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To improve the corrosion resistance of magnesium alloys, the construction of protective coatings is necessary to extend the service life of Mg-based materials.

SiO₂ nanoparticles modified by dodecyltrimethoxysilane (DTMS) were added to the PP and a superhydrophobic Mg(OH)₂/PP-60mSiO₂ composite coating was fabricated on the surface of AZ31 magnesium alloy via the hydrothermal method and subsequently the immersion treatment.

Hydrophilic SiO₂ nanoparticles become hydrophobic after modified by DTMS, showing a higher dispersibility in xylene. By incorporating modified SiO₂ nanoparticles into the composite PP coating, the hydrophobicity of the layer was enhanced, resulting in a contact angle of 166.3° and a sliding angle of 3.4°. It also improved the water repellency and durability of the coating. Furthermore, the intermediate layer of Mg(OH)₂ significantly strengthened the bond between the PP layer and the substrate. The Mg(OH)₂/PP-60mSiO₂ composite coating significantly enhances the corrosion resistance of the magnesium alloy by effectively blocking the infiltration of the corrosion anions during corrosion. The corrosion current density of the Mg(OH)₂/PP-60mSiO₂ composite coating is approximately 8.23 × 10^–9 A·cm^-2, which can achieve a magnitude three times lower than its substrate, making it a promising surface modification for the Mg alloy.

The composite coating effectively and durably enhances the corrosion resistance of magnesium alloys.

Due to the harsh underground environment in coal mining, the surface of hydraulic support columns corrodes severely, resulting in significant economic losses. Therefore, a highly corrosion-resistant coatings is needed to extend the service life of the columns.

This study aims to compare the corrosion resistance of ST-Cr₃C₂-NiCr (sealed treatment Cr₃C₂-NiCr) coatings with industrially applied chromium plating. The corrosion failure mechanism of the coatings was investigated.

The results demonstrated that the ST-Cr₃C₂-NiCr coatings exhibited excellent corrosion resistance. After sealing treatment, the corrosion potential of Cr₃C₂-NiCr coatings was −0.215 V, and the corrosion current density of Cr₃C₂-NiCr coatings was lower than that of the plated parts.

ST-Cr₃C₂-NiCr coatings prepared by supersonic atmospheric plasma spraying could provide excellent corrosion resistance in the coal industry.

The low porosity and the presence of the NiCr phase were crucial factors contributing to the preferable corrosion resistance exhibited by the ST-Cr₃C₂-NiCr coatings. The corrosive process of the coatings involved layer-by-layer delamination of surface oxide film, sub-surface pitting, formation and degradation of sub-surface passive film, as well as severe block-like delamination.

The purpose of this paper is to investigate the effect of laser peening (LP) on mechanical and wear properties of 304 stainless steel sheet.

Three-dimensional morphology, micro-hardness and micro-structure of shocked samples were tested. The wear amount, wear track morphology and wear mechanism were also characterized under dry sliding wear using Al₂O₃ ceramics ball.

The LP treatment generates deformation twins that contribute to the grain refinement and hardness increase. The wear test displays that the wear mechanism of samples is mainly abrasive wear and oxidation wear at 10 N load. While at 30 N, the delamination and adhesion areas of treated sample are reduced visibly compared to untreated ones.

This study specifically investigates the mechanical and wear properties of 304 stainless steel after the direct action of LP on its surface, which shows an effective improvement on the wear resistance. For example, the wear loss of processed sample is reduced by 19% at 30 N, the friction coefficient decreases from 0.4714 to 0.4308 and the groove depth is reduced from 78.1 to 74.4 µm under same condition.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0007/

The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σ_RS and ε_PEEQ that discern varying wear situations, thereby contributing to the enrichment of wear theory. Furthermore, the findings serve as a foundational basis for nondestructive and in situ wear detection methodologies, such as nonlinear ultrasonic detection, known for its sensitivity to σ_RS and ε_PEEQ.

This paper elucidates the wear mechanism through the lens of residual stress (σ_RS) and plastic deformation within distinct fretting regimes, using a two-dimensional cylindrical/flat contact model. It specifically explores the impact of the displacement amplitude and cycles on the distribution of residual stress and equivalent plastic strain (ε_PEEQ) in both gross slip regime and partial slip regimes.

Therefore, when surface observation of wear is challenging, detecting the σ_RS trend at the center/edge, region width and ε_PEEQ distribution, as well as the maximum σ_RS distribution along the depth, proves effective in distinguishing wear situations (partial or gross slip regimes). However, discerning wear situations based on ε_PEEQ along the depth direction remains challenging. Moreover, in the gross slip regime, using σ_RS distribution or ε_PEEQ along the width direction rather than the depth direction can effectively provide feedback on cycles and wear range.

This work introduces a novel perspective for investigating wear theory through the distribution of residual stress (σ_RS) and equivalent plastic strain (ε_PEEQ). It presents a feasible detection theory for wear situations using nondestructive and in situ methods, such as nonlinear ultrasonic detection, which is sensitive to σ_RS and ε_PEEQ.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0005/

The purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.

We propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.

A case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.

Previous research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

This study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.

The proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.

The proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.

This paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

An investigation was conducted into the impact of various process parameters on the surface and subsurface quality of glass-ceramic materials, as well as the mechanism of material removal and crack formation, through the use of ultrasonic-assisted grinding.

A mathematical model of crack propagation in ultrasonic-assisted grinding was established, and the mechanism of crack formation was described through the model. A series of simulations and experiments were conducted to investigate the impact of process parameters on crack depth, surface roughness, and surface topography during ultrasonic-assisted surface and axial grinding. Additionally, the mechanism of crack formation was explored.

During ultrasonic-assisted grinding, the average grinding forces are between 0.4–1.0 N, which is much smaller than that of ordinary grinding (1.0–3.5 N). In surface grinding, the maximum surface stresses between the workpiece and the tool gradually decrease with the tool speed. The surface stresses of the workpiece increase with the grinding depth, and the depth of subsurface cracks increases with the grinding depth. With the increase of the axial grinding speed, the subsurface damage depth increases. The roughness increases from 0.780um/1.433um.

A mathematical model of crack propagation in ultrasonic-assisted grinding was established, and the mechanism of crack formation was described through the model. The deformation involved in the grinding process is large, and the FEM-SPH modeling method is used to solve the problem that the results of the traditional finite element method are not convergent and the calculation efficiency is low.

Selective jet electrodeposition (SJED) is an emerging additive manufacturing (AM) technology for realizing metallic components of nano and micro sizes. The deposited parts on the substrate form metallurgical bonding, so separating them from the substrate is an unsolved issue. Therefore, this paper aims to propose a method for separating the deposited micro parts from a sacrificial substrate. Furthermore, single and multi-bead optimization is performed to fabricate microparts with varying density.

A typical SJED process consists of a nozzle (to establish a column of electrolytes) retrofitted on a machine tool (to provide relative motion between substrate and nozzle) that deposits material atom-by-atom on a conductive substrate.

A comprehensive study of process parameters affecting the layer height, layer width and morphology of the deposited micro-parts has been provided. The uniformity in the deposited parts can be achieved with the help of low applied voltage and high scanning speed. Multi-bead analysis for the flat surface condition is experimentally performed, and the flat surface condition is achieved when the centre distance between two adjacent beads is kept at half of the width of a single bead.

Although several literatures have demonstrated that the SJED process can be used for the fabrication of parts; however, part fabrication through multi-bead optimization is limited. Moreover, the removal of the fabricated part from the substrate is the novelty of the current work.