Metal Parts Research Articles

Load magnitude and location estimation on additively manufactured circular structures using deep learning

Additively Manufactured (AM) metal parts have been used at critical engineering applications. Estimation of load and its location is important to avoid accidents. Since the excitation signals move through multiple paths, estimation of load and its location is difficult. In this study, surface response to excitation method (SuRE) was implemented with multiple steps by considering the challenges of the geometry. Two 3D printed stainless steel rocket nozzle type structures with two different sizes and a C shaped tube section were used. Two different PZT placements were used for the big nozzle while the PZTs were located at the opposite sides of the small nozzle. The large nozzle had 280 test cases for 9 categories, while the smaller nozzle had 60 test cases for 5 categories. Finally, the C shaped specimen had 48 test cases for three categories. Continuous wavelet transform (CWT) was used to obtain more representative presentation of the data to the deep learning algorithm. Convolutional Neural Networks (CNNs) were used for classification. Wavelet transformation – Deep learning combination yielded 100 % accuracy for the large nozzle and 97.14 % for the smaller nozzle. These findings demonstrate the effectiveness of this technique on curved surfaces representative of real-world parts. The proposed method estimated the load location with 100 % accuracy.

Influence of Laser Welding Modes along a Curved Path on the Mechanical Properties and Heterogeneity of the Microstructure of 316L Steel Plates.

The results of experimental studies in the manufacture of components of the supporting structure of the first wall panel, carried out as part of the manufacture of a model of the International Thermonuclear Experimental Reactor (ITER) using laser welding technology, are presented. The influence of laser welding modes on the quality of formation, microstructure characteristics, and mechanical properties of a welded joint made of 10 mm thick 316L steel was studied. A coaxial nozzle was designed and manufactured to protect the weld pool with a curved trajectory. The mechanical properties of the welded joint are 98-100% that of the base metal, and the microhardness of the welded joint and base metal is in the range of 180-230 HV. It was established that the lower part of the weld metal on the fusion line has transcrystalline grains and differs in δ-ferrite content; due to a high welding speed, the ratio of the depth to the width of the welding seam is 14 times. The width of the rectilinear part of the seam is 15-20% larger than its curved part.

Hybrid segmented manufacturing of metal part by integrating wire arc additive manufacturing and machining

Hybrid segmented manufacturing of metal part by integrating wire arc additive manufacturing and machining

Investigating the relationship between shift work schedule and blood and metabolic parameters: a 10-years retrospective cohort study

Shift work has become increasingly common in modern society. Shift work has been associated with a range of negative health outcomes. Therefore, this 10-years retrospective cohort study, aimed to investigate the relationship between shift work and blood and metabolic parameters. This retrospective cohort study was conducted in a metal parts manufacturing industry in 2023. In this study, 204 shift workers and 204 day workers were examined. All the studied blood and metabolic parameters were collected by reviewing the medical records of all participants during a 10-years period (2013–2022). Moreover, the amounts of physical, chemical, and ergonomics harmful agents in the work environment were investigated. All the collected data were analyzed using SPSS version 25.0. The values of Body Mass Index (BMI), Red Blood Cell Count (RBC), Platelets Count (PLT), Thyroid-Stimulating Hormone Level (TSH), Fasting Blood Sugar Level (FBS), Creatinine, Triglyceride (TG), Liver Enzymes level (SGOT and SGPT), and Systolic Blood Pressure (SBP) were higher among the shift work employees, and a significant difference was observed between the values of these parameters between the two groups. The results of logistic regression showed that the highest effect of shift work was observed on the parameters of FBS, TG, SGPT, TSH, Physical activity, BMI, Sleep duration, PLT, and Sleep quality with beta coefficient values of 0.49, 0.33, 0.29, 0.29, 0.20, 0.18, 0.14, 0.13 and, 0.11, respectively (p-value < 0.01). The present study contributes to a growing body of evidence that blood and metabolic factors are likely to be influenced by shift work. These findings have important implications for policy makers, highlighting the need for interventions to mitigate the negative health effects of shift work on workers.

In-situ composition analysis during laser powder bed fusion of Nd-Fe-based feedstock using machine-integrated optical emission spectroscopy

Powder bed fusion of metals using a laser beam (PBF-LB/M) is a widely used additive manufacturing (AM) technique that enables the material-efficient fabrication of complex geometries in metallic parts. However, achieving high-quality parts with desired properties heavily depends on variances in the manufacturing process and powder composition, making quality control an indispensable aspect of almost all applications. Today, mainly grayscale imaging, photodiodes, or pyrometry are employed, whereas in-situ recording of chemical (compositional) information has rarely been done during PBF-LB/M. This pilot study uses Nd-Fe-B as a compositional sensitive model material to explore the feasibility of in-situ optical emission spectroscopy (OES) for elemental analysis of metallic samples during PBF-LB/M, validated by ex-situ analysis. Our results show that the Boltzmann-corrected local emissivity of the Fe and Nd lines in the plume is a reliable indicator for determining the temperature and elemental material concentration voxel-wise, which could enable a digital 3-D reconstruction of the material composition of a printed part in the future.

The Perry project: A versatile low-cost syringe pump for dispensing in automated synthesis systems

Syringe pumps are used in applications such as chemistry, medicine, or microbiology offering high-precision dosing or a way to ease the workload of tedious tasks. Further, high-performance syringe pumps are crucial to automating laboratory tasks. The Perry Pump is demonstrated with syringe sizes ranging from 1–20 mL and includes a reservoir, which enables larger volumes to be used. The lowest volume demonstrated is 20 μL at 5.5 μL/s, while the largest is 20 mL at 0.145 mL/s. The Perry Pump is designed with the intention of easy to 3D-print, limited metal parts, and a high versatility and tolerance to chemicals.

Anisotropy of Additively Manufactured Metallic Materials.

Additive manufacturing (AM) is a technology that builds parts layer by layer. Over the past decade, metal additive manufacturing (AM) technology has developed rapidly to form a complete industry chain. AM metal parts are employed in a multitude of industries, including biomedical, aerospace, automotive, marine, and offshore. The design of components can be improved to a greater extent than is possible with existing manufacturing processes, which can result in a significant enhancement of performance. Studies on the anisotropy of additively manufactured metallic materials have been reported, and they describe the advantages and disadvantages of preparing different metallic materials using additive manufacturing processes; however, there are few in-depth and comprehensive studies that summarize the microstructural and mechanical properties of different types of additively manufactured metallic materials in the same article. This paper begins by outlining the intricate relationship between the additive manufacturing process, microstructure, and metal properties. It then explains the fundamental principles of powder bed fusion (PBF) and directed energy deposition (DED). It goes on to describe the molten pool and heat-affected zone in the additive manufacturing process and analyzes their effects on the microstructure of the formed parts. Subsequently, the mechanical properties and typical microstructures of additively manufactured titanium alloys, stainless steel, magnesium-aluminum alloys, and high-temperature alloys, along with their anisotropy, are summarized and presented. The summary indicates that the factors leading to the anisotropy of the mechanical properties of metallic AM parts are either their unique microstructural features or manufacturing defects. This anisotropy can be improved by post-heat treatment. Finally, the most recent research on the subject of metal AM anisotropy is presented.

Non-Planar Helical Path Generation Method for Laser Metal Deposition of Overhanging Thin-Walled Structures

Laser metal deposition is a branch of additive manufacturing that offers advantages over traditional manufacturing techniques for forming overhanging thin-walled metal parts. Previously, helical paths that were suitable for manufacturing such parts were not only limited to stacking material on a flat surface but were also fixed to the model boundaries. In order to solve these two problems to meet more complex process requirements, a non-planar helical path generation method is proposed for laser metal deposition. The method is based on the characteristics of the additive manufacturing process planning flow, which first slices the model using curved surfaces, then offsets the contours on the sliced layering, and finally generates continuous helical paths according to the contracted or expanded contours. In order to verify the feasibility of the method, hollow blades are formed on cylindrical surfaces following the planned paths. The results show that the proposed method is not only capable of assisting the laser metal deposition process to fabricate thin-walled structures on non-planar surfaces but also capable of freely adjusting the contour dimension.

An experimental investigation of process parameters on material removal and surface roughness improvement in abrasive flow machining

There is a huge demand for mechanical components with long life cycles and superior surface finishes as a result of the Industrial Revolution. Several methods for nano-finishing complex structures, including abrasive flow machining (AFM), have been developed in response to the need for superior surface finish. In AFM, abrasion only happens in locations where flow is restricted. This property makes AFM useful in polishing the interior, inaccessible cavities and recesses of the metallic parts using a semi-liquid paste. This paper focuses on investigating the impact of process parameters on material removal, and percentage improvement in surface roughness on cylindrical brass workpieces using Taguchi L9 orthogonal array. The number of cycles, extrusion pressure, and grit size of abrasives have been selected as process parameters. The abrasive medium employed in this investigation is composed of a blend of polymer, hydrocarbon gel, and aluminium oxide abrasive particles with varying grit sizes. The minimum value of roughness after AFM has been achieved 4.25 microns, and the maximum %ΔRa has been achieved 28%. The number of cycles that have the largest percentage contribution to material removal was 83.74%. Whereas, the percentage contributions of the abrasive particle grit size and extrusion pressure are 2.03%, and 14.16%, respectively. N2P2G3 is the optimal level of process parameters used for material removal. Similarly, the number of cycles has the largest percentage contribution of 83.48% in surface roughness. Whereas extrusion pressure and grit size contribute 7.38%, and 8.88% in surface roughness. The optimal level of process parameters that have been attained in the case of surface roughness is N3P2G3.

Functional behaviour of novel multi-layered deposition via twin wire arc additive manufacturing: Microstructure evolution and wear tailoring characteristics

Twin wire arc additive manufacturing offers advantages such as high deposition rates and the ability to use a wide variety of materials, making it suitable for large-scale metal part production with enhanced efficiency and flexibility. Functionally graded deposition (FGD) based structure of dissimilar steels (SS316LSi and ER70S-6) fabricated via twin wire arc additive manufacturing and their detailed performance analysis are accomplished in the current research work. Microstructural, mechanical, and tribological characteristics of the deposited structure were investigated and analysed. Microstructure reveals polygonal ferrite, widmanstatten ferrite, skeletal ferrite, lathy ferrite, ferrite solidification, and ferrite to austenite (F/A) transformation. FGD structure possessed a microhardness of 371 HV. Average ultimate tensile strength, yield strength, and elongation percentage were 675.98 MPa, 414.21 MPa, and 38.37 %, respectively. In wear analysis, the average coefficient of friction value of WAAM-processed FGD samples is 0.44–0.49. 3D profilometer analysis reveals that average values of roughness, maximum height of roughness, and maximum roughness valley depth are significant with increasing applied loads. The findings of the present research focus on the quality and appropriateness of dissimilar steels for structural applications and provide a novel method for creating high-strength components.

Compound criticality of bauxite production: implications for sustainability and trade neo-colonialism

We describe bauxite as having compound criticality. (1) It is a critical raw material (CRM) from which alumina and thereby aluminium are produced, and aluminium has the highest production levels of all metals deemed significant to the Clean Energy Transition. (2) Gallium is a by-product CRM, extracted during alumina refining. (3) Multiple other CRM by-products could be extracted during alumina refining: scandium, lithium, cobalt, titanium and REE. (4) Production is profoundly influenced by price inelasticity (for by-products), regime instability of bauxite production and supply chain bottlenecks. We review the geology of bauxite ore deposits as a function of complex CRM supply potential, and interrogate the trait of criticality using four of the world's dominant producer nations of bauxite and potential patterns of value addition from raw materials to refined products. Trade agreements and mitigation strategies to ensure security of supply in consumer regions prioritize removal of low-cost bauxite by large-scale mining operations and shipping of unprocessed ore at high CO 2 footprint from producer nations. Ore-refining nations have investments that are deemed extractive because they remove large quantities of raw material for export with minimal or no in-country processing and they can also place producer nations in debt. Opposition to neo-colonial debt-trap policies and extractive practices increases regime instability and thereby criticality. We highlight how societal-environmental-political interactions and the mineral-energy nexus in the bauxite-aluminium supply chain are subject to increasing turbulence, which may reshape the geographies of supply chains.

TTFDNet: Precise Depth Estimation from Single-Frame Fringe Patterns.

This work presents TTFDNet, a transformer-based and transfer learning network for end-to-end depth estimation from single-frame fringe patterns in fringe projection profilometry. TTFDNet features a precise contour and coarse depth (PCCD) pre-processor, a global multi-dimensional fusion (GMDF) module and a progressive depth extractor (PDE). It utilizes transfer learning through fringe structure consistency evaluation (FSCE) to leverage the transformer's benefits even on a small dataset. Tested on 208 scenes, the model achieved a mean absolute error (MAE) of 0.00372 mm, outperforming Unet (0.03458 mm) models, PDE (0.01063 mm) and PCTNet (0.00518 mm). It demonstrated precise measurement capabilities with deviations of ~90 μm for a 25.4 mm radius ball and ~6 μm for a 20 mm thick metal part. Additionally, TTFDNet showed excellent generalization and robustness in dynamic reconstruction and varied imaging conditions, making it appropriate for practical applications in manufacturing, automation and computer vision.

Investigation of Metal Powder Blending for PBF-LB/M Using Particle Tracing with Ti-6Al-4V

Laser-based powder bed fusion of metals (PBF-LB/M) is the most used additive manufacturing (AM) technology for metal parts. Nevertheless, challenges persist in effectively managing metal powder, particularly in blending methodologies in the choice of blenders as well as in the verification of blend results. In this study, a bespoke laboratory-scale AM blender is developed, tailored to address these challenges, prioritizing low-impact blending to mitigate powder degradation. As a blending type, a V-shape tumbling geometry meeting the requirements for laboratory AM usage is chosen based on a literature assessment. The implementation of thermal oxidation as a powder marking technique enables particle tracing. Blending validation is achieved using light microscopy for area measurement based on binary image processing. The powder size and shape remain unaffected after marking and blending. Only a small narrowing of the particle size distribution is detected after 180 min of blending. The V-shape tumbling blender efficiently yields a completely random state in under 10 min for rotational speeds of 20, 40, and 60 rounds per minute. In conclusion, this research underscores the critical role of blender selection in AM and advocates for continued exploration to refine powder blending practices, with the aim of advancing the capabilities and competitiveness of AM technologies.

Prediction of material toughness using ensemble learning and data augmentation

ABSTRACT The present work investigates the impact resistance of metallic parts produced using Laser Powder Bed Fusion and the possibility of its prediction using machine learning algorithms. The challenge lies in finding optimal process parameters before printing based on the existing data. Economic constraints often result in the availability of only a limited amount of data for predictive purposes. In this work, around one hundred data points from Charpy impact tests on AlSi10Mg0.5 were used to analyse the correlation between the impact resistance and process parameters, including information about sample porosity. The present research implements a data augmentation technique that artificially increases the volume of training data by applying domain-specific transformations to the original limited dataset. Using this technique, the dataset had been extended to over one thousand data points. To identify the most suitable approach for the specific issue at hand, several algorithms were explored: Regression Neural Network, K-Nearest Neighbours, Decision Tree, Random Forest, AdaBoost, Gradient Boosting, XGBoost, as well as ensemble combinations of Random Forest with AdaBoost, Gradient Boosting, and XGBoost algorithms. The results suggest that the Random Forest and the boosting algorithms generalise best given the sparse testing data. The best-performing models yield a prediction fitness reaching 86 percent. Therefore, an effective model for predicting the impact resistance had been developed and can be used to optimise the quality of additively manufactured parts.

Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision

In-process measurement of machining precision is of great importance to advanced manufacturing, which is an essential technology to realize compensation machining. In terms of cost-effectiveness and repeatability of computer vision, it has become a trend to replace traditional manual measurement with computer vision measurement. In this paper, an in-process measurement method is proposed to improve precision and reduce the costs of machining precision. Firstly, a universal features model framework of machining parts is established to analyze the CAD model and give standard information on the machining features. Secondly, a window generator is proposed to adaptively crop the image of the machining part according to the size of features. Then, the automatic detection of the edges of machining features is performed based on regions of interest (ROIs) from the cropped image. Finally, the measurement of machining precision is realized through a Hough transform on the detected edges. To verify the effectiveness of the proposed method, a series of in-process measurement experiments were carried out on machined parts with various features and sheet metal parts, such as dimensional accuracy measurement tests, straightness measurement tests, and roundness measurement tests under the same part conditions. The best measurement accuracy of this method for dimensional accuracy, straightness, and roundness were 99%, 97%, and 96%, respectively. In comparison, precision measurement experiments were conducted under the same conditions using the Canny edge detection algorithm, the sub-pixel edge detection algorithm, and the Otsu–Canny edge detection algorithm. Experimental results show that the feature-model-based in-process measurement of machining precision using computer vision demonstrates superiority and effectiveness among various measurement methods.

Manipulation of a Complex Object Using Dual-Arm Robot with Mask R-CNN and Grasping Strategy

Hot forging is one of the common manufacturing processes for producing brass workpieces. However forging produces flash which is a thin metal part around the desired part formed with an excessive material. Using robots with vision system to manipulate this workpiece has encountered several challenging issues, e.g. the uncertain shape of flash, color, reflection of brass surface, different lighting condition, and the uncertainty surrounding the position and orientation of the workpiece. In this research, Mask region-based convolutional neural network together with image processing is used to resolve these issues. The depth camera can provide images for visual detection. Machine learning Mask region-based convolutional neural network model was trained with color images and the position of the object is determined by the depth image. A dual arm 7 degree of freedom collaborative robot with proposed grasping strategy is used to grasp the workpiece that can be in inappropriate position and pose. Eventually, experiments were conducted to assess the visual detection process and the grasp planning of the robot.

Nugget and corona bond size measurement through active thermography and transfer learning model

Resistance spot welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as both nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). Starting from the thermal signal phase gradient and amplitude gradient maps, a convolutional neural network (CNN) estimates the size of nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study suggests adopting a new methodology for automatic RSW quality control based on thermal signal analysis.

Pressure-less melt infiltration characteristics of Mg melt into Ti powder preforms with various pore structures: Toward the application to the binder jetting additive manufacturing

Binder jetting (BJT) additive manufacturing enables the fabrication of complex-shaped metallic parts but requires sintering as post-processing to obtain dense parts. The sintering inevitably causes the shrinkage from green body to dense products, resulting in low dimensional accuracy. In contrast, pressure-less melt infiltration (PMI) fills pores in the green body with a liquid metal driven by capillary pressure and fabricates a dense metal matrix composite without severe shrinkage. To consider the applicability of pressure-less melt infiltration to the post-processing of binder jetting additive manufacturing, it is required to clarify the relationship between pressure-less melt infiltration process conditions and the relative density of the composites. In this study, the liquid Mg/solid Ti system was selected because of good wettability and absence of Mg-Ti intermetallic, and the conditions of Ti powder preforms (corresponding to the green body) were focused on. Ti powder preforms with various powder sizes (2300, 410, 140, and 20 μm) and different shapes (irregular and spherical) were cold-pressed at 76, 102, 127, 153, and 178 MPa to fabricate preforms with various relative densities, pore sizes, and pore morphologies to response the similar characteristics in BJT preforms. Mg melt infiltrated into the preforms under constant conditions to fabricate Mg/Ti composites. The structures of the preforms and composites were characterized by sample size measurement, optical and scanning electron microscopy, and X-ray computed tomography. It was revealed that the relative density of the composite increased with decreasing Ti powder size and using spherical Ti powder, whereas the compact pressure exhibited a slight effect. Furthermore, defect volume increased with increasing infiltration height but decreased around the top surface of the sample. Minute observations around defects in the composites revealed that defects were formed at larger pores, and Mg melt was infiltrated into pores above the defects. These results indicated that infiltration velocity varied depending on the flow channel structure, and gas was trapped at larger pores (far from the outside of the sample) where infiltration velocity was low. Using smaller Ti powder decreased the variation in pore size in the preform, resulting in improving the relative density of the composites by suppressing the gas trapping. This study provides a new insight into the pressure-less melt infiltration process as a post-processing of binder jetting additive manufacturing.

Nanotechnology-Enabled Rapid Investment Casting of Aluminum Alloy 7075

Abstract Rapid investment casting with additively produced molds can offer excellent surface finishes, tight dimensional tolerances, and complex geometries for high-performance metal parts in a rapid fashion. However, there is a long-standing challenge in the investment casting of high-strength aluminum alloy (AA) 7075 due to its hot cracking susceptibility and severe solidification shrinkage. Here, we show the unprecedented rapid investment casting of AA7075 by applying nano-treating technology, whereby a low-volume fraction of nanoparticles is dispersed into metal to modify its solidification behavior and microstructure. TiC nanoparticles were able to effectively modify both primary and secondary phases while suppressing the hot cracking susceptibility of AA7075 during solidification. Despite the low cooling rate, nano-treated AA7075 exhibits fine equiaxed grains with an average size of 47.1 μm, in contrast to the large dendritic grains measuring 714.8 μm in pure AA7075. Nano-treated AA7075 parts produced by rapid investment casting exhibited exceptional tensile strength and ductility in both as-cast and heat-treated conditions. This study highlights the potential of investment casting high-performance alloys which were traditionally considered impossible to fabricate by this method.

3D measurement of precise part in complex circumstance using line structured light and improved U-Net

Abstract Line structured light scanning is extensively utilized for the 3D measurement of precise metal parts, but the curvature surfaces and specific materials of these parts generate specular reflection, making it challenging to accurately extract the center of the laser stripe in complex circumstances. Therefore, the primary challenges are the noise separation in the laser stripe image and the extraction of the laser stripe center under complex circumstances. To solve the above problems, an improved U-Net semantic segmentation algorithm is proposed by adding an attention mechanism and modifying skip connections to the classical U-Net network structure for accurate laser stripe segmentation. Secondly, the dual smoothing method of mean smoothing and Savitzky–Golay smoothing is combined with the Hessian matrix algorithm to complete the subpixel extraction of the center point of the laser stripe. Finally, taking the blade and shaft part as the measurement objects, the experimental results demonstrate that the method can obtain more complete, smoother, and denser results than the traditional method under highly reflective surfaces, vital interference spots, and strong ambient light. The proposed method is used for the 3D measurement of the shaft parts, and the diameter measurement maximum error is 0.029 mm, verifying the feasibility of the proposed method.