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The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

This paper aims to report the effect of titanium oxide (TiO₂) particles on the specific wear rate (SWR) of alkaline treated bamboo and flax fiber-reinforced composites (FRCs) under dry sliding condition by using a robust statistical method.

In this research, the epoxy/bamboo and epoxy/flax composites filled with 0–8 Wt.% TiO₂ particles have been fabricated using simple hand layup techniques, and wear testing of the composite was done in accordance with the ASTM G99-05 standard. The Taguchi design of experiments (DOE) was used to conduct a statistical analysis of experimental wear results. An analysis of variance (ANOVA) was conducted to identify significant control factors affecting SWR under dry sliding conditions. Taguchi prediction model is also developed to verify the correlation between the test parameters and performance output.

The research study reveals that TiO₂ filler particles in the epoxy/bamboo and epoxy/flax composite will improve the tribological properties of the developed composites. Statistical analysis of SWR concludes that normal load is the most influencing factor, followed by sliding distance, Wt.% TiO2 filler and sliding velocity. ANOVA concludes that normal load has the maximum effect of 31.92% and 35.77% and Wt.% of TiO2 filler has the effect of 17.33% and 16.98%, respectively, on the SWR of bamboo and flax FRCs. A fairly good agreement between the Taguchi predictive model and experimental results is obtained.

This research paper attempts to include both TiO₂ filler and bamboo/flax fibers to develop a novel hybrid composite material. TiO₂ micro and nanoparticles are promising filler materials, it helps to enhance the mechanical and tribological properties of the epoxy composites. Taguchi DOE and ANOVA used for statistical analysis serve as guidelines for academicians and practitioners on how to best optimize the control variable with particular reference to natural FRCs.

This study aims to evaluate in situ oxidative polymerization of aniline (Ani) as a post-processing method to promote extrusion-based 3D printed parts, made from insulating polymers, to components with functional properties, including electrical conductivity and chemical sensitivity.

Extrusion-based 3D printed parts of polyethylene terephthalate modified with glycol (PETG) and polypropylene (PP) were coated in an aqueous acid solution via in situ oxidative polymerization of Ani. First, the feedstocks were characterized. Densely printed samples were then used to assess the adhesion of polyaniline (PAni) and electrical conductivity on printed parts. The best feedstock candidate for PAni coating was selected for further analysis. Last, a Taguchi methodology was used to evaluate the influence of printing parameters on the coating of porous samples. Analysis of variance and Tukey post hoc test were used to identify the best levels for each parameter.

Colorimetry measurements showed significant color shifts in PP samples and no shifts in PETG samples upon pullout testing. The incorporation of PAni content and electrical conductivity were, respectively, 41% and 571% higher for PETG in comparison to PP. Upon coating, the surface energy of both materials decreased. Additionally, the dynamic mechanical analysis test showed minimal influence of PAni over the dynamic mechanical properties of PETG. The parametric study indicated that only layer thickness and infill pattern had a significant influence on PAni incorporation and electrical conductivity of coated porous samples.

Current literature reports difficulties in incorporating PAni without affecting dimensional precision and feedstock stability. In situ, oxidative polymerization of Ani could overcome these limitations. However, its use as a functional post-processing of extrusion-based printed parts is a novelty.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

The aim of this study is to investigate the printing parameters of fused deposition modeling (FDM), a material extrusion-based method, and to examine the mechanical and thermal properties of their polylactic acid (PLA) components reinforced with copper, bronze, and carbon fiber micro particles.

Tensile test samples were created by extruding composite filament materials using FDM-based 3D printer. Taguchi method was used to design experiments where layer thickness, infill density, and nozzle temperature were the printing variables. Analysis of variance (ANOVA) was applied to determine the effect of these variables on tensile strength.

The results of this study showed that the reinforcement of metal particles in PLA material reduces strength and increases elongation. The highest tensile strength was obtained when the layer thickness, infill density, and nozzle temperature were set to 100 µm, 60%, and 230 °C, respectively. As a result of thermal analysis, cooper-PLA showed the highest thermal resistance among metal-based PLA samples.

It is very important to examine the mechanical and thermal quality of parts fabricated in FDM with metal-PLA composites. In the literature, the mechanical properties of metal-reinforced composite PLA parts have been examined using different factors and levels. However, the fabrication of parts using the FDM method with four different metal-added PLA materials has not been examined before. Another unique aspect of the study is that both mechanical and thermal properties of composite materials will be examined.

The current analysis was conducted to investigate the quality of surfaces and geometry of tracks printed using PolyMideTM CoPA, PolymaxTM PC and PolyMideTM PA6-CF materials through fused deposition modelling (FDM). This study also examined the degree of fusion of adjacent filaments (tracks) to approximate the optimal process parameters of the three materials.

Images of fused adjacent filaments were acquired using scanning electron microscopy (SEM), after which, they were analysed using Image J Software and Minitab Software to determine the optimal process parameters.

The optimal process parameters for PolyMideTM CoPA are 0.25 mm, 40 mm/s, −0.10 mm, 255°C and 0.50 mm for layer thickness, printing speed, hatch spacing, extrusion temperature and extrusion width, respectively. It was also concluded that the optimal process parameters for PolymaxTM PC are 0.30 mm, 40 mm/s, 0.00 mm, 260°C and 0.6 mm for layer thickness, printing speed, hatch spacing, extrusion temperature and extrusion width, respectively.

It was difficult to separate tracks printed using PolyMideTM PA6-CF from the support structure, making it impossible to examine and determine their degree of fusion using SEM.

The study provides more knowledge on FDM, which is one of the leading additive manufacturing technology for polymers. The information provided in this study helps in continued uptake of the technique, which can help create job opportunities, especially among the youth and young engineers.

This study proposes a new and a more accurate method for optimising process parameters of FDM at meso-scale level.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.

The purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.

A Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.

The proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.

The model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.

Mamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

Metro stations have become a crucial aspect of urban rail transportation, integrating facilities, equipment and pedestrians. Impractical physical layout designs and pedestrian psychology impact the effectiveness of an evacuation during a metro fire. Prior research on emergency evacuation has overlooked the complexity of metro stations and failed to adequately consider the physical heterogeneity of stations and pedestrian psychology. Therefore, this study aims to develop a comprehensive evacuation optimization strategy for metro stations by applying the concept of design for safety (DFS) to an emergency evacuation. This approach offers novel insights into the management of complex systems in metro stations during emergencies.

Physical and social factors affecting evacuations are identified. Moreover, the social force model (SFM) is modified by combining the fire dynamics model (FDM) and considering pedestrians' impatience and panic psychology. Based on the Nanjing South Metro Station, a multiagent-based simulation (MABS) model is developed. Finally, based on DFS, optimization strategies for metro stations are suggested.

The most effective evacuation occurs when the width of the stairs is 3 meters and the transfer corridor is 14 meters. Additionally, a luggage disposal area should be set up. The exit strategy of the fewest evacuees is better than the nearest-exit strategy, and the staff in the metro station should guide pedestrians correctly.

Previous studies rarely consider metro stations as sociotechnical systems or apply DFS to proactively reduce evacuation risks. This study provides a new perspective on the evacuation framework of metro stations, which can guide the designers and managers of metro stations.