Several Types Of Defects Research Articles

Application of Central Composite Design in the Drilling Process of Carbon Fiber-Reinforced Polymer Composite

Composite materials are utilized in various industries due to their advantageous properties. Drilling is a crucial process for joining these materials to construct the structures. During the drilling of composite materials, several types of defect can occur, with delamination being the most prevalent. Delamination adversely effects the properties of the drilled hole and diminishes the quality of the final structure. Thrust force is a key parameter used to monitor the drilling process; a higher thrust force increases the likelihood of defects, particularly delamination, in the drilled area. In this article, a central composite design is applied to the drilling process of carbon fiber-reinforced polymer (CFRP) composites, focusing on parameters such as rotational speed, feed rate, and the angle between the composite layer sequences. The objective is to minimize delamination factors and thrust force. The effect of drilling parameters on the responses is analyzed independently. The results indicate that the derived models can predict the thrust force and delamination factors in the drilling of CFRP composites.

Quality Control of Ceramic Wall Products Six Sigma Method with Dmaic Tools and Failure Mode and Effect Analysis (FMEA)

Considering the importance of increasingly rapid industrial development, business people must be aware of the orientation towards product quality. The manufacturing industry has an important role in industrial development. It is hoped that it will be able to grow and have advantages in industrial development, therefore it is necessary to improve and increase product quality. The company continues to strive to maintain and improve product quality and increase productivity so that customers are satisfied with the products produced. One way that needs to be done is to reduce the number of defective products in the production process. This effort is one way of continuous improvement carried out by PT. ABC and companies operating in the same field. The ceramic wall production process has relatively high demand but still has a high level of defects. Based on information on production data and data on the number of defects for the period January – September 2023, wall ceramic production has a defect rate of 3.86%. Companies must reduce the number of defects to achieve company targets. This research aims to improve the production process of wall ceramics by minimizing the number of defective products. This research uses the six sigma method with the DMAIC (Define, Measure, Analyze, Improve, Control) tool assisted by the FMEA (Failure Mode and Analysis) method. Corrective action to reduce defects based on the 5 Why analysis is to find out what the problem is and ask "why" and "what is the root of the problem". The research results show that there are 4 types of defects which have a large number of defects, namely Rupture Defects, Application Defects, Peeling Defects and Hole Defects. From the research results, solutions were obtained for the emergence of several types of defects in the wall ceramic production process, namely by providing planning suggestions and corrective actions which are discussed this time. At the defect calculation stage, Broken produces a DPMO value of 29.362 with a sigma level of 3.39 with a sigma level of 3.48, Defect Peeled produces a DPMO value of 28.044 with a sigma level of 3.38, and holes are defective. resulting in a DPMO value of 26,020 with a sigma value of 3.45 from the data calculation results, it is still not enough to meet the company's target at the sigma level, therefore the company must make quality improvements to achieve the main target at the sigma level.

Transverse modulational dynamics of quenched patterns.

We study the modulational dynamics of striped patterns formed in the wake of a planar directional quench. Such quenches, which move across a medium and nucleate pattern-forming instabilities in their wake, have been shown in numerous applications to control and select the wavenumber and orientation of striped phases. In the context of the prototypical complex Ginzburg-Landau and Swift-Hohenberg equations, we use a multiple-scale analysis to derive a one-dimensional viscous Burgers' equation, which describes the long-wavelength modulational and defect dynamics in the direction transverse to the quenching motion, that is, along the quenching line. We show that the wavenumber selecting properties of the quench determine the nonlinear flux parameter in the Burgers' modulation equation, while the viscosity parameter of the Burgers' equation is naturally determined by the transverse diffusivity of the pure stripe state. We use this approximation to accurately characterize the transverse dynamics of several types of defects formed in the wake, including grain boundaries and phase-slips.

PENGENDALIAN KUALITAS PRODUK HPL MENGGUNAKAN METODE SIX SIGMA DAN FAILURE MODE AND EFFECT ANALYSIS UNTUK MENGURANGI CACAT DI PT TACO ANUGRAH CORPORINDO

The competition in the manufacturing industry in Indonesia is getting tighter, thus encouraging the industry to compete to improve the quality of its products. PT Taco Anugrah Corporindo is one of the manufacturing industries that produces HPL (High Pressure Laminate). In the production process, HPL produces several types of defects, the average for each type of defect still exceeds the KPI (Key Performance Indicator) set by the company. PT Taco Anugrah Corporindo wants the level of defects to be within the KPI range that has been set by identifying problems and repairing them. The purpose of this study is to reduce the number of defects. Six Sigma with the DMAIC method as a quality improvement to achieve the target of 3.4 failures per one million opportunities (DPMO). Using FMEA to Analysis the causes of the problem and recommendations for improvements. The results of this study found six types of potential CTQ, that is 71% PM, 11% PMLP, 7% BH, 5% DP, 4% Overlay and 3% TMB. The average calculation of the DPMO and Sigma Level values is 4,465 which is still far from 6 sigma. The causes of defects in products are caused by several factors such as humans, machines, methods, materials, and the environment. As for recommendations for improvement that can be given are the preparation of SOP and IK, training for operators, adjusting the machine by installing grounding and anti-static, monitoring work methods and maintaining the workplace.

Quality control of plastic sack products using the DMAIC method at PT. XYZ

PT. XYZ is a company engaged in the production of plastic sacks which are used for various industrial purposes. There are several types of defects in plastic sacks, namely, torn sacks, inappropriate sack brands, and slanted seams. This research aims to determine the level of product defects, factors causing product defects and provide suggestions for improvements to the production process at PT. XYZ. This research uses the six sigma method through the DMAIC stages (Define, Measure, Analyze, Improve and Control). Based on research results, the defect rate for plastic sack products was 2.72%, while the average level was 3.9 sigma. There are several factors that cause defects in plastic sack products, namely human factors, machines, work methods and materials. Proposed improvements to improve product quality at PT. XYZ there are several factors that must be improved, namely, humans are carrying out training and directing employees to follow SOPs, machines are making continuous maintenance schedules according to machine capacity so that they are always in good condition, work method factors the company must make additional SOPs so that work method problems are reduced. In the future, material is tightening the process of checking the inspection of threads that are not good properly.

Quality Control Analysis of Piston Gasoline Products using the DMAICApproach

The manufacturing company in this study is a company engaged in the automotive sector that produces pistons and non-pistons. The problem that occurs in the company is that the average percentage of defects exceeds the company's standard limit of 5% while the maximum defect limit is set by the company at 1%. Identify the dominant type of defect, identify the biggest factors causing the dominant type of defect and make suggestions for improvement. In this study using the DMAIC method (Define, Measure, Analyze, Improve and Control) in the production process of the type x gasoline piston, there are several types of defects, namely monoiri, misrun,coating loss. The types of defects that occur are based on the Pareto diagram, namely the type of defect monoiri with a percentage of 56%, misrun 28% and coating loss 16%, and the causal factors are known using a fishbone diagram, there are 5 factors, namely material, people, methods, machines and environment. Based on the results of the identification of FMEA and the calculation of the highest RPN, it is known that the monoirritic defects that occur in the causative factor of the material with an RPN value of 175. For proposed improvements, namely monitoring and creating monitoring forms to facilitate cleaning of materials on a scale, and to facilitate cleaning of larger materials. scheduled.

Quality Control of Camille Beauty Face Mask Products Using Failure Mode and Effect Analysis Method : A Case Study

Regarding criticism and suggestions from consumers, it shows that of the 881,140 products sold, there are 22,947 products that are considered manufacturing defects, with an average percentage of defective products per month of 2.71%, with several types of defects submitted by consumers through the survey. This is quite serious because it will provide a bad experience to consumers in using this product. Failure Mode and Effect Analysis (FMEA) is a method used to evaluate system design by considering existing failure modes in a machine or equipment component system and analyzing the consequences for the reliability of the system. Based on the method used, it was found that the highest RPN value was in a case of disability, namely causing itching, with an RPN value of 504, consisting of a severity value of 9, occurrence of 7 and detection of 8, with a percentage of RPN occurrences of 35%. Beased on this result, it proposes improvements to the priority types of product defects and other product defects.

Quasi Real-Time Apple Defect Segmentation Using Deep Learning.

Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case.

Clinical Indications of Cultured Epithelial Autografts.

Cultured epithelial autografts (CEAs) have been used for decades as a treatment for massive burn injuries. Cultured epithelial autografts allow for wounds to heal by taking a small sample and growing a patient's own epithelium in culture to create large, graftable sheets. This technique is especially useful in large wounds where donor sites are limited compared with conventional skin grafting. However, CEAs have a variety of uses in wound healing and reconstruction and have the potential to aid in the closure of several types of defects. Cultured epithelial autografts have shown applicability in large burns, chronic nonhealing wounds, ulcerating wounds of various etiologies, congenital defects, wounds requiring specialized epithelium to replace like by like, and wounds in critically ill patients. Several factors must be considered when using CEAs, such as time, cost, and outcomes. In this article, we detail the various clinical applications of CEAs and how they can be situationally advantageous outside of their original purpose.

Characterization of magnesium channeled implantation layers in GaN(0001)

The effect of Mg channeled implantation into epitaxially grown gallium nitride (GaN) was studied using Hall-effect measurements, photoluminescence (PL), transmission electron microscopy (TEM), scanning transmission electron microscopy (STEM) and Rutherford backscattering spectroscopy (RBS). In the channeled implantation, deeper profiles were obtained with lower implantation energy and less damage compared to random implantation. The donor–acceptor pair signal at ∼3.28 eV, suggesting that Mg is activated, was confirmed by PL measurement when the ion dose and implantation energy are 1 × 1014 cm−2 and 20 keV, respectively. However, even with channeled implantation, several types of defects including point defects and oblong defects as seen in the random implantation were observed by TEM/STEM analysis. RBS analysis showed slightly worse crystal qualities in channeled implantation compared to non-implanted samples. Mg channeled implantation is useful to achieve deeper profiles (>1 μm), but further condition tuning of process will be necessary for practical application.

PENGENDALIAN KUALITAS PADA PRODUKSI DEEP PLATE MENGGUNAKAN METODE SIX SIGMA

So far, the industry has developed smoothly, and the application of Industry 4.0 has been widely adopted by enterprises. The company adopts industry 4.0 because it fulfills all its needs. Good quality will result from a good process and in accordance with predetermined quality standards based on market needs. This research was conducted at PT.Culletprima Setia which is a private company that produces deep plates. This study aims to analyze the Deep plate at PT.Culletprima Setia. There are several types of defects So defective products make the company have to reprocess the results of defective products. Defects that have been observed include cracks, non-round surfaces, wavy mouth parts, and spots The author plans to complete this research by carrying out quality control for Deep Plate and also carrying out quality control using the DMAIC approach which is expected to help the company PT.Culletprima Setia in identifying problems based on the six sigma, with an approach using this method used to analyze non-direct field research occurs so that conclusions can be drawn for continued improvements. With them the authors can draw conclusions so that routine quality improvement processes to reduce product defects occur. Thus the author can provide advice by taking into account the conditions of the workers on the production floor, especially for jobs that require personal protective equipment and make regulations that require workers to use personal protective equipment when working.

Basic study of the relaxation volume of crystalline defects in bcc iron

The relaxation volume (Ωrel), here determined per extra-atom or vacant site, of common crystalline defects in bcc iron (Fe) was calculated from molecular dynamics (MD) simulation cells containing defects of varying size and/or density. To this end, we used both real and reciprocal space data: for the former, the change in the MD cell volume was calculated, while for the latter, we computed X-ray diffraction reciprocal space maps to evaluate the change in the lattice parameter. We show that 〈110〉 dumbbell self-interstitial atoms have the largest Ωrel, ∼1.5atomic volume (∼1.5 Ω0). C15 clusters of size 12 and 48 atoms show Ωrel of ∼ 0.91 Ω0 and ∼ 0.98 Ω0, respectively, and similar values are found for ½〈111〉 and 〈100〉 interstitial dislocation loops, with Ωrel ∼ 0.905 Ω0 and Ωrel ∼ 0.873 Ω0, respectively. Single vacancies are characterized by a negative Ωrel, ∼−0.11 Ω0. For cavities, Ωrel rapidly increases to approach zero as the clusters grow. Using these values, we managed to predict (with an accuracy better than 2 %) the lattice strain in MD cells containing several types of defects, which indicates that the relaxation volumes can be summed up to estimate the microscopic (i.e., lattice) volume change.

Pengendalian mutu modified cassava flour (mocaf) di PT. Rumah Mocaf Indonesia, Banjarnegara, Jawa Tengah

Indonesia is a country with abundant products in agriculture and plantation. One of the agricultural products in Indonesia is cassava. PT Rumah Mocaf Indonesia is a company that focuses on developing mocaf products to improve the welfare of cassava farmers and drive the rural economy. Company produce mocaf following company standards and SNI, one of the requirements is quality control of mocaf production for continuous evaluation and improvement to achieve the condition with no defective products. The purpose of mocaf quality control is to find out the types of defects and the factors that cause them. The sampling method, in this case, used random sampling, and the quality control used The Seven Tools method combining Pareto Chart and Fishbone Diagram. Based on the results of the analysis that has been carried out, the results of the Pareto Chart show that there are several types of defects, there are in odor, color, and moisture, with the chief defect of mocaf found in the odor criteria of flour with 44 % chief defect. The results of the Fishbone Diagram show the factors that cause defects in mocaf are influenced by man factor due to the lack of knowledge and skills when carrying out the mocaf productions, material factor due to long growing period with high moisture content, method factor due to unstable drying temperature and enzyme dose and time during fermentation there are not following regulations, machine factor due to dirty machine and small capacities of the machine, and environment factor due to unpredictable weather and temperature.

Edge-glued wooden panel defect detection using deep learning

The wood-based furniture manufacturing industries prioritize quality of production to meet higher market demands. Identifying various types of edge-glued wooden panel defects are a challenge for a human worker or a camera. Several studies have shown that the detection of edge-glued defects with low, high, normal, overlong, short is identified but detection of residue and bluntness is highly challenging. Thus, the present model identifies defects of low, high, normal, overlong, short by computer vision and/or deep learning, whereas defects of residue and bluntness by deep learning based decide by pass for having better performance. The goal of this paper is to provide an improved defect detection solution for wood-based furniture manufacturing industries by process automation. Therefore, a system was designed that takes defect input images from a camera as raw image and laser-aligned image for defect detection of the edge-glued wooden panel. The process automation then performs computer vision-based image features extraction with deep learning for defect detection. The aim of this paper is to solve edge-glued defect detection problems by using design and implementation of edge-glued wooden defect detection, that can be stated as edge-glued wooden panel defect detection using deep learning (WDD-DL) for process automation by artificial intelligence and Automated Optical Inspection (AOI) consolidation. Possibly there exist several types of defects on the edges while edge-banding on the wooden panel in furniture manufacturing. Therefore, the scope is to achieve higher accuracy by raw image and laser-aligned image feature extraction using deep learning algorithms for final result defect classification in WDD-DL by AOI. The WDD-DL system uses Gabor, Harris corner, morphology, structured light detection and curvature calculation for pre-processing and InceptionResnetV2 Convolutional Neural Network algorithm to attain the best results. The applications of this work can be found in quality control of the furniture manufacturing industry for an edge, corner, joint defect detection of the wooden panels. The WDD-DL achieves best results as the precision, recall and F1 score are 0.97, 0.90 and 0.92, respectively. The experiments demonstrate higher accuracy achievement as compared to other methods with overkill and escape rate analysis. Ultimately, the discussion section provides an interesting experience sharing about the necessary factors for implementing the WDD-DL in real-time industrial operations.

Investigations Into the Role of Native Defects on Photovoltaic and Spintronic Properties in Copper Oxide

CuO is a promising contender for photovoltaics, photodetection, photocatalysis, and spintronics in theory, but experimental success in terms of device performance has been limited. We used experimental and theoretical techniques to examine the fascinating optoelectronic and spintronic features of a p-type semiconductor; i.e. copper oxide. Absorption spectra of CuO have revealed intriguing properties such as defect-induced strong absorption in the visible and near-infrared (NIR) regions, making it an attractive candidate for NIR and broadband detection. Additionally, due to its antiferromagnetic characteristics, CuO has potential applications in spintronics. Clearly, these applicability ranges are greatly dependent on the intrinsic material qualities and defects. To gain a better understanding of CuO band structures, defect dynamics, charge distribution, and absorption properties; ab-initio calculations were conducted in a systematic manner. Additionally, the stability of several types of defects has been investigated theoretically in Cu and O-rich environments. The literature is ambiguous about the stability of several defects in CuO, including copper vacancies, oxygen vacancies, and interstitials. Interestingly, it is discovered that V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cu</sub> -V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</sub> di-vacancies and V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</sub> are extremely stable in O-deficient environments, whereas V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cu</sub> is highly stable in O-rich environments. Numerous defects such as copper vacancies, oxygen vacancies, and di-vacancies all contribute significantly to the photovoltaic features such as quantum-efficiency. Furthermore, unlike pristine CuO, defect assisted CuO has a significant magnetic moment as shown by first-principle calculations, making it a suitable option for spintronics. The work will open several features of CuO for next generation devices.

An experimental investigation concerning the effect of AFP defects on progressive damage in CFRP coupons

Manufacturing of composite laminates using the fiber placement process introduces the possibility of several types of defects and features that are not present in traditional hand-layup fabrication techniques. The effect of these defects on structural performance and failure has not been fully investigated, especially for those defects with small geometrical features. The innovation lies in using advanced methods to capture the effect of defects generated during manufacturing processes which can be used to determine if these defects warrant immediate repair to ensure part integrity. Several types and configurations of defects that may occur during the automated fiber placement (AFP) process were inserted within the coupons. Acoustic emission (AE) was employed as a nondestructive evaluation technique to investigate correlations between mechanical properties and damage propagation during tension and 3-point bending tests. Digital Image Correlation (DIC) was utilized to monitor damage progression in the bending test.

FUZZY FMEA APPLICATION TO IDENTIFICATION RISK IN-PROCESS PRODUCTION OF TOYOTA HI-ACE WIRING HARNESS PRODUCT

In product manufacture, the high failure rate problem of produce product is the number of product defects. Several types of defects have a high enough percentage. To solve this problem, we need to identify the failures and to get the assessment information of the three risk factors. Our research using the traditional FMEA method at the production of Wiring Harness products to shows the current condition of various modes of failure in those areas. This study focuses on implementing fuzzy FMEA to identify the potential risks that may occur along with the assembling of the Wiring Harness process. The fuzzy FMEA approach is preventing product and process problems before they occur, this paper is also expected to result in some mitigation effort that can be applied to improve the Wiring Harness production process. With the Fuzzy FMEA method, we have found the highest FRPN value that shows the highest defect such as damage insulation is 8.5, damage terminal is 8.5, and the damaged part is 8.5 and the highest RPN from the traditional FMEA is damage insulation (324). To solve this problem, we propose to use the fishbone diagram and give suggestions for improvements to the highest failure modes that are damaged insulation.

Multi-Class Object Learning with Application to Fabric Defects Detection

Deep convolutional neural networks (CNNs) have shown great success in single-class fabric image detection. However, real-world fabric defect images generally contain several types of defects in one image. Accurately recognizing and classifying multi-class fabric defect images is still an unsolved issue due to the complexity of intersected defects, as well as difficulty in distinguishing small-size defects. To address these challenges, this study develops a methodology based on the deep learning feature pyramid networks (FPN) approach to detect multi-class fabric defects. To evaluate the proposed detection model, we built a unique multi-class fabric defects database (DHU-MO1000), where multi-class defect images are generated by industrial monitors from a textile factory. We used the dataset as the benchmark for multi-class defects detection training and testing the FPN. Furthermore, we conducted extensive experimental validations for various design choices. The experimental results show that the model outperformed existing multi-class object detection methods.

Benchmarking of Nondestructive Testing for Additive Manufacturing.

Defect detection in additive manufacturing (AM) is of paramount importance to improve the reliability of products. Nondestructive testing is not yet widely used for defect detection. The main challenges are a lack of standards and methods, the types and location of defects, and the complex geometry of many parts. During selective laser melting (SLM), several types of defects can occur such as porosity, cracking, and lack of fusion. In this study, several nondestructive tests were conducted in a highly complex shaped part in AISI 316L stainless steel with real defects manufactured by SLM. Two additional artificial defects (one horizontal and one flat bottom hole) were produced and the defect detectability was evaluated. The techniques used were as follows: dye penetrant, infrared thermography, immersion ultrasonic, eddy current, and X-ray microcomputed tomography to assess different types of defects in the as-built part. We conclude that no single technique can detect every type of defect, although multiple techniques provide complementary and redundant information to critically evaluate the integrity of the parts. This approach is fundamental for improving the reliability of defect detection, which will help expand the potential for using AM to produce parts for critical structural applications.

Carbon quantum dots: Synthesis and correlation of luminescence behavior with microstructure

Two kinds of carbon quantum dots, C-dots-160 and C-dots-200 with different fluorescence luminescence behaviors were synthesized by a one-step hydrothermal method at 160 and 200 °C, respectively, using ammonium citrate as the raw material. The relationship between the microstructure of the C-dots and the fluorescence emission behavior was investigated. Results indicate that an increase of synthesis temperature introduces more oxygen and nitrogen atoms into the C-dots, increasing the total number of structural defects and altering their concentration ratio. It is this ratio difference in the two C-dots that causes their different luminescence behaviors. The proportion of several types of defects in the C-dots-200 are relatively balanced, leading to excitation wavelength-dependent fluorescence while the most abundant defects in C-dots-160 are in the form of C=O, which is the main reason for its excitation independent luminescence behavior. The number of structural defects in C-dots-160 is less than in C-dots-200 and the latter has the stronger fluorescence emission.