SS and Ti-6Al-4V metal alloys. The results are compared with available experimental data and present a good correlation, verifying the modelling methodology. The suitability of the employed defect criteria for each material and the effect of the hatch-spacing process parameter on the optimum-density parameters are also presented.

Initiation and Mechanisms of Plasticity in Bimetallic Al-Cu Composite

We studied the shear deformation of a laminar Al-Cu composite with (100) and (110) interfaces with a shear perpendicular to the lamellae in comparison with pure single crystal Al and Cu at strain rates of 109 s−1 and 108 s−1 and different initial pressures in the range from −3 GPa to +50 GPa. The results of molecular dynamics (MD) for the plasticity initiation are generalized by means of an artificial neural network (ANN) trained by MD data for the (100) interface, and a rate sensitivity parameter identified using MD data for different strain rates. The ANN-based approach allows us to extrapolate MD data to much lower strain rates, which are more relevant for typical dynamic loadings. The considered problem is of interest as an example of the application of the developed ANN-based approach to bimetallic systems, whereas previously it was tested only for pure metals; in addition, Al-Cu composites are of practical interest for technology. The interface between metals reduces the shear strength of the composite in comparison with both pure metals. At an initial pressure below 10 GPa, the plasticity begins in the aluminum part of the composite, while at higher pressures, the plasticity of the copper part starts first. At a pressure above 40 GPa, a phase transition in the aluminum part governs the plasticity development. All this leads to a nonmonotonic pressure dependence of the critical shear stress of the Al-Cu composite in the case of (100) and (110) interfaces without misorientation. Misorientation decreases the critical stress of the nucleation of lattice dislocation and makes the pressure dependence of this stress monotonic. Deformation modes, with a defect-free copper part and a strain-accommodating aluminum part are observed in the MD and can be useful for technological applications related to deformable conducting materials.

The intelligent recoater: A new solution for in-situ monitoring of geometric and surface defects in powder bed fusion

• Powder bed homogeneity, contaminations, and printed surface quality are crucial in powder bedbased AM processes to obtain a defect-free part. • Standard process monitoring equipment does not provide a sufficiently high resolution for detecting small-scale defects. • The optical scanner mounted on the recoater can provide high-resolution images (1200 dpi) of the last printed slice and the powder bed for the whole build platform, without affecting process time. • In-situ surface topography deviations and powder bed contaminations can be observed and studied with this new monitoring setup. Powder bed homogeneity, contaminations, and printed surface quality are crucial in powder bed-based AM processes to obtain a defect-free part, but the scale at which these defects are seen is not compatible with the resolution of current industrial image-based monitoring solutions. In this work, we explore the implementation of an optical scanner in an industrial laser powder bed fusion (L-PBF) machine to detect powder bed and part-related defects. The sensor is mounted ”parasitically” on the recoater and exploits its movement to scan across the build platform before and after powder deposition to obtain high-resolution images. The acquisition seamlessly integrates with the process, without delaying the production as the acquisition occurs in parallel with the new layer deposition. The system was used to monitor test builds as well as longer builds (1000+ layers) to prove its robustness to the challenging L-PBF chamber environment. The in-situ powder bed images of the new monitoring system were compared to the acquisitions of a standard external camera setup. The improved image quality and resolution of the new system were demonstrated on both large-scale ( > 1 mm) and small-scale features. The new system proved to be capable of capturing printed surface topography anomalies and powder bed contaminations ( < 100 µm), opening a whole new range of possibilities for detecting small-scale defects via in-situ monitoring.

Metal Additive Manufacturing Process Design based on Physics Constrained Neural Networks and Multi-Objective Bayesian Optimization

The high-dimensionality of the process-structure-property (P-S-P) relationships for metal additive manufacturing (AM) is the major research challenge for process optimization to produce defect-free, structurally sound, and reliable AM parts. The goal of this work is to investigate the feasibility of a hybrid physics-based data-driven process design framework to establish reliable surrogates of process-structure relationships for metal AM process optimization. The process design framework includes a mesoscale multiphysics simulation model to predict microstructure evolution, a physics-constrained neural network to construct the surrogate of the process-structure relationship, and Bayesian optimization for process design. The proposed framework is demonstrated by optimizing the initial temperature and cooling rate for the single dendritic growth of Ti-6Al-4V alloy during rapid solidification in metal AM so that the desired dendritic area and microsegregation level can be achieved. The multi-objective optimization problem is solved with single-objective Bayesian optimization by aggregation. The effects of the aggregation weights on the optimization results are investigated with sensitivity analysis. The results show that the weights of individual objective functions determined from design preferences can help the designer to produce desired microstructure. The Pareto front and processing map for dendritic growth are constructed with the multi-objective Bayesian optimization method. The proposed process design framework is generic and can be potentially applied in materials design and digital twins of metal AM.

Residual Stress Distribution in Selective Laser Melting of SS316L Parts

Additive manufacturing is one of the fastest-growing fields in materials engineering. This is because there is a new trend for custom, high-precision, and on-demand manufacturing. The undesired residual stress induced in the components during layer-by-layer melting and solidification of the metal powder is an important issue related to the selective laser melting (SLM) process that needs to be studied deeply. These stresses may impair mechanical performance and potentially result in premature failure. As a result, a thorough knowledge of residual stress is crucial for improved component dependability. By keeping constant process parameters, samples were produced with a difference in the scanning method. Results indicate that the defect-free parts are manufactured in all the four patterns used, and the self-balanced residual stresses are within the safe limits of yield strength.

Prediction and Optimization of Process Parameters for Composite Thermoforming Using a Machine Learning Approach.

Thermoforming is a process where the laminated sheet is pre-heated to the desired forming temperature before being pressed and cooled between the molds to give the final formed part. Defects such as wrinkles, matrix-smear or ply-splitting could occur if the process is not optimized. Traditionally, for thermoforming of fiber-reinforced composites, engineers would either have to perform numerous physical trial and error experiments or to run a large number of high-fidelity simulations in order to determine satisfactory combinations of process parameters that would yield a defect-free part. Such methods are expensive in terms of equipment and raw material usage, mold fabrication cost and man-hours. In the last decade, there has been an ongoing trend of applying machine learning methods to engineering problems, but none for woven composite thermoforming. In this paper, two applications of artificial neural networks (ANN) are presented. The first is the use of ANN to analyze full-field contour results from simulation so as to predict the process parameters resulting in the quality of the formed product. Results show that the developed ANN can predict some input parameters reasonably well from just inspecting the images of the thermoformed laminate. The second application is to optimize the process parameters that would result in a quality part through the objectives of minimizing the maximum slip-path length and maximizing the regions of the laminate with a predesignated shear angle range. Our results show that the ANN can provide reasonable optimization of the process parameters to yield improved product quality. Overall, the results from the ANNs are encouraging when compared against experimental data. The image analysis method proposed here for machine learning is novel for composite manufacturing as it can potentially be combined with machine vision in the actual manufacturing operation to provide active feedback to ensure quality products.

Computational design of a crack-free aluminum alloy for additive manufacturing

The design of new alloys adapted to LPBF and combining suitable mechanical strength together with a low cracking susceptibility is a promising way to produce defect-free parts made of aluminum. The current work proposes a design procedure relying on the decrease of the brittle temperature range to mitigate the hot cracking issue and additional optimization criteria concerning the phases fractions and the solid solution strengthening to preserve the mechanical strength and some ductility. The optimization functions are included in an aggregated genetic algorithm for a rapid and efficient identification of the optimal composition. The algorithm determined a promising alloy, with a brittle temperature range of only 9 °C, and mostly constituted of FCC phase (around 90%), with some Mg 2 Si and Al 9 FeNi precipitates. The alloy is found suitable for the LPBF process with no cracks observed. Hence, the criterion for the mitigation of hot cracking is validated. Mechanical results shown a high yield stress and ultimate trensile stress but an elongation at break quite low compared to conventional manufactured aluminum alloys.

Digital Twins in deep drawing for virtual tool commissioning and inline parameter optimization

Fluctuating boundary conditions and the highly nonlinear process behavior of deep drawing operations make experience-based selection of suitable control parameters difficult. Nowadays, commissioning deep drawing tools, i.e., die spotting and identification of suitable control parameters for defect-free parts, is conducted on real world try-out presses. Transferring the tools to production machines entails adaption of these initial parameters. Once production is ramped up, any changes to material properties, lubrication and press behavior require continuous manual machine parameter tuning. Virtual tool commissioning and utilizing these simulation models in the production phase to adapt control parameters would reduce time and cost over the entire life cycle of the machine and the tool sets. The virtual representative, which provides services for a plant over several life cycle phases, is also referred to as Digital Twin. In this paper, the authors present a Digital Twin concept for deep drawing presses to predict the state of the system and optimize the control parameters during the production. The integration of all involved subsystems into one system simulation and its efficient calculation is the biggest challenge. The authors combine a virtual commissioning simulation tool with a finite element model to implement all relevant properties of the deep drawing press and the interaction of its subsystems. It is shown, how the idea of a system simulation makes predictions of system parameters specific to a production situation possible, and therefore, can help to select suitable control parameters that leads to a reduction of the error rate in deep drawing.

Hybrid microstructure-defect printability map in laser powder bed fusion additive manufacturing

This work presents a methodology for predicting a hybrid microstructure and defect processing map for laser powder bed fusion (LPBF) additive manufacturing (AM). A finite element (FE) analysis based thermal model is coupled with a phase field model (PFM) in order to predict both defect formation and microstructure across the LPBF process parameter space. The parameter space is first categorized into defect regions such as keyholing, balling, and lack of fusion. These regions are predicted using the FE thermal model, allowing a defect map to be generated based on the geometries of the melt pools for a defined powder layer thickness. A region of the parameter space that leads to successful AM of defect-free parts is then identified. Segregation across the parameter space is predicted by an integrated computational model, which couples a finite interface dissipation PFM and the aforementioned FE thermal model. The parameter space is then separated into regions that result in cellular or planar microstructures based on the area fraction of cellular-dendritic segregation predicted within the melt pools. By merging the microstructure and defect maps, a hybrid microstructure-defect printability map is generated that distinguishes a process parameter region for fabricating defect-free parts with homogeneous microstructures. This hybrid map is validated with experimental observations for a Ni-5wt.%Nb alloy. The hybrid printability map can be used in the rapid selection and optimization of process parameters for additively manufacturing any alloy (provided models and their parameterization are available, and the alloy is printable), and can potentially help design alloys best suited for AM.

Investigation on the Cause-Effect Relationships between the Process Parameters and the Resulting Geometric Properties for Wire-Based Coaxial Laser Metal Deposition

Coaxial Laser Metal Deposition with wire (LMD-w) is a valuable complement to the already established Additive Manufacturing processes in production because it allows a direction-independent process with high deposition rates and high deposition accuracy. However, there is a lack of knowledge regarding the adjustment of the process parameters during process development to build defect-free parts. Therefore, in this work, a process development for coaxial LMD-w was conducted using an aluminum wire AlMg4,5MnZr and a stainless steel wire AISI 316L. At first, the boundaries for parameter combinations that led to a defect-free process were identified. The proportion between the process parameters energy per unit length and speed ratio proved crucial for a defect-free process. Then, the influence of the process parameters on the height and width of single beads for both materials was analyzed using a regression analysis. It was shown that linear models are suitable for describing the correlation between the process parameters and the dimensions of the beads. Lastly, a material-independent formula is presented to calculate the height increment per layer needed for an additive process. For future studies, the results of this work will be an aid for process development with different materials.

Defect detection of concrete in infrastructure based on Rayleigh wave propagation generated by laser-induced plasma shock waves

Defects in large concrete structures, including critical infrastructure such as bridges and tunnels, are traditionally detected by hammering tests by inspectors. However, the results depend on the inspectors’ skills. Additionally, hammering tests are impractical to thoroughly inspect massive structures in a short time. Herein defect detection in concrete structures is realized by telemetry using shock waves generated by laser-induced plasma (LIP). LIP is generated by irradiating a Nd:YAG pulsed laser near the concrete surface. As LIP spreads to the surrounding area at an ultrafast speed, a spherical shock wave is generated. This shock wave realizes a non-contact, non-destructive impulse excitation force on a concrete structure. The response of the concrete structure is measured with a laser Doppler vibrometer (LDV). Applying this method to detect defects in an artificial concrete specimen can identify the natural frequencies and modes of the defects. By measuring the propagation of the Rayleigh waves generated by the LIP shock waves, defects and the approximate location of the boundary between the defective part and the defect-free part are detected.