Types Of Defects Research Articles

Оценка качества и механических свойств получаемых слоев металла из низкоуглеродистой стали методом WAAM с использованием дополнительной механической и ультразвуковой обработки

Introduction. Additive manufacturing is a technology that enables three-dimensional (3D) components to be printed layer by layer according to digital models. Completely different from traditional manufacturing methods such as casting, forging, and machining, additive manufacturing is a near net shape manufacturing process that can greatly enhance design freedom and reduce manufacturing runtime. The material processing challenges in Wire and Arc Additive Manufacturing (WAAM) are related to achieving performance metrics related to geometric, physical, and material properties. Tight tolerances and stringent surface integrity requirements cannot be achieved by utilizing stand-alone AM technologies. Therefore, WAAM parts typically require some post-processing to meet requirements related to surface finish, dimensional tolerances and mechanical properties. It is therefore not surprising that the integration of AM with post-processing technologies into single and multi-setup machining solutions, commonly referred to as hybrid AM, has become a very attractive proposition for industry. The purpose of the work is to evaluate the quality and mechanical properties of the resulting metal layers of mild steel by WAAM method using additional mechanical and ultrasonic processing. Research Methods. To conduct the experiments, a set of welding equipment was used — a single-phase inverter device KEMPPI Kempomat 1701, designed for welding with wire in shielding gases. A mixture of argon and carbon dioxide (80 % argon and 20 % CO2) was used as a shielding gas. SV-08G2S (0.8 C-2 Mg-Si) wire was used as the surfacing material. A plate made of steel St3 with overall dimensions 150×100×5 mm was used as a base for surfacing. The surface of the plate before surfacing was thoroughly cleaned from the layer of oxides, oil, rust and other contaminants. For this purpose mechanical cleaning of the surface was used with BOSCH abrasive wheel with a diameter of 125 mm diameter and a grit size of 120. Before surfacing the surface of the product was degreased with white spirit. The gas flow rate was set at 8 dm3/min. To select the optimal wire feed rate and volt-ampere characteristic, surfacing was performed at each adjustment step of wire feed rate, and voltage. Mechanical statistical tensile tests, chemical composition analysis and metallographic studies were also performed. Results and Discussion. Gas porosity is a typical defect that occurs during the WAAM process and should be eliminated because it adversely affects the mechanical properties. Initially, gas porosity leads to a reduction in the mechanical strength of the part due to damage from microcrack formation. In addition, it often causes the surfaced layer to have worse fatigue properties due to the spatial distribution of different shape and size structures. In our experiments we found that a wire feed speed range of 5–6 m/min is optimal. Increasing the flow rate of shielding gas in the range of 8–14 l/min allows reducing porosity in the surfaced metal to almost zero. The mechanical properties of the surfaced beads show that the average value of yield strength after machining is higher than that of unprocessed specimens. The data obtained from these experiments are in good agreement with those reported in the literature. The presented results can be used in real WAAM technological processes.

Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts

In the high-stakes domain of precision manufacturing, Cubic Boron Nitride (CBN) inserts are pivotal for their hardness and durability. However, post-production surface defects on these inserts can compromise product integrity and performance. This paper proposes an automated detection and classification system using machine vision to scrutinize these surface defects. By integrating an optical bracket, a high-resolution industrial camera, precise lighting, and an advanced development board, the system employs digital image processing to ascertain and categorize imperfections on CBN inserts. The methodology initiates with a high-definition image capture by the imaging platform, tailored for CBN insert inspection. A suite of defect detection algorithms undergoes comparative analysis to discern their efficacy, emphasizing the impact of algorithm parameters and dataset diversity on detection precision. The most effective algorithm is then encapsulated into a versatile application, ensuring compatibility with various operating systems. Empirical verification of the system shows that the detection accuracy of multiple defect types exceeds 90%, and the tooth surface recognition efficiency significantly reaches three frames per second, with the front and side cutting surfaces of the tool in each frame. This breakthrough indicates a scalable, reliable solution for automatically detecting and classifying surface defects on CBN inserts, paving the way for enhanced quality control in automated, high-speed production lines.

New dimer integrable systems and defects in five dimensional gauge theory

We study the relation between the quantum integrable systems derived from the dimer graphs and five dimensional N = 1 supersymmetric gauge theories on S1 × ℝ4. We construct integrable systems based on new dimer graphs obtained from modification of hexagon dimer diagram. We study the gauge theories in correspondence to the newly proposed integrable systems. By examining three types of defects — a line defect, a canonical co-dimensional two defect and a monodromy defect — in five-dimensional gauge theory with N = 1 supersymmetry and Ωε1,ε2-background. We identify, in the ε2 → 0 limit, the canonical co-dimensional two defect satisfying the Baxter T-Q equation of the generalized A-type dimer integrable system, and the monodromy defect as its common eigenstate of the commuting Hamiltonians, with the eigenvalues being the expectation value of the BPS Wilson loop in the anti-symmetric representation of the bulk gauge group.

A Redox-Active Ionic Liquid Surface Treatment for Healing CsPbBr3 Nanocrystals.

Additive engineering of lead halide perovskites has been a successful strategy for reducing a variety of deleterious defect types. Ionic liquids (ILs) are a unique group of such additives that have been used to passivate halide vacancies in both bulk lead halide perovskites and their colloidal nanocrystal analogues. Herein, we expand the types of defects that can be addressed through IL treatments in CsPbBr3 nanocrystals with a novel phosphonium tribromide IL that heals metallic lead surface defects through redox chemistry. This new type of surface treatment leads to a significant increase in PLQY and outperforms equivalent treatments with non-redox-active bromide ILs. Such redox-active ligands widen the scope of defect types that can be addressed in semiconductor nanocrystals.

Combination of Enamel Matrix Derivatives with Bone Graft vs Bone Graft Alone in the Treatment of Periodontal Intrabony and Furcation Defects: A Systematic Review and Meta-Analysis.

To compare the clinical performance of the combination of enamel matrix derivatives and bone substitutes (EMD+BG) with bone substitutes (BG) alone in the surgical treatment of periodontal intrabony and furcation defects. Electronic databases (Medline, Embase and Web of Science) were searched for randomised controlled trials in humans that investigated the combination of EMD+BG vs BG alone in either intrabony or furcation defects with a minimal follow-up of 6 months. A random-effect meta-analysis was conducted according to the type of defect (intrabony or furcation defects) and the follow-up time (6 or ≥ 12 months). From a total of 1583 entries, 9 randomised controlled clinical trials (RCTs) were retrieved and included in the qualitative and quantitative synthesis. All of them were included in the meta-analysis. The meta-analysis detected an additional clinical attachment level (CAL) gain in intrabony defects treated with EMD+BG compared to BG alone in studies with ≥ 12-month follow-up (mean difference = 0.67 mm, 95% CI [0.44 ; 0.90], p 0.00001). No additional benefit was found in furcation defects in terms of CAL gain or probing depth (PD) reduction. The addition of EMD may improve the clinical outcomes of intrabony defects treated with bone substitutes. These findings may support the use of this combined therapy, particularly in large and non-contained defects.

Influence of planar defects on the mechanical behaviors of spherical metallic nanoparticles

Abstract In present study, we adopt molecular dynamics simulations to investigate the influences of typical planar defects, including twin boundaries (TBs), stacking faults (SFs) and grain boundaries (GBs), on the mechanical properties of fcc copper nanoparticles. Groups of nanoparticle samples, including defect-free single crystal and those with specific defects, are examined for elastic modulus, yield strength, and deformation mechanisms. Detailed results reveal that the elastic behavior of nanoparticles can be well described by a modified theoretical model regardless the type of defects. While the planar defects have negligible influence on the elastic modulus, they significantly enhance the yield strength of nanoparticles. Notably, nanoparticles containing fivefold TBs exhibit the highest yield stress, i.e. ∼17.0 GPa, even surpassing that of the defect-free counterparts, i.e. ∼10.0 GP. Analysis of atomic deformation unravels that the distinct yielding behaviors are attributed to the activation of different slip systems and the nucleation of dislocations at specific preferential sites. These findings highlight the potential of fabricating planar defects to tailor the mechanical properties of metallic nanoparticles for targeted applications in nanotechnology and materials science.

DEFECT CHARACTERIZATION OF METALLIC MATERIALS USING SELF-PARAMETERIZED DENSITY-BASED CLUSTERING AND COMPUTATIONAL INTELLIGENCE TECHNIQUES

Rapid growth of manufacturing industries is propelled by transformative technologies such as machine intelligence, autonomous computing and non-destructive testing (NDT). During the manufacturing of wrought products, there is no guarantee that the final product is 100-% flawless. Thus, all final products are subjected to quality checking to identify and eliminate defective products. In industries, most of internal defects are identified using NDT techniques, which fail to precisely characterize the defects. In this paper a novel algorithm, called Self-Parameterized Density-Based Clustering (SPDBC), is proposed for defect characterization. The proposed clustering method uses spatial parameters to identify the size and position of defects by filtering out the noise and other data that correspond to the non-defect area. Using these filtered data, computational intelligence techniques are employed to predict the defect type. SPDBC achieved Jaccard indices of 97.02 % and 98.78 % for identifying the defect size and position, respectively. Gradient boosting regression trees (GBRT) achieved a maximum accuracy of 97.44 % in predicting the defect type. As a result, the proposed approach can assist NDT experts in various sectors to differentiate between problem severities faster and replace defective parts before any major breakdown occurs.

Research on magnetic flux leakage testing of pipelines by finite element simulation combined with artificial neural network

Magnetic flux leakage (MFL) testing technology is widely employed in non-destructive testing of pipelines, and the analysis of leakage signals plays a crucial role in assessing pipelinea safety. This paper introduces a novel approach for MFL testing, which combines finite element simulation with artificial neural networks. First, a finite element model for MFL testing of defects is established, the influence of magnetization states on MFL signals is discussed, and the variation of signal extremum with magnetization intensity is analyzed. Next, suitable MFL signal features are selected to focus on the relationship between defect types, defect sizes, and these features. Finally, a kernel extreme learning machine (KELM) predictive model is developed to classify defect types and predict defect sizes. The results indicate that as magnetization intensity increases, the magnetization process of the pipeline can be divided into a nonlinear growth phase and a linear phase, with MFL signal extremum rapidly increasing and then gradually growing linearly. Different geometric features of defects correspond to distinct distributions of MFL signals, effectively reflecting variations in defect types and sizes. Compared to traditional ELM models, the KELM model achieves higher prediction accuracy and stable performance, with the radial basis kernel function significantly enhancing the generalization and predictive capabilities of the neural network.

Trapezius Muscle Flap for Posterior Neck and Shoulder Defects Following Malignant Tumor Resection-A Case Series from a Tertiary Cancer Facility.

Reconstructing defects of the shoulder and posterior scalp, arising following tumor resection, requires careful consideration of available options. While free tissue transfers and local advancement flaps have their established roles, the trapezius muscle flap offers a unique set of advantages like proximity, versatility, and reliability as it has a robust blood supply and relatively straightforward surgical technique. In this retrospective analysis, demographic and disease profiles of 10 patients with posterior scalp and shoulder defects following tumor resection, which were reconstructed with a trapezius flap between November 2020 and November 2023, were studied. Complications and functional outcomes were analyzed. Ten patients (9 men and 1 woman) were reconstructed using a trapezius flap. The mean age of the study sample was 45.3 years (30-60 years). Two patients developed donor site seroma and one of them developed wound dehiscence; one patient had partial necrosis of the flap tip, all managed conservatively, and one patient had tumor recurrence. However, the long-term result was good in all patients except the one with recurrence. The post-excision defects in the shoulder and posterior scalp were of mean size 13 ± 2 × 6 ± 2 cm, and the donor site morbidity analyzed by using VAS (visual analog scale) had a mean score of less than 3. This case series provides valuable insights into the successful use of the trapezius muscle flap for shoulder and posterior scalp defects following malignant tumor resection. The documented complications are minimal in comparison to the overall positive outcomes, making the trapezius muscle flap a viable and reliable option for reconstruction in this specific clinical scenario. However, further research is required to improve the design of the flap for different types of defects, refine surgical techniques, and compare outcomes with other reconstruction methods.

Finely controlled bioceramic granules and sonoporation for osteogenic bone defect repair and reconstruction

Osteoporotic fractures caused by imbalance in bone homeostasis like chemotherapy, loss of ovarian function，glucocorticoid use and aging require special surgical procedure to treat and restore anatomical function. However, due to escalating bone weakness and impairment, it remains a clinical challenge to treat these types of fracture defects through conventional means. Herein, we fabricated a novel calcium silicate‑strontium (CS-Sr35) bioceramic granules with high biological properties and combined it with ultrasound aided sonoporation (sono) in augmenting osteoporotic critical-sized bone defect. The use of sonoporation mediated by microbubble was to influence efficient response for tissue permeability and delivery of bioactive ions from the bioceramic scaffold. The graded robust granule with adjustable microstructure exhibited physiochemical and biointegration properties favorable for ion release in vitro and osteogenic related activities in vivo. Herein, we compared the treatment of bone defect repair using (i) no scaffold (ii) CS-Sr35 only and (iii) CS-Sr35 + sono. The gradual bone repair process was elucidated by X-ray, histology and micro-CT analyses. Overall, our results showed that the CS-Sr35 + sono exhibited a tailored biodissolution behavior with complete defect repair and reconstruction. Meanwhile, the group with only CS-Sr35 granules had less osteoblastic healing while the blank group showed inadequate critical-sized defect repair throughout the study. Our study suggests that, the synergistic technique of combining bioceramic granule with sonoporation significantly optimizes osteogenic activity and biomineralization ability of the bioceramic granule for new bone growth in osteoporotic pathology and also this tailored technique provides versatile approach for improving the biological effect for next generation reconstruction and repair of diseased bone defect.

Design and analysis of pneumatic composite phononic crystal

Tuning band gaps in soft phononic crystal by air pressure-induced deformation is a feasible method to manipulate elastic waves. In this paper, we design a pneumatic composite phononic crystal that incorporates a labyrinthine structure into the Helmholtz-type phononic crystal. By calculating the band gaps of the representative volume element (RVE) arranged in a two-dimensional triangular lattice, we find that this design effectively extends the transmission path of sound waves, achieving band gaps in the vicinity of the lower frequency range. We apply pressure to the scatterer’s pneumatic actuator to adjust its volume through the deformation of soft material, altering the air filling rate and enabling reversible adjustment of the frequency and width of band gaps. Nonlinear finite element simulations investigate the effects of different pressure levels on the band gaps. Furthermore, we explore bandgaps tuning in one-dimensional phononic crystals and the impact of various types of point defects on the transmission characteristics of phononic crystals. Results show that the pneumatic phononic crystal exhibits rich band gaps under air pressure loading, in which a low-frequency band gap exists, and is capable of reversible continuous tuning. This study provides valuable for manipulating elastic waves in periodic structures and designing soft acoustic devices.

Identification and characteristic analysis of internal defects in rock-filled concrete based on deep learning method

The distribution characteristics and geometric morphology characteristics of defects within RFC are important factors affecting the strength properties and rupture morphology of RFC. However, the excessive size of commonly used aggregates for RFC leads to difficulties in conducting in-depth experimental studies indoors. Based on the improved U-Net and image processing technology, this research establishes an integrated model for the identification, classification, and extraction of defects inside the RFC, quantitatively counts and analyzes the acquired defect distribution characteristics and geometrical morphology characteristics, and establishes a defect characteristic distribution function that can be used for the numerical reconstruction of defects. In order to realize the acceleration of U-Net training using training weights, use VGG-16 with the fully connected layer removed instead of the Encoder part of the U-Net. The integrated model in this research can realize automatic identification, classification, and extraction of multiple types of defects at the same time, and the established distribution function of defect characteristics provides a data basis and new ideas for the establishment of RFC three-dimensional numerical models containing real defects.

Ultrasonic Nondestructive Testing of Li-ion Batteries for Anomaly Detection and Quality Control: Applications and Signal Processing

Use of ultrasonic nondestructive methods for quality control and testing of different types of Lithium-ion battery is progressing in recent years. This study provides a comprehensive review of the current applications and technical challenges of ultrasonic testing in inspection of lithium-ion batteries. First, basic principles and theoretical modeling of such batteries are shown. Secondly, it summarizes the most common defect types in these batteries. Finally, it talks about signal processing methods used for feature extraction and development.

Implementing a deep learning model for defect classification in Thai Arabica green coffee beans

Arabica coffee is a significant economic driver in Northern Thailand and has substantial opportunities for market growth. However, the Thai coffee business must ensure consistent quality standards and is currently heavily dependent on manual labor, to first identify, and then remove substandard unroasted coffee beans. This research developed a classification model based on a Convolutional Neural Network to detect 17 types of defects in green coffee beans. The image augmentation phase was enhanced by rotating images at 45, 90, 135, 180, 225, and 270° and expanding the dataset from 979 original images across 17 defect types to a robust 6,853 images. Several architectures including MobileNetV2, MobileNetV3, EfficientNetV2, InceptionV2, and ResNetV2 were assessed. Following extensive evaluations, MobileNetV3 emerged as the best-performing model and underwent further fine-tuning, achieving significant accuracy improvements through hyperparameter optimization. The model's robustness and generalizability were validated via 5-fold cross-validation, with accuracy ranging from 98.78 % to 99.84 % across all defect types. When tested with unseen data, the model achieved an accuracy of 88.63 %. A web application prototype was also developed for real-time coffee bean defect classification and its usability was tested. Seven farmers reported high satisfaction with the ease of use and effectiveness of the application in classifying coffee bean defects, with 71.4 % expressing a strong likelihood of recommending the application to others. These promising results demonstrate the practical utility of the model in enhancing quality sorting processes in the coffee industry.

Lateral forehead flap in oral reconstruction: A case report

Introduction: Lateral Forehead flap is very versatile as it represents a good choice for reparation of different types of nose and oral cavity defects and provides an optimal, aesthetic, and functional outcome. In era of free transfer tissue, the use of loco-regional flaps, alone or in combination may prove to be the only practical solution in the absence of resources or other options. Case presentation: A-65 years old male with multiple comorbidities underwent a wide resection with adequate margin of the lower lip and small segment of the upper lip due to a locally advanced squamous carcinoma. After long consideration, the last resort was the pedicled lateral forehead flap. Information was sourced from patient’s case notes and operating theatre report. Results: The donor defect was covered with a split-thickness skin graft harvested from the right thigh area. The flap survived and both functional and aesthetic outcome were satisfactory. The site of the donor defect result was also acceptable. No complications were noted in our case study beside a small tip necrosis and breakdown of the suture line. Conclusion: Lateral forehead flap is a reliable flap with an acceptable outcome in cases with previous history of radiotherapy or surgery. Decision making of a particular reconstructive option should be done taking in consideration the available resources, surgical team expertise along with the patient's general condition. The pedicled forehead flap still useful when other options are inappropriate or have not been successful.

Quantifying the impact of construction defects on square RC columns

Reinforced concrete (RC) columns are integral to structural integrity, and their performance is highly dependent on precise construction practices. This study investigates the influence of construction defects on the behavior of RC columns, focusing on key parameters such as misplaced longitudinal (vertical) reinforcing bars, honeycombed concrete, variations in stirrup spacing, casting eccentricity, and the effectiveness of square welded wire mesh for confinement. An experimental program was conducted with thirteen square RC column specimens designed to simulate potential construction defects. The findings demonstrate a clear relationship between defect types and column performance. Misplacement of vertical reinforcing bars by up to 50 % resulted in a 17 % reduction in ultimate load capacity. Honeycombed concrete, due to inadequate compaction, was identified as the most harmful defect, leading to a 46 % decrease in ultimate load and a 34 % reduction in toughness. Conversely, reducing stirrup spacing, while maintaining reinforcement quantity, enhanced column performance; a 33 % reduction in spacing resulted in comparable ultimate load but a 36 % increase in toughness. Increased casting eccentricity negatively impacted performance, with a 30 mm eccentricity causing a 46 % reduction in ultimate load capacity. The addition of square welded wire mesh over stirrups significantly improved performance, yielding a 13 % increase in ultimate load, while the use of welded wire mesh alone was ineffective, resulting in nearly a 50 % decrease in load capacity. These results underscore the importance of proper construction practices for the safety and durability of concrete structures. Accurate reinforcement placement, thorough concrete compaction, and precise column alignment are crucial. The research also suggests that using square welded wire mesh alongside stirrups can mitigate some construction defects’ adverse effects. Future studies should explore a broader range of column designs and loading conditions, as well as the effectiveness of various repair and strengthening techniques for columns with existing defects.

Enhancing strength-ductility synergy by introducing multilattice defects and heterogeneous structures in CoCrNi-based medium-entropy alloys prepared by powder plasma arc additive manufacturing

In the work, cold rolling and annealing were applied to powder plasma arc additively manufactured (CoCrNi)94Al3Ti3 medium-entropy alloy (MEA) to efficiently attain different types of lattice defects and heterogeneous structures, thereby enhancing the strength of the alloy. Tensile tests show that mechanical properties of the MEA were significantly enhanced after cold rolling and annealing treatments compared to the directly deposited alloys. The microstructure and mechanical properties of cold rolled samples (50 % thickness reduction) annealed at 1073–1273 K for 1 h are compared. It has been shown that the MEAs prepared by additive manufacturing accumulate a large amount of deformation energy within the grains during the cold rolling process. This lead to recrystallized grains first nucleating within the original columnar grains, and efficient recrystallization could be realized. At annealing temperatures ≤1173 K, the recrystallized grain size has not been coarsened, the coarse and fine grains formed a heterogeneous grain structure, leading to significant back stress strengthening. TEM observations at different alloys indicate that the formation and increase in the number of multiple lattice defects (SFs, DTs, and L–C locks) is the main reason for the high work-hardening capacity of the alloy. This investigation demonstrates that the combined approach provides a novel means to fabricate high strength and ductile CoCrNi-based MEAs.

Grain-Boundary Elimination via Liquid Medium Annealing toward High-Efficiency Sb2Se3 Solar Cells.

Suppression of charge recombination caused by unfavorable grain boundaries (GBs) in polycrystalline thin films is essential for improving the optoelectronic performance of semiconductor devices. For emerging antimony selenide (Sb2Se3) materials, the unique quasi-1D structure intensifies the dependence of GB properties on the grain size and orientation, which also increases the impact of defects related to grain structure on device performance. However, these characteristics pose significant challenges in the preparation of thin films. In this study, a novel annealing approach using ammonia-thiourea is developed mixed solution as the liquid medium (LM) to finely regulate the crystallization of Sb2Se3 films, resulting in micron-sized large grains with enhanced [hk1] orientation and fewer defects. Mechanistic studies indicate that the intermediate phase formed at the GBs promotes the growth of large grains. Moreover, LM creates a closed and uniform environment for thin-film annealing, suppressing the volatilization of Se and reducing the types of deep-level defects. Consequently, the film delivers a device efficiency of 9.28%, the highest efficiency achieved for Sb2Se3 solar cells fabricated via thermal evaporation. Hence, this study provides a facile and effective annealing method for controlling the crystallization of low-dimensional materials and offers valuable guidance for the development of chalcogenide materials.

Research on X-ray nondestructive defect detection method of tire based on dynamic Snake Convolution YOLO model

Tire X-ray nondestructive testing before leaving the factory is crucial for driving safety. Given the complexity of tire structures and the diversity of defect types, traditional manual visual inspections and machine learning methods face significant challenges in terms of accuracy and efficiency. This study proposes an innovative tire X-ray image nondestructive testing technique based on the YOLOv5 model, incorporating several advanced technologies to enhance detection performance. Specifically, we introduce Dynamic Snake Convolution (DSConv), which adaptively focuses on slender and curved features within tires. Additionally, we have designed a C3 module based on DSConv, specifically targeting slender defects such as cord-overlap and cord-cracking. To improve the detection accuracy of small defects, we redesigned the neck network structure and introduced the Scale sequence feature fusion module (SSFF) and the Triple feature encoding module (TFE) to integrate multi-scale information from different network layers. Furthermore, we developed the Convolution Block Attention Module, integrated into the SSFF, which effectively reduces the interference of complex backgrounds and focuses on defect recognition. In the post-processing stage, we employed the Soft-NMS algorithm to optimize the confidence of candidate detection boxes, enhancing the accuracy of box selection. The experimental results show that compared to the YOLOv5 benchmark model, the algorithm proposed in this study achieved a 5.9 percentage point increase in mAP0.5 and a 5.7 percentage point increase in mAP0.5:0.95, demonstrating superior detection accuracy compared to current mainstream object detection algorithms and effectively completing the nondestructive testing task of tire defects.

Co-occurrence of Charcot-Marie-Tooth disease type 1 and glomerulosclerosis in a patient with a de novo INF2 variant

BackgroundRenal disease is associated with Charcot-Marie-Tooth disease (CMT), a common inherited neurological disorder. Three forms of CMT have been identified: CMT1 of the demyelinating type, CMT2 of the axonal defect type, and intermediate type (Int-CMT). INF2 is an important target for variants that cause the complex symptoms of focal segmental glomerulosclerosis (FSGS) and CMT.Case presentationWe report the case of a 13-year-old female Chinese patient (born in 2011) with a rare co-occurrence of CMT1 and glomerulosclerosis (GS) (CMT1-GS). The patient presented with slowly progressive gait disorder with unsteadiness during walking, pes cavus, and kyphoscoliosis since the age of 1 year. Electrophysiological studies and brain magnetic resonance imaging revealed demyelinating features consistent with CMT1. At 12 years of age, she was hospitalised for hypertension and dizziness; her serum albumin was 27.9 g/L, serum creatinine was 87 μmol/L, estimated glomerular filtration rate was 88.6 mL/min, and 24-h urine protein was 4.95 g. A renal biopsy showed glomerulosclerosis. Renal function deteriorated further during the follow-up period, and she received a kidney transplant at the age of 13. Whole-exome sequencing identified a de novo heterozygous c.326T > G (p.Met109Arg) variant in exon 2 of INF2. The variant was classified as “pathogenic” according to the American College of Medical Genetics and Genomics criteria.ConclusionsWe describe a rare clinical phenotype of CMT1-GS associated with a de novo variant of INF2. Our findings expand the phenotypic and genotypic spectrums of INF2-associated disorders.