Fabrication Quality Research Articles

An experimental investigation into the influence of incorrect root gaps in welded-in transverse stiffeners on fatigue performance

The paper presents a comprehensive set of special experimental fatigue tests designed to determine the fatigue strength sensitivity of steel components, under circumstances where fabrication quality levels deviate from regulatory standards. Precise monitoring of the manufacturing process of test specimens containing internal imperfections facilitates accurate documentation of the phenomena that contribute to the eventual shape of the imperfection. Welded-in stiffeners with incorrect root gaps of varying sizes were subjected to fatigue testing, as such studies have never been carried out in experimental form before, as the background data to EN 1993-1-9 disclosed. The experimental investigation confirms numerical estimations, which predicted that even large root gaps do not result in lower fatigue strength values, when compared to stiffeners that were properly fitted. Moreover, details in welded beams that feature an intersection of three welds at the stiffener exhibit no discernible decrease in fatigue performance compared to rolled sections.

Development and Application of an Interval-Valued Pythagorean Fuzzy Set-Based Grade Evaluation Method to Foreign Fiber Content

ABSTRACT To solve the problem of the grade evaluation of foreign fiber content in cotton, this paper divided the problem into multi-criteria decision-making field and proposed an interval-valued Pythagorean fuzzy set foreign fiber content grade evaluation method. Taking into account the various properties of foreign fibers, the multi-criteria evaluation of single foreign fiber content and comprehensive foreign fiber content is carried out to improve the quality of cotton textiles. Based on the Pythagorean fuzzy set, we used AHP and TOPSIS methods to determine the relevant weight information, combined with the grade evaluation index of foreign fiber content and the interval-valued Pythagorean fuzzy weighted average or geometric operators to construct a fiber multi-criteria decision model. Then, the evaluation index function is improved to make the score function more consistent with the facts, and the digitization and intelligence methods required for foreign fiber content detection and grade evaluation are developed. Further, an example is given to verify the feasibility and effectiveness of the model. In addition, the multi-criteria decision-making method adopted has high theoretical innovation and application value for the textile industry.

Yarn tension model and vibration analysis during unwinding of carbon fiber bobbins

A low-quality unwinding process of the unwinding machine can lead to downtime of the knitting machine and loss of fabric quality. To address this issue, we studied the tension and vibration of carbon fiber yarns during unwinding. Based on the theory of axially moving strings, the dynamic model of carbon fiber yarn was established during the unwinding process, bringing the parameters into the simulation. According to the simulation results, the yarn tension in the unwinding process is closely related to the spring preload, which needs control within a certain range. When the unwinding speed increases, the fluctuation amplitude, lateral vibration amplitude, and axial vibration amplitude of the yarn tension gradually increase, causing friction and wear of the carbon fiber yarn. By controlling factors such as spring preload, unwinding speed, and the number of bobbins unwound simultaneously, one can effectively control the yarn’s vibration and improve the quality of the yarn.

Chemical extraction and its effect on the properties of cordleaf burbark (Triumfetta cordifolia A. rich) fibres for the manufacture of textile yarns

Tropical Triumfetta cordifolia (TC) fibre extracted from the equatorial region of Cameroon has been characterized as a potential fibre for textiles. An investigation of extraction parameters to soften this fibre is crucial to use it as a biobased material in the spinning process. To obtain textile quality fibres, 34 sodium hydroxide extraction tests were carried out to study the effect of extraction conditions on its characteristics. Thus, three levels of concentrations (0.5, 1.0 and 1.5 wt%), temperatures (80, 100 and 120 °C) and durations (120, 180 and 240min) were used for extraction by cooking, and at room temperature, durations of 120, 150 or 180 min with three concentrations (2.5, 3.0 and 3.5 wt%) were considered. Only 6 combinations produced fibres that were clear and soft to the touch, without defects (corrugations, stuck fibres) and without residual bark epidermis at the macroscopic scale. For these fibres, the dissolution of non-cellulosic substances, morphological, physical, thermal and mechanical properties depended on the austerity of the alkaline retting. Under mild conditions, the SEM surfaces of the fibres showed large residues of the middle lamella, which made the lignin content (10 wt%) and hydrophilic function higher. Under medium conditions, the fibre surfaces were clean and slightly wrinkled (at 80 °C; 120min). Under severe conditions, heterogeneous transverse shrinkage and wrinkling were observed and accompanied by cellulose degradation (39 wt%) with a significant reduction in tenacity at 16cN/tex. The medium extraction conditions were considered more effective, and their fibres showed cellulose content up to 49 wt%, density up to 1.39 g cm−3, “Fickian” moisture absorption kinetics with saturation up to 11 wt%, thermal stability up to 237 °C, Young's modulus up to 3.7 GPa, tensile strength up to 113 MPa and tenacity up to 40cN/tex. These new results were compared with lignocellulosic textile fibres in the literature, showing similarity with banana, sisal and jute fibres.

Exploring weft knit fabric defects based on their presence and quality impact: A case study


 
 
 
 While addressing grey fabric quality in a renowned circular weft knitting mill of Bangladesh, the authors experienced some questionable approach practiced by knitters. The subjective nature of defect detection by knitters/inspectors often time causes wrong emphasizing on frequently occurring defect(s) instead of focusing on influential defect(s) and subsequently, employing wrong quality control approach to minimize the grey fabric defects. Knit fabric defects (e.g., hole, stain, press-off, gout, miss knit, barrè, tucking, etc.) should be assessed by type, fault coverage, gravity and the frequency of occurrence instead of focusing only on frequency of occurrence in the fabric. In this study, grey weft-knitted fabric quality is investigated based on influential defects instead of frequently occurring defects. Quality data of single jersey, fleece and 1X1 rib were gathered and analyzed from an established knitting factory in Bangladesh over three months duration. A fabric inspection machine and 4-point inspection method were employed in this study. Gout was found as the most frequently occurring defect for each fabric type but not influential for rib fabric. For a significant amount of knitted fabrics, totaling of 55,524.91 m2 inspected fabric, the most occurring defects were ranked as gout, press-off, hole, miss knit, stain, and tucking and influential defects (based on inspection points) were ranked as gout, press-off, hole, stain, miss knit, and tucking (highest to lowest). In the inspection report, the knitter/inspector mistakenly categorized gout as the most occurring as well as the most influential defect for 1X1 rib fabrics and suggested remedies accordingly.
 
 
 

Investigation of the composition, structure and magnetic properties of the Nd2Fe14B ceramics dependence on the initial powder characteristics and spark plasma sintering modes

High phase and structural sensitivity of magnetic properties strongly impacts the fabrication quality of the permanent magnets, therefore revealing effects of synthesis conditions on physico-chemical characteristics of magnetic materials is of paramount importance for optimization of magnet production especially in case when non-conventional techniques are involved. Here we report for the influence of pulse current frequency on structure, composition, and magnetic properties of an industrially-important Nd2Fe14B magnetic ceramics on the initial powder characteristics and the modes of its spark plasma sintering (SPS) in a vacuum environment. The influence of initial powder mechanical grinding as well as the number of generated electric current pulses (in the range from 3.3 to 326.7 ms) in SPS conditions on the structure (defects and pores formation, grain size), phase and element composition (formation of secondary nonmagnetic phases and oxides, impurities) of Nd2Fe14B alloy was studied by SEM, TEM, EDS and XRD methods. The regularity of changes in the magnetization saturation value (σ) and coercive force (Нс) depending on the morphology, structure and composition of the initial Nd2Fe14B powder and the alloy obtained by mechanical milling and subsequent sintering at different modes of pulse current has been established.

Investigation of the effect of preparation processes on CO2 laser-faded denim fabric quality

Purpose In the denim industry, enzyme washing and its combination with stone washing are generally used to get the desired worn-out look. However, these conventional methods include high water, energy and time consumption. Nowadays, laser fading, which is a computer-controlled, dry, ecological finishing method, is preferred in the denim fading process. The purpose of this study is to observe the effects of chemical pretreatment applications on laser-faded denim fabric in terms of color and mechanical properties. To eliminate the enzyme washing process in denim fading and to minimize the disadvantages of laser fading, such as decreased mechanical properties and increased fabric yellowness, various chemical pretreatment applications were applied to the denim fabric before laser fading, followed by simple rinsing instead of enzyme washing. Design/methodology/approach Two different indigo-dyed, organic cotton denim fabrics with different unit weights were exposed to pretreatment processes and then laser treatment, followed by simple rinsing. Polysilicic acid, boric acid, borax and bicarbonate were used for pretreatment processes, and laser treatment was carried out under optimized laser parameters (40 dpi resolution and 300 µs pixel time). Tensile strength was tested, and color values (CIE L*, a*, b*, ΔE*, C* and h), color yield (K/S), yellowness and whiteness indexes were measured to identify the color differences. Findings Before laser fading, 30 g/L and 40 g/L polysilicic acid pretreatments for sulfur-indigo-dyed fabric and a mixture of 10 g/L boric acid and 10 g/L borax pretreatments for the fabric only indigo-dyed were recommended for the laser fading with sufficient mechanical properties and good color values. Originality/value With the chemical pretreatments defined in this study, it was possible to reduce yellowness and maintain the mechanical properties after laser fading, thus minimizing the disadvantages of laser treatment and also eliminating enzyme washing.

Portable Instruments Based on NIR Sensors and Multivariate Statistical Methods for a Semiautomatic Quality Control of Textiles

Near-infrared (NIR) spectroscopy is a widely used technique for determining the composition of textile fibers. This paper analyzes the possibility of using low-cost portable NIR sensors based on InGaAs PIN photodiode array detectors to acquire the NIR spectra of textile samples. The NIR spectra are then processed by applying a sequential application of multivariate statistical methods (principal component analysis, canonical variate analysis, and the k-nearest neighbor classifier) to classify the textile samples based on their composition. This paper tries to solve a real problem faced by a knitwear manufacturer, which arose because different pieces of the same garment were made with “identical” acrylic yarns from two suppliers. The sweaters had a composition of 50% acrylic, 45% wool, and 5% viscose. The problem occurred after the garments were dyed, where different shades were observed due to the different origins of the acrylic yarns. This is a challenging real-world problem for two reasons. First, there is the need to differentiate between acrylic yarns of different origins, which experts say cannot be visually distinguished before garments are dyed. Second, measurements are made in the field using portable NIR sensors rather than in a controlled laboratory using sophisticated and expensive benchtop NIR spectrometers. The experimental results obtained with the portable sensors achieved a classification accuracy of 95%, slightly lower than the 100% obtained with the high-performance laboratory benchtop NIR spectrometer. The results presented in this paper show that portable NIR sensors combined with appropriate multivariate statistical classification methods can be effectively used for on-site textile quality control.

Smart fabric inspection using Mimosa pudica plant

Fabric quality governing and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using the sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the plant biologically named “Mimosa pudica”i. This method consists of two stages. The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with aid of SPSA. The proposed work SPSA is developed for defective pixels identification in both uniform and non-uniform patterns of fabrics. In this work, SPSA has been done by checking with devised condition, correlation and error probability. Every pixel will be checked with the developed algorithm, to get marked either defective or non-defective pixels. The proposed SPSA has been tested on the different types of fabric defect databases and shows a prodigious performance over existing methods like the Differential evolution based optimal Gabor filter model (DEOGF), Gabor filter bank (GFB), Adaptive sparse representation-based detection model (ASR) and Fourier and wavelet shrinkage (FWR).

Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5.

It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recognition accuracy of small foreign fibers, and low detection speed. A polarization imaging device of cotton foreign fiber was constructed based on the difference in optical properties and polarization characteristics between cotton fibers. An object detection and classification algorithm based on an improved YOLOv5 was proposed to achieve small foreign fiber recognition and classification. The methods were as follows: (1) The lightweight network Shufflenetv2 with the Hard-Swish activation function was used as the backbone feature extraction network to improve the detection speed and reduce the model volume. (2) The PANet network connection of YOLOv5 was modified to obtain a fine-grained feature map to improve the detection accuracy for small targets. (3) A CA attention module was added to the YOLOv5 network to increase the weight of the useful features while suppressing the weight of invalid features to improve the detection accuracy of foreign fiber targets. Moreover, we conducted ablation experiments on the improved strategy. The model volume, mAP@0.5, mAP@0.5:0.95, and FPS of the improved YOLOv5 were up to 0.75 MB, 96.9%, 59.9%, and 385 f/s, respectively, compared to YOLOv5, and the improved YOLOv5 increased by 1.03%, 7.13%, and 126.47%, respectively, which proves that the method can be applied to the vision system of an actual production line for cotton foreign fiber detection.

A dual-index quality evaluation method for direct ink writing of soft materials

Direct ink writing is a versatile bioprinting technology to fabricate soft models, where soft materials are extruded through an orifice and settled on a platform to form multi-layered structures. An extruded filament is the basic unit of the extrusion process which can affect the overall quality of model fabrication. Here, we report a novel method to evaluate the quality of printing via introducing two dimensionless indices, which independently reflect the average width and the width variation of extruded filaments. Different concentrations of Pluronic® F127 materials were used to print zigzag multi-layer models to understand the influences of printing parameters on both indices. Results show the dual-index method can semi-quantitatively expose the morphological differences of extrudates under altered printing conditions, providing a potential evaluation tool for the quality assessment of direct ink writing.

ЯНГИ ТУЗИЛИШЛИ БЎЙЛАМА ПАХТА-ИПАКЛИ ТРИКОТАЖ ТЎҚИМАЛАРИ СИФАТИНИ КОМПЛЕКС БАҲОЛАШ

The supply of modern equipment and technology plays an important role in the production of quality products at textile enterprises. Improving the quality of products in turn contributes to expanding the range of products and satisfying consumer demand. The article provides a comprehensive assessment of the quality indicators of cotton and silk yarns using local raw materials. Four types of cotton-silk knitting samples with longitudinal patterns were knitted on a double-needle knitting machine of the 14th class manufactured by LongXing. Evaluation of the quality of knitted fabrics makes it possible to select rational options among them. The introduction of these results will serve to fill the domestic and foreign markets with high-quality products - lightweight outerwear.

Predicting quality parameters of denim fabrics using developed ANN based Artificial Bee Colony algorithm

Denim is a special fabric preferred by all segments of society. Many production factors affect the properties, performance levels and quality of denim products. In today’s textile manufacturing management, quality parameter prediction is very important. In the context of this work, a new prediction model has been introduced for calculating denim fabric quality parameters. The meta-heuristic combination of Artificial Neural Network (ANN) and Artificial Bee Colony (ABC) algorithm is the proposed in this study. The proposed hybrid model was evaluated using data on production parameters acquired from a significant Turkish manufacturer of denim fabric. The denim fabric quality parameters results obtained from the ANN and the proposed ANN based ABC (ABC-ANN) algorithms were compared with the results of all models on test data. The performances of the measured quality values were evaluated and compared in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Receiver Operating Characteristic (ROC) curve analysis. According to the comparison results, the proposed ANN-based ABC algorithm is superior in predicting the mechanical properties of denim fabric from the production parameters.

Solar Thermal Textiles for On-Body Radiative Energy Collection Inspired by Polar Animals.

Humans use textiles to maintain thermal homeostasis amidst environmental extremes but known textiles have limited thermal windows. There is evidence that polar-dwelling animals have evolved a different mechanism of thermoregulation by using optical polymer materials to achieve an on-body "greenhouse" effect. Here, we design a bilayer textile to mimic these adaptations. Two ultralightweight fabrics with complementary optical functions, a polypropylene visible-transparent insulator and a nylon visible-absorber-infrared-reflector coated with a conjugated polymer, perform the same putative function as polar bear hair and skin, respectively. While retaining familiar textile qualities, these layers suppress dissipation of body heat and maximize radiative absorption of visible light. Under moderate illumination of 130 W/m2, the textile achieves a heating effect of +10 °C relative to a typical cotton T-shirt which is 30% heavier. Current approaches to personal radiative heating are limited to absorber/reflector layer optimization alone and fail to reproduce the thermoregulation afforded by the absorber-transmitter structure of polar animal pelts. With increasing pressures to adapt to a rapidly changing climate, our work leverages optical polymers to bridge this gap and evolve the basic function of textiles.

Hybrid mutation moth flame optimization with deep learning-based smart fabric defect detection

The occurrence of faults in textile manufacturing methods results in major wastage of the properties. Additionally, it also affects the quality of the fabric products. Manual inspection methods fail to detect the defects with respect to efficiency, accuracy, and consistency due to size of the detects, carelessness, and optical illusion. Therefore, automatic fabric inspection has become a powerful tool to optimize the quality of fabric. In this background, the current study develops a novel Hybrid Mutation Moth Flame Optimization with Deep Learning-Based Smart Fabric Defect Detection (HMFODL-FDD) technique for sustainable manufacturing. The proposed HMFODL-FDD technique exploits the Computer Vision (CV) and Deep Learning (DL) techniques for the detection of defects in fabric. To accomplish this objective, the presented HMFODL-FDD technique employs contrast enhancement process to boost the quality of the images. For feature extraction, the HMFODL-FDD technique uses Inception v3 model with HMFO algorithm-based hyperparameter optimizer. Finally, the classification of the fabric defects is performed using Back Propagation Neural Network (BPNN) model. The experimental outcomes confirmed that the proposed HMFODL-FDD technique achieved superior performance over DL techniques in terms of effective defect classification in fabric images with a maximum accuracy of 95.47%.

CPA Methodology: educational technological design proposal to solve problems

Technology Enhanced Learning (TEL) is a teaching and learning approach that utilizes various forms of technology to enhance the learning experience (Cochrane, Redmond, and Corrin, 2018). One example of TEL is the use of digital fabrication, where we can find various open-source prototyping platforms, in educational settings. ADAPTA is an assistive technology educational event, in which elder people and students came together in meaningful and authentic domestic problem-solving educational activity, implementing the CPA methodology, based on a process of observation and prototyping, attempts to open a space for the design of assistive parts/devices for the elderly to improve your quality of life, through creation, testing, fabrication and usage of the pieces. A relevant aspect of the CPA methodology is that it allows the cultivation of 21st Century skills, through challenging projects that encourage the four Ps – Project, Passion, Peers and Play (Resnick, 2017).
 
 The CPA in Spanish, (APC in English for Autonomous Project Cell) methodology is a four-step process that can be used in conjunction with digital design processes and digital fabrication to support TEL. The four steps of the CPA methodology are: (a) Observation: This step involves students observing and documenting their surroundings or a specific problem they want to solve. This can involve collecting data, taking photos, or sketching out ideas in a real space (b) Documentation: In this step, students document their observations and ideas in a clear and organized manner. This can involve creating a written report, creating a visual representation of their ideas, using an open source online platform (c) Make/reflect: In this step, students used analog drawing and sketches, digital drawing, and digital fabrication. Later, the Arduino platform to create a prototype of their solution. This can involve programming the Arduino using a variety of programming languages, such as C++ or Python, and connecting various electronic components to the Arduino to create their desired outcome. As they work on their prototype, students should also be encouraged to reflect on their progress and make any necessary adjustments (d) Show and Tell: In the final step, students present their prototype to their peers and teachers/guides, demonstrating the functioning of their solution and explaining the process they followed to create it. This step allows students to share their learning with others and receive feedback on their work.
 
 Following the 2018 OECD objectives that aspire to an education that promotes individual, social and planetary well-being, we pay special attention to the domestic problems experienced by older adults. Considering that 16.2% of the country's inhabitants are over 60 years of age and, according to data from the Casen Survey, some 460,000 elderly people live in single-person households in Chile, it is important to pay attention to how aging creates difficulties in mobility and coexistence with the environment of the domestic space, such as the kitchen and bathroom, with the handling of forces and weights, difficult access, unattainable heights, etc. The workshop took place at a municipal space (Hub Providencia) in Santiago Chile, which met every week, showing them a variety of possible solutions to start observing in their homes, how they interacted with space and usability.
 Examples of possible solutions: 3d printed gadgets: open bottle, cary 3 bags, separate a book, open a bottle Nb 2.By following the CPA methodology, students were engaged in authentic problem-solving activities and learn valuable skills, such as critical thinking, communication, and collaboration. The use of digital drawing and fabrication, and Arduino in TEL could help the elder group to start designing from the drawing (pencil and paper). Afterwards this product was taken by the younger group of students, who were in charge of building the physical prototype. By providing hands-on opportunities for both group of students to engage in authentic problem-solving activities, they could converge on physical products that were finally given from the younger students to the elder ones.

Exploring commonly used terms from online reviews in the fashion field to predict review helpfulness

Online shopping for fashion products is a challenging process for consumers. Although customers can facilitate purchasing, review content and helpful voting systems can be unreliable. This study aims to apply linguistic approaches on term recognition to identify and extract frequent terms in fashion reviews and predict their helpfulness. Features are chosen using the latent Dirichlet allocation (LDA) model for topics, bi-grams using the term frequency- inverse document frequency (TF-IDF) vectorizer and topics plus bi-grams using the TF-IDF vectorizer. The feature sets are then used to train four supervised algorithms on an imbalanced dataset to highlight the model performance. Models are validated using a dataset of 828,700 customer reviews collected from Amazon Fashion platform. The experimental results show that choosing LDA plus n-grams using the TF-IDF vectorizer for a random forest classifier outperforms the other models, with an accuracy of 0.81 and an F1-score of 0.78. Furthermore, the study indicates that reviews describing fabric quality, trend and fashion aesthetics, size details, price, and return experience are more helpful. Using the results, customers are made aware of how to narrow their search terms and retailers can optimize their review system more intelligently, especially on the first page of a product's description.

In situ monitoring in laser melt injection based on fusion of infrared thermal and high-speed camera images

Unstable reliability and poor repeatability in the production of metal matrix composite (MMC) parts are primary challenges currently faced by laser melt injection (LMI). In situ monitoring of LMI during the part fabrication process provides a sound solution. This research presents promising results that aim to bridge the knowledge gap by determining an innovative technique to correlate the combined features of infrared thermal and high-speed cameras to state monitoring of LMI and the forming quality identification of MMC. The combined features of the molten pool and spatter were extracted based on the understanding of LMI physical mechanisms and then were selected by the fisher score algorithm with the introduction of weighting factor (WF-FS). The support vector machine (SVM) prediction models based on single-sensor features, multi-sensor features, and fusion features decomposed with different methods were developed to identify the quality of MMC. The results indicated that the multi-sensor features exhibit better classification performance. The empirical mode decomposition (EMD) method can improve classification performance by mining the depth and subtle feature information of multi-source features. The optimal feature subset with 9 EMD features was selected by principal component analysis to balance computational period and classification accuracy. This work provides an effective and novel approach to monitor the LMI process and evaluate the MMC part fabrication quality.

A review on additive manufacturing of SS-Ni multi-material fabrication

Additive manufacturing (AM) is an emerging technology used to fabricate complex and near net shape components with minimum tooling and material wastage. This technology has found its application in biomedical, aerospace, automobile and structural elements. Multi material additive manufacturing (MMAM) process is able to manufacture components built with a combination of different materials like metals, ceramics, and reinforced materials to achieve the desired properties at a specific location. Many multi-material combinations have been fabricated through MMAM such as Al-Fe, Al-Cu, SS-Ni, Ni-Ti, Ti-SS, diamond-W-Cu etc. The most common combination among these discussed materials are SS-Ni multi-material for its wide range of applications in gas turbine parts, energy industry, petrochemical industry, marine industry, nuclear fission reactors, aerospace structure and aerospace engines. The processing of SS-Ni multi-material using LPBF, DED and WAAM has been found to provide superior properties such as high strength at extreme temperature environment, corrosion resistance, oxidation resistance, higher toughness at low cost when compared with conventional manufacturing methods thereby, motivating the present review on SS-Ni multi-material AM. This review aims to summarize the various methods used for the fabrication of SS-Ni multi-material AM, different combination strategies and its effect on the quality of fabrication, their microstructure variations, property enhancement, challenges and applications.

On the existing test dataset of isotropic cylindrical metal shells under axial compression and the design of modern metal civil engineering shells

In the structural engineering limit state design philosophy of the Eurocodes, a target reliability is achieved by using partial safety factors on both the loading and the resistance that are, in principle, to be calibrated based on test data. The partial factor that is currently used for the buckling limit state of metal shells has been adopted from the knowledge base on other structural elements and has been retained for reasons of historical continuity and to maintain a close relationship between all Eurocode steel design standards. However, the mechanics of the behaviour of thin-walled metal shells gives strong reasons to believe that the partial factors for buckling should be dependent on the shell geometrical form, slenderness, load case and quality of fabrication for the target reliability to be consistently achieved. None of these are currently considered in defining the partial factor for resistance.The most ubiquitous thin-walled metal shell structures are imperfection-sensitive cylinders under uniform axial compression. A dataset of many hundreds of these test results has thus been accumulated over many decades, though it is of variable quality and sparsely documented. This paper shows that this dataset is an entirely inappropriate basis on which to calibrate the safety level of full-scale metal civil engineering shells. Indeed, the professional community should face the uncomfortable reckoning that an experimental test dataset suitable for the reliable calibration of the safety level of design relationships for full-scale metal civil engineering shells may likely never come into existance, and that the bolder approach of extensive computational simulation must instead be embraced.