Chunhui Yang

Metal Additive Manufacturing (MAM) has experienced significant growth in recent years due to its ability to fabricate complex geometries with minimal material waste, compared to traditional manufacturing technologies. It is especially prevalent in the automotive, aerospace, and biomedical industries because of its capabilities and flexibility to accommodate lightweight design, rapid prototyping, and design customisation. However, the quality of additively manufactured metal parts is often compromised by defects such as cracks and distortions, resulting from high residual stresses caused by extreme temperatures and rapid cooling during the additive manufacturing process. These challenges have slowed the widespread adoption of AM for structurally critical components. In addition, qualification and certification processes remain expensive and time-consuming. To address these issues, this study proposes a novel high-accuracy dual-cantilever calibration inherent strain method (ISM) that can anticipate and mitigate potential defects prior to fabrication for the Direct Metal Laser Sintering (DMLS) process of 17 − 4 PH stainless steel. The novel method integrates multiscale modelling and simulations across three length scales including a microscale model to capture the dynamics of the melt pool and thermal gradients, a mesoscale model to evaluate residual stresses and a macroscale model for a distributed orthotropic mechanical calibration to apply for the dual cantilever using an inherent-strain method. The calibrated inherent strain is then used to forecast deformation, providing a practical tool for design engineers to enhance the quality of 3D printed components. Deformation can be minimised by optimising key process parameters. The devise calibration process is validated via conducting the additive manufacturing of a dual-cantilever beam with 17 − 4 PH stainless steel as an efficient and effective approach.

Cemented paste backfill (CPB) is widely used in mining operations to enhance underground stope stability, production, and safety. Accurately predicting paste flow behaviours in backfill reticulation circuits is crucial for efficient delivery control and asset longevity. However, the predictions remain challenging due to complex rheology and flow-induced particle heterogeneities of CPB. This study develops a computational fluid dynamics (CFD)-based analysis framework to investigate flow dynamics of the CPB and the wear conditions of the pipes, considering slip layer and shear-induced particle migration. Experimental loop tests are conducted to measure pressure drops of CPB at different velocities, providing data for validating the developed CFD model. Simulation results are in good agreement with the measured pressure drops and wear rates of the internal pipeline wall. Furthermore, comparisons with existing models indicate that the developed model provides more accurate predictions. Microscopical analyses reveal that shear-induced particle migration leads to the formation of a distinct plug flow region, with particles accumulating near the unyielded boundary. Meanwhile, a low particle concentration near the pipe wall reduces local viscosity and pressure drop. Parametric studies reveal that increased flow velocity and reduced pipe diameter significantly elevate both pressure drop and wear rate, while higher solid concentrations induce nonlinear rheological effects.

This study explores the degradation behaviours and mechanical performance of 3D-printed recycled polycarbonate (rPC) over multiple recycling cycles using fused granulate fabrication (FGF), a sustainable and scalable 3D printing technique

The construction industry, as a major contributor to greenhouse gas emissions, urgently requires sustainable development solutions to achieve the Net Zero Emission Goal. Magnesium oxide (MgO)-based cementitious composites have emerged as promising alternatives due to their ability to reduce environmental impact and their potential to enhance structural integrity. Despite these advantages, limitations such as poor resistance to harsh environmental conditions and concerns over long-term durability continue to restrict their broader application. To better understand these strengths and limitations, this review investigates the influence of MgO; supplementary cementitious materials (SCMs) such as fly ash, silica fume, and rice husk ash. It also examines fibers, including polyethylene (PE), polypropylene (PP), polyvinyl alcohol (PVA), glass, sisal, and cellulose, and their effect on the mechanical and durability properties of MgO-based composites. Mechanical performance is assessed through compressive and tensile strength, while durability is evaluated in terms of porosity, permeability, water absorption, shrinkage (autogenous and drying), and carbonation resistance. Key challenges and future research directions to promote the use of MgO composites in sustainable construction are also identified.

Purpose: This study addresses the gap in existing Industry 4.0 (I4.0) readiness models, which overlook the foundational role of lean manufacturing (LM) in enabling digital technology adoption. It explores how lean capabilities enhance I4.0 readiness, supporting manufacturing firms leveraging their prior investments in LM for digital transformation.Design/methodology/approach: This study adopts a systematic literature review (SLR) of 20 I4.0 readiness models to investigate the extent to which LM concepts are incorporated. To reinforce the analysis, a content analysis was conducted using a comprehensive spreadsheet that captured each model’s dimensions. Key readiness dimensions were synthesised alongside assimilated lean concepts. Based on this analysis, a novel lean-centric I4.0 readiness model was developed, comprising six core dimensions and 39 sub-dimensions, which reflect essential lean capabilities aligned with digital transformation.Findings: The findings reveal a substantial gap in existing I4.0 readiness models in explicitly incorporating core LM concepts. The proposed lean-centric I4.0 readiness model emphasises the need to balance technology, organisational, and process maturity enablers, contrary to models prioritising technological factors alone.Research limitations/implications: The review is based on the published literature on manufacturing where LM practices may have different degrees of adoption in different manufacturing sectors. Also, the review was confined to the literature published in English, which may limit the generalisability to other regions. Future research can validate this model empirically across diverse manufacturing contexts.Practical implications: The novel lean-centric I4.0 readiness model developed in the study enables manufacturing firms to assess readiness through a lean lens. It can also aid their understanding of how their existing LM practices enable digital transformation readiness. The model facilitates strategic decision-making, resource allocation, and priority setting, reducing the risk of digital transformation failures.Originality/value: This is the first study to examine I4.0 readiness models for the extent of integration of LM concepts. The proposed lean-centric I4.0 readiness model with a range of dimensions and sub-dimensions enriches the in-depth integration of LM and I4.0 literature. It offers a foundation for further empirical studies on lean-centric readiness assessment.

While fused filament fabrication (FFF) has gained widespread popularity in additive manufacturing, its prevalent limitation in mechanical properties has prompted researchers to explore innovative solutions, with the creation of nanocomposites emerging as a promising solution. In this study, the effect of multi-walled carbon nanotubes (MWCNTs) on the material properties and morphology of acrylonitrile butadiene styrene (ABS)-based nanocomposites at various MWCNT concentrations of 0.1–1.5% is investigated. A 0.5% MWCNT addition was found to be the optimal content for mechanical, electrical, and thermal properties for FFF-printed specimens printed at longitudinal and transverse build orientations with profound improvement compared to pure ABS. Morphological analysis confirms the significant influence of air voids, low interlayer bonding and the agglomeration of additives on the properties of FFF-printed parts. Then, functionalisation methods are developed in this study for the effective modification of nanoadditives, and their influences on mechanical, electrical and thermal properties of FFF-printed nanocomposite parts are investigated. Both the covalent and non-covalent methods of functionalisation result in a uniform dispersion of nanoadditives with a positive impact on the material properties of those parts, especially for those printed at transverse build orientations.

Fused filament fabrication (FFF), a 3D printing technique, has gained prominence due to its diverse application in rapid prototyping, custom tooling, architectural modelling, and medical device fabrication. The current literature shows extensive works on single-objective optimisation but limitation in studies on multi-objective optimisation that determines a set of process parameters to attain a balance between conflicting properties such as mechanical properties and build time. Therefore, this study experimentally investigates mechanical properties and build time of FFF printed acrylonitrile butadiene styrene (ABS), considering the influences of five key FFF printing process parameters on mechanical properties, including extrusion temperature, layer thickness, printing speed, number of contours, and infill density. Response surface methodology (RSM) and artificial neural network (ANN) are both adopted for pattern recognition before the genetic algorithm (GA) and multi-criteria decision-making (MCDM) algorithm are applied for optimisation. Results reveal that the infill density is the main contributor to tensile strength while the layer thickness has the highest impact on build time. Both RSM-GA and ANN-GA approaches succeed at achieving a balance between tensile strength and build time. In comparison to RSM, ANN proves to be a superior tool with remarkable accuracy in predicting responses across diverse parameter settings. The findings of this study hold significant implications for designers and manufacturers of domestic and small industrial components.