Identifying and Prioritising Future Robot Control Research with Multi-Criteria Decision-Making

The gap between researchers who carry out scientific exploration and practitioners who can make use of the research results is well known. In addition, while practitioners place a high value on research, they do not read many research papers. This paper attempts to define and prioritise future research in robotics using the analytical hierarchy process (AHP). Fifteen research alternatives and gaps, five performance criteria, eight industry types, and six production processes, investigated by both academics and practitioners, are filtered to six alternatives, four performance criteria, three industry types, and three production processes, respectively, based on the most important factors in decision-making. Subsequently, they are analysed by the Expert Choice software. This research aims at bridging the gap between academics and practitioners in robotics research and at conducting research that is relevant to industry. The results indicate that the research in multi-robot control ranked first with 26.8%, followed by the research in safe control with 23.3% and the research in remote robot supervision with 19.0%. The research in force control ranked fourth with 17.8%, followed by the research in 3D vision and wireless communication with 8.4% and 6.4%, respectively. Based on the results, the academics involved in robotics research should direct their effort to the research activities that received the highest priority in the AHP model

Artificial neural network approach for the prediction of wear for Al6061 with reinforcements

In the prospect of finding a lightweight and wear-resistant materials, researchers have considered aluminium-based metal matrix composites (MMC), as aluminium has a wide variety of applications but possesses low wear resistance properties. To enhance the wear resistance of aluminium alloys, ceramic particles are reinforced. In this endeavour, commercially available aluminium alloy is reinforced with 2, 4 and 6 wt% of silicon carbide (SiC) and Vanadium pentoxide (V _2 O _5 ) powder to improve its wear resistance. The intensity of reinforcement in the matrix was uniform, and the Scanning Electron Microscope image showed the grain refinement and grain boundary of the MMC’s. Wear tests were performed for L16 array set, uncertainty analysis of wear measurement is evaluated, and data were used to develop Artificial Neural Network (ANN) model. The efficient ANN model with a regression coefficient of 0.999 was used to make predictions for remaining sets. Experimental and predicted wear results were analysed; it is observed that higher wt% reinforcement of V _2 O _5 increased wear resistance of aluminium compared to SiC. The methodology adapted using ANN for prediction of wear using meagre experimentation, will lay a path for tribologists to predict the wear of novel metal matrix composites in their endeavour of finding wear-resistant materials

Influence of Pilot-Fueling and Nozzle-Opening Pressure on Performance and Tailpipe Emissions of WCO Biodiesel in a CRDi Engine

Pilot-fueling and nozzle-injection pressure are significant injection parameters, and they have significant impacts on modern vehicles for enhancing the engine output, in addition to meeting rigorous tailpipe-exhaust emission standards. In this current work, the influence of the pilot-fueling pressure and nozzle-opening pressure (NOP) on the engine performance and tailpipe outcomes from a compression-ignition (CI) engine at a higher injection pressure and varying load conditions was investigated using a waste cooking oil (WCO) biodiesel (B20). The experiments were executed in a high-pressure CRDi-fitted diesel engine at the start of pilot fueling (SOPF) (timing: 23° bTDC), and at the start of the main fueling (SOMF) (timing: 33° bTDC). The results showed that the combined influence of the pilot-fueling and nozzle-opening pressure induced a remarkable enhancement in the BTE, by 25.13%, and the BSFC decreased by 13.88%, compared with diesel at 10% pilot fueling. Carbon monoxide, hydrocarbon, and smoke emissions were drastically reduced for the higher pilot-fueling quantity by 21.05%, 16.66%, and 33.10%, respectively, compared with the diesel at 10% pilot fueling. With the implementation of the pilot-fueling strategy, there is no effect on the NOx reduction

Influence of Pilot-Fueling and Nozzle-Opening Pressure on Performance and Tailpipe Emissions of WCO Biodiesel in a CRDi Engine

Pilot-fueling and nozzle-injection pressure are significant injection parameters, and they have significant impacts on modern vehicles for enhancing the engine output, in addition to meeting rigorous tailpipe-exhaust emission standards. In this current work, the influence of the pilot-fueling pressure and nozzle-opening pressure (NOP) on the engine performance and tailpipe outcomes from a compression-ignition (CI) engine at a higher injection pressure and varying load conditions was investigated using a waste cooking oil (WCO) biodiesel (B20). The experiments were executed in a high-pressure CRDi-fitted diesel engine at the start of pilot fueling (SOPF) (timing: 23° bTDC), and at the start of the main fueling (SOMF) (timing: 33° bTDC). The results showed that the combined influence of the pilot-fueling and nozzle-opening pressure induced a remarkable enhancement in the BTE, by 25.13%, and the BSFC decreased by 13.88%, compared with diesel at 10% pilot fueling. Carbon monoxide, hydrocarbon, and smoke emissions were drastically reduced for the higher pilot-fueling quantity by 21.05%, 16.66%, and 33.10%, respectively, compared with the diesel at 10% pilot fueling. With the implementation of the pilot-fueling strategy, there is no effect on the NOx reduction

Assessment of Destructive and Nondestructive Analysis for GGBS Based Geopolymer Concrete and Its Statistical Analysis

Geopolymer is the alternative to current construction material trends. In this paper, an attempt is made to produce a sustainable construction composite material using geopolymer. Ground granulated blast furnace slag (GGBS)-based geopolymer concrete was prepared and tested for different alkaline to binder ratios (A/B). The effect of various temperatures on compressive strength properties was assessed. The cubes were exposed to temperature ranging from 50 to 70 °C for a duration ranging from 2 to 10 h, and the compressive strength of the specimens was analyzed for destructive and non-destructive analysis and tested for 7, 28, and 90 days. The obtained compressive strength (CS) results were analyzed employing the probability plot (PP) curve, distribution overview curve (DOC), probability density function (PDF), Weibull, survival, and hazard function curve. Maximum compressive strength was achieved for the temperature of 70 °C and an A/B of 0.45 for destructive tests and non-destructive tests with 44.6 MPa and 43.56 MPa, respectively, on 90 days of testing. The survival and hazard function curves showed incremental distribution characteristics for 28 and 90 days of testing results with a probability factor ranging from 0.8 to 1.0

Optimization of Alkaline Activator on the Strength Properties of Geopolymer Concrete

This study investigates the effects of red mud on the performance of geopolymer concrete in regard to fresh and mechanical properties. Red mud was used as a binder, and GGBS replaced the binder. Different proportions of red mud ranging from 0 to 30% with an interval of 2% and activator agents such as KOH and K2SiO3 for various alkaline-to-binder ratios such as 0.30, 0.40, and 0.50 were used; their effect on the fresh and mechanical properties of geopolymer concrete were the focusing parameter on the current study. Fresh properties such as setting time, slump, compaction factor, and vee-bee consistometer test, and mechanical properties such as compressive strength, split tensile strength, flexural strength, modulus of elasticity, and impact energy were studied. ANOVA and radar plot analysis were studied for various alkaline to binder (A/B) compressive strength results tested for 7 to 90 days. The increase of red mud quantity caused the decline of workability, but there was continuous enhancement of mechanical properties of GPC up to a specific limit. An alkaline-to-binder ratio of 0.4 shows excellent results compared with other ratios at ambient conditions for strength properties. ANOVA and radar plot reveal that A/B of 0.40 for 90 days shows excellent results compared with other ratios, and CS values vary in a linear manner

Machine Learning Prediction and Optimization of Performance and Emissions Characteristics of IC Engine

In this work, a study was conducted to investigate the effects of different biodiesel blends with hydrogen peroxide additive on the performance and emissions of an internal combustion engine under various operating parameters. A CI engine was operated with diesel, four dissimilar biodiesels, and H2O2 at various proportions. The biodiesel blends used were Jatropha (D60JB30A10, D60JB34A6, D60JB38A2, D60JB40), Honge (D60HB30A10, D60HB34A6, D60HB38A2, D60HB40), Simarouba (D60SB30A10, D60SB34A6, D60SB38A2, D60SB40), and Neem (D60NB30A10, D60NB34A6, D60NB38A2, D60NB40). The engine was tested at different injection operating pressures (200, 205, and 210 bar), a speed of 1500 rpm, and a CR of 17.5:1. From the experiments conducted, it was highlighted that, under specific conditions, i.e., with an injection pressure of 205 bar, 80% load, a compression ratio of 17.5, an injection timing set at 230 before top dead center, and an engine speed of 1500 rpm, the biodiesel blends D60JB30A10, D60HB30A10, D60SB30A10, and D60NB30A10 achieved the highest brake thermal efficiencies of 24%, 23.9675%, 23.935%, and 23.9025%, respectively. Notably, the blend D60JB30A10 stood out with the highest brake thermal efficiency of 24% among these tested blends. Similarly, when evaluating emissions under the same operational conditions, the D60JB30A10 blend exhibited the lowest emissions levels: CO (0.16% Vol), CO2 (7.8% Vol), HC (59 PPM), and Smoke (60 HSU), while NOx (720 PPM) emissions showed a relative increase with higher concentrations of the hydrogen-based additive. The D60HB30A10, D60SB30A10, and D60NB30A10 blends showed higher emissions in comparison. Additionally, the study suggests that machine learning techniques can be employed to predict engine performance and emission characteristics, thereby cutting down on time and costs associated with traditional engine trials. Specifically, machine learning methods, like XG Boost, random forest regressor, decision tree regressor, and linear regression, were utilized for prediction purposes. Among these techniques, the XG Boost model demonstrated highly accurate predictions, followed by the random forest regressor, decision tree regressor, and linear regression models. The accuracy of the predictions for XG Boost model was assessed through evaluation metrics such as R2-Score (0.999), Root Mean Squared Error (0.540), Mean Squared Error (0.248), and Mean Absolute Error (0.292), which allowed for a thorough analysis of the algorithm’s performance compared to actual values