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This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

This study aims to propose a facilitating methodology for the application of Fuzzy FMEA (Failure Mode and Effect Analysis), comparing the traditional approach with fuzzy variations, supported by a case application in the aeronautical sector.

Based on experts' opinions in risk analysis within the aeronautical sector, rules governing the relationship between severity, occurrence, detection and risk factor were defined. This served as input for developing a fuzzyfied FMEA tool using the Matlab Fuzzy Logic Toolbox. The tool was applied to the sealing process in a company within the aeronautical sector, using triangular and trapezoidal membership functions, and the results were compared with the traditional FMEA approach.

The results of the comparative application of traditional FMEA and fuzzyfied FMEA using triangular and trapezoidal functions have yielded valuable insights into risk analysis. The findings indicated that fuzzyfied FMEA maintained coherence with the traditional analysis in identifying higher-risk effects, aligning with the prioritization of critical failure modes. Additionally, fuzzyfied FMEA allowed for a more refined prioritization by accounting for variations in each variable through fuzzy rules, thereby improving the accuracy of risk analysis and providing a more realistic representation of potential hazards. The application of the developed fuzzyfied FMEA approach showed promise in enhancing risk assessment in the aeronautical sector by considering uncertainties and offering a more detailed and context-specific analysis compared to conventional FMEA.

This study emphasizes the potential of fuzzyfied FMEA in enhancing risk assessment by accurately identifying critical failure modes and providing a more realistic representation of potential hazards. The application case reveals that the proposed tool can be integrated with expert knowledge to improve decision-making processes and risk mitigation strategies within the aeronautical industry. Due to its straightforward approach, this facilitating methodology could also prove beneficial in other industrial sectors.

This paper presents the development and application of a facilitating methodology for implementing Fuzzy FMEA, comparing it with the traditional approach and incorporating variations using triangular and trapezoidal functions. This proposed methodology uses the Toolbox Fuzzy Logic of Matlab to create a fuzzyfied FMEA tool, enabling a more nuanced and context-specific risk analysis by considering uncertainties.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

This study aims to quantify and prioritize the main causes of lean wastes and to apply reduction methods by employing better waste cause identification methodologies.

We employed fuzzy techniques for order preference by similarity to the ideal solution (FTOPSIS), fuzzy analytical hierarchy process (FAHP), and failure mode effect analysis (FMEA) to determine the causes of defects. To determine the current defect cause identification procedures, time studies, checklists, and process flow charts were employed. The study focuses on the sewing department of a clothing industry in Addis Ababa, Ethiopia.

These techniques outperform conventional techniques and offer a better solution for challenging decision-making situations. Each lean waste’s FMEA criteria, such as severity, occurrence, and detectability, were examined. A pairwise comparison revealed that defect has a larger effect than other lean wastes. Defects were mostly caused by inadequate operator training. To minimize lean waste, prioritizing their causes is crucial.

The research focuses on a case company and the result could not be generalized for the whole industry.

The study used quantitative approaches to quantify and prioritize the causes of lean waste in the garment industry and provides insight for industrialists to focus on the waste causes to improve their quality performance.

The methodology of integrating FMEA with FAHP and FTOPSIS was the new contribution to have a better solution to decision variables by considering the severity, occurrence, and detectability of the causes of wastes. The data collection approach was based on experts’ focus group discussion to rate the main causes of defects which could provide optimal values of defect cause prioritization.

This study aims to comprehensively investigate the mixed-mode fracture behavior and mechanical properties of selective laser sintering (SLS) polyamide 12 (PA12) components, considering different build orientations and layer thicknesses. The primary objectives include the following. Conducting mixed-mode fracture and mechanical analyses on SLS PA12 parts. Investigating the influence of build orientation and layer thickness on the mechanical properties of SLS-printed components. Examining the fracture mechanisms of SLS-produced Arcan fracture and tensile specimens through experimental methods and finite element analyses.

The research used a combination of experimental techniques and numerical analyses. Tensile and Arcan fracture specimens were fabricated using the SLS process with varying build orientations (X, X–Y, Z) and layer thicknesses (0.1 mm, 0.2 mm). Mechanical properties, including tensile strength, modulus of elasticity and critical stress intensity factor, were quantified through experimental testing. Mixed-mode fracture tests were conducted using a specialized fixture, and finite element analyses using the J-integral method were performed to calculate fracture toughness. Scanning electron microscopy (SEM) was used for detailed morphological analysis of fractured surfaces.

The investigation revealed that the highest tensile properties were achieved in samples fabricated horizontally in the X orientation with a layer thickness of 0.1 mm. Additionally, parts manufactured with a layer thickness of 0.2 mm exhibited favorable mixed-mode fracture behavior. The results emphasize the significance of build orientation and layer thickness in influencing mechanical properties and fracture behavior. SEM analysis provided valuable insights into the failure mechanisms of SLS-produced PA12 components.

This study contributes to the field of additive manufacturing by providing a comprehensive analysis of the mixed-mode fracture behavior and mechanical properties of SLS-produced PA12 components. The investigation offers novel insights into the influence of build orientation and layer thickness on the performance of such components. The combination of experimental testing, numerical analyses and SEM morphological observations enhances the understanding of fracture behavior in additive manufacturing processes. The findings contribute to optimizing the design and manufacturing of high-quality PA12 components using SLS technology.

This study provides an analysis of patch repair for cracked aircraft structures. Delamination is a type of damage that affects the patch's behavior. The purpose of this study is to assess the influence of delamination on repair performance.

An analytical and numerical study using the finite element method was conducted for a cracked plate repaired with a patch containing a pre-existing delamination defect. The method for defining the contact pair surfaces and modeling the delamination interaction within the patch interface is specified using the virtual crack closure technique (VCCT) approach.

The efficiency of the repair is measured in terms of the J-integral. The effects of delamination initiation, mechanical loading, crack length and patch stacking sequences are presented. It is noted that in mode I, delamination propagation is only significant at node A. The numerical results are in good agreement with those of the analytical solution found in the literature. It is observed that the patch's behavior is strongly dependent on loading, crack size and stacking sequences in terms of reducing the structure's lifespan, especially in the presence of delamination.

The numerical modeling presented by the VCCT approach is highly valuable for studying delamination evolution. The influence of loading, crack size and stacking sequences on repair performance is discussed in this work.

It is generally known that the perforated section such as the castellated section is good to sustain distributed loads but inadequate to sustain highly concentrated loads. Therefore, it is possible to design the opening in a different arrangement of web opening to achieve section efficiency, thus improving the strength and torsional behaviour of the section with web opening. This study aims to focus on the finite element analysis of I-beam with and without openings in steel section dominated to lateral-torsional buckling behaviour.

In this work, the analysis of different sizes, shapes and arrangements of web opening is performed by using LUSAS application to conduct numerical analysis on lateral-torsional buckling behaviour. This involves three diameter sizes of web opening, five types of opening shapes and two criteria of the model.

The section with c-hexagon web opening was placed about 200-mm centre to centre and 100-mm edge distance, contribute to 7.26% increase of buckling capacity. For the section with 150-mm centre to centre and 50-mm edge distance, the occurrence of local buckling contributes to decrease of lateral buckling section capacity to 19.943 kNm, where pure lateral-torsional buckling mostly occurred because of prevented section. Besides that, the web opening diameter was also analysed. The web crippling was observed because of the increase of opening diameter from 0.67 to 0.80 D.

This contributes to a decrease in buckling capacity as figured in the contour of the deformed shape. For Model 1, an increase of buckling capacity (31.46%) is observed when the opening diameter are changed from 0.67 to 0.80 D.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.

This study addresses the reliability of a composite fiber (carbon fibers/epoxy matrix) at microscopic level, with a specific focus on its behavior under compressive stresses. The primary goal is to investigate the factors that influence the reliability of the composite, specifically considering the effects of initial fiber deformation and fiber volume fraction.

The analysis involves a multi-step approach. Initially, micromechanics theory is employed to derive limit state equations that define the stress levels at which the fiber remains within an acceptable range of deformation. To assess the composite's structural reliability, a dedicated code is developed using the Monte Carlo method, incorporating random variables.

Results highlight the significance of initial fiber deformation and volume fraction on the composite's reliability. They indicate that the level of initial deformation of the fibers plays a crucial role in determining the composite reliability. A fiber with 0.5% initial deformation exhibits the ability to endure up to 28% additional stress compared to a fiber with 1% initial deformation. Conversely, a higher fiber volume fraction contributes positively to the composite's reliability. A composite with 60% fiber content and 0.5% initial deformation can support up to 40% additional stress compared to a composite containing 40% fibers with the same deformation.

The study's originality lies in its comprehensive exploration of the factors affecting the reliability of carbon fiber-epoxy matrix composites under compressive stresses. The integration of micromechanics theory and the Monte Carlo method for structural reliability analysis contributes to a thorough understanding of the composite's behavior. The findings shed light on the critical roles played by initial fiber deformation and fiber volume fraction in determining the overall reliability of the composite. Additionally, the study underscores the importance of careful fiber placement during the manufacturing process and emphasizes the role of volume fraction in ensuring the final product's reliability.