Manufacturing Defects Research Articles

A New Method for Compression Testing of Reinforced Polymers

Compressive testing of specimens taken from relatively thin composite plates is difficult, especially due to the occurrence of buckling. To prevent buckling, the central portion of the specimens used for the compression test has smaller dimensions, and the specimens can be guided along their entire length. For these reasons, optical methods, such as digital image correlation (DIC), cannot be used for the compression test and strain rosettes cannot be glued onto the samples to determine Poisson’s ratio. In this study, compression tests of a glass fiber-reinforced polymer (GFRP) were conducted using both the ASTM D695 (Boeing version) and a newly proposed method. The new method involves using special specimens that allow T-type rosettes to be bonded to determine Poisson’s ratio, whose value of 0.14 was thus determined. SEM images of the failure surfaces were presented and interpreted. A finite element analysis (FEA) of the specimens tested in compression is also presented. The first analyzed case considers the homogeneous and orthotropic composite, loaded with a uniformly distributed force. The normal stress in the central section of the specimen, determined with FEA, has an error of 6.52% compared to that determined experimentally. Additionally, the strain in the center of the strain gauge, determined with FEA, has an error of 4.76% compared to the measured one. In the second case studied with FEA, the sample is loaded with a quasi-concentrated force, which can move in the direction of the symmetry axes of the cross-section, to study the effect of the eccentricity of the compression force on the state of stress. It was shown that the eccentricity of the force has a great influence: the stress distribution in the section of the specimen becomes strongly non-uniform. For a force eccentricity of 0.4 mm in the direction of the OX axis, the minimum stress decreases by 53.7%, and the maximum stress increases by 55.4%. In order to analyze the influence of some manufacturing defects, two other cases were analyzed by FEA, in which it was assumed that the thicknesses of the outer resin layers were modified, making them asymmetrical. For this final FEA, the specimen was considered to be composed of laminates. These results demonstrate the special attention that must be paid to the centric application of force in compression testing.

An integrated approach of zero defect manufacturing and process mining to avoid defect occurrence in production and improve sustainability

Purpose This paper aims to integrate zero defect manufacturing (ZDM) with process mining (PM) to avoid defect occurrence during production and improve sustainability. Design/methodology/approach The method is developed based on literature review in ZDM and PM. It uses PM for process discovery as an initial strategy in priority to predict-prevent strategies of ZDM. Findings It presents the applicability of the proposed approach in observing manufacturing process behavior, identifying dynamic causes of defects during production, predicting the time of defect occurrence and preventing defective products. It also identifies, explains and measures criteria for environmental, social and economic pillars of sustainability affected by defects and presents the impacts of the proposed approach on sustainability improvement. Originality/value The extended view of this research, as well as its analytical approach, helps practitioners to develop their ZDM and PM approaches more holistically. The practical application of this research is illustrated through implementing it in a real-life manufacturing case, where the outcomes prove its applicability in avoiding defect occurrence and improving all three pillars of sustainability.

Fatigue Crack Segmentation and Characterization of Additively Manufactured Ti‐6Al‐4V Using X‐Ray Computed Tomography

ABSTRACTX‐ray computed tomography (XCT) is extremely useful for the non‐destructive analysis of additively manufactured (AM) components. AM components often show manufacturing defects such as lack‐of‐fusion (LoF), which are detrimental to the fatigue life of components. To better understand how cracks initiate and propagate from internal defects, we fabricated Ti‐6Al‐4V samples with an internal cavity using electron beam powder bed fusion. The samples were tested in high‐cycle and very high‐cycle fatigue regimes. XCT was used to locate crack initiation sites and to determine characteristic properties of cracks and defects with the aid of deep learning segmentation. LoF defects exposed to the outer surface of the samples after machining were found to be as detrimental to fatigue life as the internal artificial defects. This work can benefit industries that utilize the AM of high‐strength, lightweight alloys, in the design and manufacturing of components to improve part reliability and fatigue life.

Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10–15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite’s overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25–30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components.

Analysis of wrinkles and corrugations in the electrode calendering process

ABSTRACT Calendering is a crucial process in manufacturing of lithium-ion batteries electrodes. Wrinkles and corrugations are the main manufacturing defects in the calendering process. This study aims at studying the formation mechanism of wrinkles and corrugations. Corrugations and wrinkles of the electrodes were revealed to be more dependent on the difference between the front and back tensions. The tortuosity of corrugations was reduced by 34.2% when the tension difference decreased from 20 to 5 N. The increase in the tension difference led to a linear increase of the shear displacement δ, causing severe wrinkles in the uncoated area of the electrodes. The analytical prediction model for wrinkles during calendering was established based on the shearing of a rectangular membrane. The optimal web tension conditions, at a tension difference of 17 N, was achieved to obtain the optimal calendered electrodes.

Multi-scale modeling: Finite element analysis of the thermophysical properties of carbon/carbon composites considering manufacturing defects and porosity at high temperature

Carbon/carbon (C/C) composites have strict requirements for their thermal properties in extreme environments, especially at high temperature, where their properties are crucial for long-term life. The structure of C/C composites is exceptionally complex and exhibits multi-scale characteristics, and their thermophysical properties are closely related to temperature. Therefore, exploring their thermal response and heat transfer mechanisms through experimental method is relatively costly. This paper constructs a multi-scale finite element model to investigate the influence of structure and manufacturing defects on performance, and analyzes the thermophysical properties of the composites at High-temperature. By constructing a micro-representative volume element (Micro-RVE) that includes fibers, matrix, and pores, and using a steady-state heat transfer analysis method, the influence of material phase distribution on performance is studied. At the same time, a Meso-RVE reflecting the layering form, needle punching effect and manufacturing defects is established, and the transient heat transfer analysis method is used to investigate the influence of the structural form on performance. Finite element analysis shows that, the simulation values of the three C/C composites of unidirectional fiber bundles, short-chopped fiber felts, and needle punched C/C composites show good consistency with the experimental results in the published literature. This paper uses numerical methods to calculate the thermophysical properties of C/C composites from room temperature to 900°C and explores their variation with temperature. The constructed multi-scale model provides an accurate and effective method for predicting the thermophysical properties of C/C composites and also provides new perspectives and insights for thermal-mechanical coupling analysis and structural design.

Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller. II. Simulation for the full-scale propeller with defects

Paper II of this two-part paper investigated the effects of leading-edge (LE) manufacturing defects on the open-water cavitation performance of a full-scale propeller based on the geometry of David Taylor Model Basin propeller by using the three-dimensional (3D) steady Reynolds-Averaged Navier–Stokes solver. Various simulation parameters, including domain size, grid size, stretch ratio, first-grid spacing, y+, and turbulence model, were carefully examined for their effects on the solutions, leading to the development of the best modeling practices for the full-scale propeller with LE defects. Employing these recommended best-practice settings, simulations were conducted on the full-scale propellers with 0.10, 0.25, and 0.50 mm LE defects. Compared to the predictions from Paper I [Jin et al., “Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller—Paper I: Simulation for the model- and full-scale propellers without defect,” Phys. Fluids 36, 105179 (2024).], which did not account for LE defects, the results showed that the LE defects within International Standards Organization (ISO) 484 Class S tolerances narrow the cavitation buckets. As a consequence, such LE defects can result in more than 40% reduction in cavitation inception speed, which is similar to the conclusions drawn from earlier two-dimensional (2D) studies [Jin et al., “2D CFD studies on effects of leading-edge propeller manufacturing defects on cavitation performance,” in SNAME Maritime Convention (The Society of Naval Architects and Marine Engineers, 2020).]. Note that Paper I [Jin et al., “Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller—Paper I: Simulation for the model- and full-scale propellers without defect,” Phys. Fluids 36, 105179 (2024).] presents the simulations for the model- and full-scale propellers without LE defects.

Internal damage evolution of C/SiC composites in air at 1650 °C studied by in-situ synchrotron X-ray imaging

Carbon fibre reinforced silicon carbide (C/SiC) ceramic matrix composites have attracted considerable attention due to their exceptional properties and extensive potential applications as high-temperature structural materials. However, due to their complex structure and manufacturing defects, C/SiC composites exhibit intricate mechanical behaviour under thermal-mechanical-oxidative coupling environments. To date, systematic studies on the internal damage evolution and failure mechanisms of C/SiC composites under high-temperature oxidative environments are lacking. In this study, a combination of synchrotron X-ray micro-computed tomography (SR-μCT) and in-situ experiments under thermal-mechanical-oxidative coupling environments at room temperature and 1650 °C in air was used to characterize the internal microstructures and damage evolution processes of C/SiC composites at different loading levels. Additionally, the 3D strain fields during in-situ loading were quantitatively analysed using the Digital Volume Correlation (DVC) method. The findings underscore the substantial impact of oxidative damage on the mechanical response of C/SiC composites, particularly concerning tensile properties and fracture modes. At room temperature, severe delamination, fibre bundle pull-out and interfacial debonding occurred internally. Whereas, under high-temperature atmospheric conditions, severe fibre oxidation reactions occurred at the specimen edges, resulting in rapid porosity escalation. Crack initiation from surface defects followed by rapid inward propagation is observed. Moreover, while the strain distribution remains relatively uniform until fracture, a pronounced concentration of strain is evident near the fracture zones at room temperature, with an even greater concentration observed at 1650 °C. Notably, the region of concentrated strain within the 3D deformation field corresponds closely to the final fracture location, as revealed by quantitative DVC analysis.

Numerical study on effects of leading-edge manufacturing defects on cavitation performance of a full-scale propeller. I. Simulation for the model- and full-scale propellers without defect

Paper I of this two-part paper focuses on predicting the open-water cavitation performance of the model- and full-scale propellers based on the geometry of David Taylor model basin 5168 propeller without leading-edge (LE) defect. Simulations were conducted using the three-dimensional (3D) steady Reynolds-Averaged Navier–Stokes solver in the commercial software package Star-CCM+. Effects of simulation parameters, including domain size, grid size, stretch ratio, first-grid spacing, y+, and turbulence model on the solutions were carefully examined and the best-practice settings for the model propeller and the full-scale propeller with no LE defect were developed. Validation studies were carried out for the propeller model against the experimental data. Results for the full-scale propeller were compared with those of the model-scale propeller. The differences in model and full-scale predictions are generally insignificant. To further confirm this finding, the scale effect was investigated with four additional scale ratios, λ = 2, 3, 4, and 5. The results showed that the scale effect is in general minimal and, therefore, the developed best-practice settings can be applied to the open-water simulation of the full-scale propeller. Paper II presents the simulation for the full-scale propellers with LE defects.

Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects

Acoustic metamaterials are a subject of increasing study and utility. Through designed combinations of geometries with material properties, acoustic metamaterials can be built to arbitrarily manipulate acoustic waves for various applications. Despite the theoretical advances in this field, however, acoustic metamaterials have seen limited penetration into industry and commercial use. This is largely due to the difficulty of manufacturing the intricate geometries that are integral to their function and the sensitivity of metamaterial designs to material batch variability and manufacturing defects. Capturing the effects of stochastic material properties and geometric defects requires empirical testing of manufactured samples, but this can quickly become prohibitively expensive with higher precision requirements or with an increasing number of input variables. This paper demonstrates how uncertainty quantification techniques, and more specifically the use of polynomial chaos expansions and spectral projections, can be used to greatly reduce sampling needs for characterizing acoustic metamaterial dispersion curves. With a novel method of encoding geometric defects in a 1D, interpretable, resolution-independent way, our uncertainty quantification approach allows for both stochastic material properties and geometric defects to be considered simultaneously. Two to three orders of magnitude sampling reductions down to ∼100 and ∼101 were achieved in 1D and 7D input space scenarios, respectively. Remarkably, this reduction in sampling was possible while preserving accurate output probability distributions of the metamaterial performance characteristics (bandgap size and location).

Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks

In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications.

Simplified Gambogic Acid Prodrug Nanoparticles to Improve Efficiency and Reduce Toxicity for Clinical Translation Potential.

Poor in vivo characteristics of gambogic acid (GA) and difficulties in industrial manufacturing of its nanocarriers have hindered its clinical translation. Therefore, a reproducible nano-drug delivery system must be developed to realize simpler manufacture and address inherent defects of GA, such as short circulation and severe side effects, in order to facilitate its clinical application. Herein, a drug self-assembled nanoparticles (NPs) consisting of a hydrophobic prodrug based on GA and oleyl alcohol (OA), as well as vitamin E-polyethylene glycol succinate (TPGS) as a shield to improve the stability of the NPs is reported. The preparation method is simple enough to stably facilitate large-scale manufacturing. The self-assembled NPs exhibit a remarkably high drug-loading capacity, and their prolonged circulation enables the NPs to demonstrate superior antitumor efficacy in both cellular and animal models. The flexible hydrophobic long chain wraps GA groups, which mitigates vascular irritation and reduces hemolysis rates. Consequently, the prodrug nano-system addresses GA-related concerns regarding stability, efficacy, and safety, offering a simple, stable, and secure nano-platform for similar candidate drugs.

Laser powder bed fusion manufactured TPMS cellular structures: Failure modeling by using initial damage concept and continuum damage mechanics model

The present study examined the compressive mechanical properties and failure modes of Schwarz P and Schwarz G Triple Periodic Minimal Surface (TPMS) cellular structures using Finite Element (FE) simulations and experimental tests. A novel approach based on the initial damage parameter, which accounts for the initial defects induced by the Laser Powder Bed Fusion (LPBF) manufacturing process, was employed to model the compression behavior of the cellular structures. Compression tests were conducted on 3 Schwarz P and 3 Schwarz G specimens. A digital camera captured the deformation patterns of the specimens, revealing that shear bands formed in the Schwarz P specimens, while the Schwarz G specimens deformed uniformly. The FE simulations utilized modified material parameters according to the Continuum Damage Mechanics (CDM) framework. Moreover, an isotropic CDM model with micro-defect closure effect was implemented to investigate the failure mechanisms of the specimens. The FE simulation results indicated that the CDM model accurately predicted the mechanical behavior of the cellular structures fabricated by the LPBF process. The Scanning Electron Microscopy (SEM) images confirmed that the specimens had manufacturing defects such as micro-voids, pores, and surface roughness and that the crack initiation locations corresponded to the maximum damage locations of the FE simulations.

Minimizing Defects in Additive Manufacturing and Laser Welding Through Energy Coupling Experiments and Dynamic Modelling

Additive manufacturing is a revolutionary technology that produces complex components as a single piece, replacing traditional assemblies and reducing waste. Laser powder bed fusion, a type of additive manufacturing, allows for limitless design freedom by building intricate parts layer-by-layer through melting and fusing metal powder. However, build quality remains inconsistent, with deformations like pores and spattering occurring. To improve modelling of the behaviour of melted metal, thus minimizing defects, an experiment which determined the metal’s absorptance of laser energy through integrating sphere radiometry was conducted. The integrating sphere, which spatially integrated a fraction of measured light to calculate full radiance, had to sit above the build plate and not interfere with powder spreading. I designed two parts which attached the integrating sphere to the motorized powder spreading blade. Utilizing small ridges in the enclosure as weight-bearing tracks, the blade’s torque was reduced while allowing precise movement of the sphere. Through implementing these parts, the experiment was conducted, whose results will aid in our understanding of laser-metal interactions. Defects like spattering also occur during laser welding, due to the vaporization of unstable liquid metal. To combat this, novel beam shapes have been seen to increase the stability of the keyhole. To model this effect, I created a phenomenological, dynamic model of the keyhole depth during a copper weld. Two differential equations were created to model the keyhole depth’s rate of change using a dual mode and single mode laser, depending on absorptance and incident laser intensity. The dynamic depth of the keyhole for both modes was numerical solved in Python and iterated using existing copper weld data. Through better quantitative understanding of energy coupling and numerical modelling of the resulting dynamics, defects in laser welding and additive manufacturing can be minimized.

Microservices architecture to enable an open platform for realizing zero defects in cyber-physical manufacturing

Abstract The market’s demand for high-quality products necessitates innovative manufacturing approaches that emphasize flexibility, adaptability, and the reduction of defects. Traditional systems are currently evolving towards embracing I4.0 technologies, including data collection, processing, analytics and digital twin, aiming for zero-defect manufacturing quality. This paper introduces an open platform, compliant with RAMI4.0 standards, designed to improve manufacturing quality. The platform integrates data using Asset Administration Shells with microservices adaptation for data ingestion and advanced analytics. Additionally, it incorporates Non-Destructive Inspection tools, demonstrating a seamless integration of measurement and quality assurance. This study details the architectural specification and validation of the openZDM platform, employing microservices to ensure flexibility, modularity and scalability, aligning with the RAMI4.0 model. The platform was validated through deployment in an automotive assembly line, highlighting its effectiveness in integrating inspection scenarios and early defect detection tools. The architecture employs a choreographed approach to manage loosely coupled microservices, enabling efficient data lifecycle management from collection through analytics to visualization.

Towards a Fracture Mechanics-Based Assessment for Fatigue Life Prediction of Ni–Ti Stents

The fatigue failure of Ni–Ti peripheral stents still represents an open issue of major concern due to the non-linear material behavior, the complex loads acting in vivo, and the manufacturing process. The fatigue assessment currently exploits total-life methodologies devoted to preventing crack nucleation. This work investigates a complementary fracture mechanics-based approach accounting for crack propagation from pre-existing manufacturing defects. Fatigue crack growth tests were performed on rolled Ni–Ti samples with a thickness and microstructure comparable to that of stents. A fracture mechanics-based assessment was implemented to predict the fatigue durability of surrogate samples tested at different mean and alternate strains. The fracture surfaces of the samples were inspected to determine a statistical distribution of defect size at the fracture origin. The cyclic J-integral was adopted as the crack driving force parameter, and it allowed to account for the complex response of the material, undergoing energy dissipations during phase transformation. Encouraging fatigue life predictions conforming to experimental data were obtained in the finite-life regime, whereas conservative estimates were computed below the fatigue threshold. This approach can be reverted to determine the maximum acceptable material defects for specific applications, providing a useful tool to manufacturing companies.

Detecting Manufacturing Defects on Printed Circuit Boards Using Metamaterial-Based Circular Microstrip Patch Antenna

In this study, a microstrip circular patch antenna is designed having a metamaterial-based (MTM) ground plane to detect manufacturing defects of short circuits and open circuits on printed circuit boards (PCB). In that regard, PCB specimens of FR-4 as the substrate and copper microstrip lines as the conductive wiring lines are designed and manufactured. Five vertical copper lines printed side by side on the top of the substrate are used to demonstrate the working mechanism of the designed antenna sensor. Two different defect scenarios of open and short circuits with controlled locations are studied to determine variations in the return loss data of the proposed structure. MTM cell structures with cross lines enclosed by an octagon ring are periodically placed as part of the ground plane of the proposed antenna to obtain higher sensitivity of the designed and manufactured sensor. Antenna return loss behaviors in terms of the locations of the faults are employed to prepare a database to detect not only the presence of the faults but also determine their locations. Since the samples to be measured are not irreversibly damaged during the testing process, the proposed design can be considered a non-destructive measurement method to provide information about the type and location of defects with real-time measurement data.

Stochastic Analysis of an Industrial System with Preparation Time Repair under Warranty Policy

The main purpose of this study is to examine the behavior of certain industrial systems under the conditions of product warranty policy. Given this policy, we are interested in considering all the parameters of the system in order to study them and show their impact on the proposed system. In order to increase the efficiency of the system and activate the product warranty policy, the system will undergo a preliminary examination for any system failure occurrence to determine the reasons that led to the failure. After completing the initial inspection, the mechanic will decide whether it is a user-caused failure or a manufacturing defect to determine whether or not the repair of the broken appliance is covered by the manufacturer’s contractual warranty policy. All times in the system are assumed as a negative exponential distribution. The expressions for some measures of system effectiveness such as reliability, mean time to system failure (MTSF), and availability are derived using the supplementary variable technique and Laplace transform. Sensitivity analysis and a relative sensitivity are then carried out in order to recognize how each system parameter affects performance measures. Finally, some numerical examples are designed and performed to show how the change of each system parameter affects system performance.

Detektion von Bindefehlern beim DED-Arc

Abstract Based on artificial intelligence (AI) developed for monitoring arc welding, this article presents a deep neural network for monitoring lack of fusion defects in wire arc additive manufacturing of aluminium. The aim is to detect defects in built-up volumes on the basis of weld source data. These can be successfully processed by the algorithm presented and a trained AI. The achieved accuracy of the network is > 90 percent.

Anode defects’ propagation in polymer electrolyte membrane fuel cells stack

This paper investigates how cell manufacturing defects can propagate within healthy areas of the same cell or to adjacent cells within a stack. To do so, a defective stack is assembled on the basis of 30 cells without defect and 5 defective cells with controlled lack of anode catalyst layer. The analysis of the degradation rate showed that the adjacent cells to the defective cells are the ones whose performance degraded the fastest, which implies that the presence of defects within the stack induces a propagation of the performance decrease. Post-mortem analyses are performed in the vicinity of the anode defect in the adjacent cell and highlight a significant degradation of perfluorosulfonic acid (PFSA) inducing hydrogen leakages through the membrane. The propagation of the degradation of the membrane in the surroundings of the anode defect to the adjacent cell can be discussed based on the stack design and operating conditions.