•Precision plastic biaspheric lens having high and uneven thickness was developed using injection molding process.•A hybrid ANN and PSO technique was employed to predict the optimal process ...parameters.•Mould with aspheric profiles was prepared using single point diamond turning machine.•Surface characterisation like form, waveiness and surface roughness of mould and moulded bi-aspheric lens are measured.•Aberrations in the injection molded lens were studied using Shack-Hartmann Wavefront Sensor.
Injection molding of bi-aspheric lens using polycarbonate material with minimum variation in volumetric shrinkage is crucial for optical quality and is more challenging task among the researchers. In this paper, a hybrid artificial neural networks (ANN) and particle swarm optimization (PSO) technique is used to predict the optimal process parameters of injection molding process of the bi-aspheric lens. The developed ANN network (7-13-6) was trained as well as tested with experimental data sampled from statistical methods. The well trained and tested ANN network was coupled with improved PSO algorithm as a hybrid ANN-PSO to optimize the injection molding process parameters. The optimized injection molding process parameters obtained from hybrid ANN-PSO algorithm are validated with experiments using J. S. W injection molding machine. It is observed from the lens quality parameters that the proposed hybrid ANN-PSO method optimized the injection molding process of the bi-aspheric lens with an optical power of 27.73 Diopter and the lens posses seventh order spherical aberrations.
AbstractThe combine harvester, a versatile machine for harvesting various crops, has evolved to address specific challenges. In India, wheel-type harvesters were once common but struggled in muddy ...conditions, leading to the rise of track-type harvesters with better traction and maneuverability. However, these new machines faced breakdowns on uneven terrain, disrupting harvest schedules and causing financial losses. To tackle this, a solution was proposed: an adjustable chassis for track-type harvesters. This innovative design aimed to lift and level the chassis, enhancing stability and performance. Four hydraulic cylinders were installed between the chassis frame and walking device to achieve this. Mathematical models were developed to guide the mechanism’s design, ensuring optimal height and transverse tilt adjustments. Hydraulic components were carefully selected based on force calculations, with a MATLAB Simulink circuit designed for performance analysis.A virtual model of the adjustable chassis was created using CATIA software and imported into ADAMS simulation software to assess its attitude. Numerical validation in ANSYS software confirmed the design’s safety and feasibility. The height adjustment range was 0–245 mm, and the transverse tilt adjustment range was 0 ± 15.2° for a piston extension length of 0–100 mm. During simulation, the maximum pressure exerted by the pump was 100 bar, with hydraulic cylinders exerting a maximum force of 43.2 kN over 40 s. ADAMS simulation revealed maximum height adjustment and transverse tilt of 243 mm and 15.2°, respectively, under specific driving parameters.The designed lifting chassis exhibited a maximum displacement of 2.7 mm and a maximum stress of 394 MPa, with a safety factor of 1.94, indicating suitability for real-world applications. This solution promises to improve the efficiency and reliability of track-type combine harvesters, mitigating breakdowns and optimizing harvest operations.
The growing intensive aquaculture system around the world maintains a high stocking density, wherein it is essential to increase and sustain the optimum dissolved oxygen concentration (DO) through ...the provision of artificial aeration systems. The selection of an aerator is a crucial aspect of aquaculture operations. The selected aerator must be economically efficient and should be able to fulfill the requirement of oxygen supply in the pond water. The present study provides an extensive literature review on the importance of artificial aeration in aquaculture, the standard method of test for performance evaluation of an aerator, various aeration systems and their mechanisms, method to determine the numbers of aerator requirement, comparative studies of different type of aerators, and economic consideration in selection of aerators. In addition, a thorough analysis has been done to suggest the type of aerator that is economically viable and efficient for different pond volumes based on the performance data reported in the reviews. Therefore, this study may help the end-users (fish farmers) to select the best aerator based on their requirements.
An artificial neural network (ANN) based program coupled with genetic algorithm (GA) was developed on MATLAB platform for predicting the optimized process parameters required for reducing high free ...fatty acids (FFA) of any vegetable oils for successful transesterification. The developed ANN was a feed forward back propagation network (4-7-13-1) with one input, two hidden and one output layers. The input parameters for the ANN to generalize the pretreatment process were initial acid value of vegetable oil (IAV), methanol-to-oil ratio (
M), catalyst concentration (
C) and reaction time (
T) and the output parameter was final acid value (FAV) of oil. The developed ANN was trained with the experimental data obtained for jatropha, mahua, simaruoba and rice bran oils with acid value more than 14
mg KOH/g-oil. The trained ANN was tested with separate set of data generated from pretreatment of mahua oil using response surface methodology (RSM) based on central composite rotatable design (CCRD) and found to predict the input pretreatment process parameters with low mean square error (MSE) and relative percent deviation (RPD). The well trained ANN synaptic joint weights and threshold values were used by GA to evaluate the fitness (to get FAV of oil less than 2 after pretreatment) of individuals (combinations of
M,
C and
T) for optimization. The optimized process parameters predicted by the developed ANN–GA technique for sunflower oil with IAV 28
±
1
mg KOH/g-oil were experimentally verified and the FAV was measured to be 2
±
0.2
mg KOH/g-oil against the predicted value of 2
mg KOH/g-oil.
Hand-pushed cone penetrometer has been utilized to assess soil compaction based on soil penetration resistance (SPR). However, the manual operation of these devices can be quite challenging and ...erroneous. The accuracy of the data gathered using such devices can be hindered by the operator’s limitation in maintaining a constant-rate of penetration into the soil, thus preventing the attainment of a steady, predefined penetration rate. To address this issue, a constant-rate, IoT-enabled cone penetrometer is designed and constructed. The developed penetrometer measures the SPR across the soil profile with a designed lead screw arrangement that ensures a constant-rate penetration. The device comprises a mechanical system and an electronic control unit that utilizes IoT framework. The mechanical system includes a primary frame, lead screw arrangement and a standard penetrometer probe. The SPR, and depth along with coordinates of location are measured and stored on a web server. A user-friendly customizable web interface and mobile application enable real-time access. The developed penetrometer measures the SPR up to 2000 kPa up to 400 mm depth. Comparative analysis shows that the constant-rate penetrometer (CRP) provides more reliable and less variable resistance measurements compared to the hand-pushed penetrometer (HPP), with significant differences observed at multiple depths. This penetrometer maintains a constant-rate of penetration, ensuring adherence to predefined standards for accurate measurements.
Tractor engine performance is critical for optimizing energy efficiency in tillage operations across various soil conditions. This study introduces an artificial neural network (ANN) based novel ...approach for predicting and optimizing specific fuel consumption (SFC) and fuel consumption per tilled area (FCA), during tillage operations. Field tests were conducted to collect the data on rear axle torque, forward velocity, fuel consumption, throttle position, engine speed, and depth of operation under soft, medium, and hard soil conditions while using a reversible MB plough, offset disc harrow, and rigid tyne cultivator. Tractor engine performance was predicted using ANN and optimized by matching the required rear axle power with the available rear axle power. Optimal operating parameters for three distinct tillage implements, each associated with varying soil types and tillage depths, were determined based on minimizing SFC and FCA. An integrated ANN-based user advisory system was developed to provide tractor engine performance predictions and recommendations for optimal operating parameters to maximize fuel efficiency. The recommendations were validated with actual field data, resulting in Mean Absolute Percentage Errors of 7.56% and 8.98% for MB plough, 11.19% and 9.36% for offset disc Harrow, and 10.90% and 10.57% for cultivator, respectively, for SFC and FCA.Highlights ANN-based novel approach is proposed to optimize fuel efficiency during tillageAdvisory system is developed to recommend parameters for optimum tillageSuggestions for minimum SFC and FCA are provided in soft, medium and hard soilThe accuracy and reliability of advisory system are proven by field validation
•Developed sensor-based automatic watering unit irrigates the paddy seedling trays in nurseries.•Optimal parameters were found to be a spray pressure of 4.5 kg/cm², a spray height of 10 cm, and a ...conveyor speed of 0.14 m/s using the RSM model.•The unit achieved a water spray requirement of 1.31 L/tray and a spray uniformity of 92.41 %, with low power consumption (41 W) and high capacity (780 trays/h).•It saves greater than 65 % of irrigation water compared to traditional methods and reduces watering costs by 63.7 %, making it efficient and cost-effective.•It offers consistent and uniform watering in trays, promoting uniform germination, crop growth, and establishment.
A sensor-based automatic watering unit was developed to irrigate the paddy seedling trays. The watering unit comprised six flat spray nozzles with a direct current (DC) water pump and flow sensor, and these were kept at the top of the conveyor frame, which was actuated by a DC water pump with a solenoid valve to drop the water in trays. The actuation of the DC solenoid valve was controlled by a limit switch fitted to the conveyor frame, which sensed the moving trays on the conveyor. The performance of the developed unit was evaluated, and process optimization was performed using the Inscribed Central Composite Design in Response Surface Methodology. The effect of main operating parameters- chain conveyor speed (0.123, 0.196, and 0.27 m/s), spray operating pressure (1.5, 3.0, and 4.5 kg/cm2), and spray height (10,30 and 50 cm) on the performance of the sensor-based automatic watering unit was studied in terms of the amount of water spray requirement and coefficient of water spray uniformity in trays. The responses of the unit at its optimal parameter operation (spray operating pressure 4.5 kg/cm2, spray height 10 cm, and chain conveyor speed 0.14 m/s) were found to be 1.31 L/tray as the amount of water spray requirement and 92.41 % as the coefficient of water spray uniformity of the unit. Hence, it can be an efficient substitute with significantly less power consumption 41 W with a higher working capacity of 780 trays/h for watering into the paddy tray nursery. The developed sensor-based automatic smart watering unit saves irrigation water per square area of 68.90 %, 76.13 %, and 82.31 % compared to existing drip, sprinkler, and flood irrigation methods in mat-type paddy nurseries. The developed sensor-based automatic smart watering unit saves the watering cost INR 11,567.16 (140.02 USD) per hectare, reducing cost by 63.7 % compared to manual watering in the mat-type nursery preparation. Thus, an automatic smart watering unit in a paddy tray nursery offers consistent and uniform watering from an Indian perspective, promoting uniform germination, crop growth, and establishment. It conserves water by ensuring the right amount of water is applied at the right time, avoiding overwatering or under-watering.
AbstractThe fuel consumption model serves as a valuable tool for estimating real-time fuel consumption levels. An accurate fuel consumption model is crucial in providing precise information regarding ...fuel utilisation and motivating experts to assess the vehicle’s economy. The goal of this study was to develop a novel approach by eliminating the measurement of real-time tractor PTO (power take-off) power for fuel consumption prediction and to develop a cloud-infused, server-less, machine learning (ML) based real-time generalised tractor fuel consumption prediction model for any tractor between the power range of 8–48 hp. The fuel consumption prediction models from 18 Machine Learning algorithms were developed, and the extensive data analysis with hyperparameter tuning concluded that the Gradient Boosting Regressor Machine Learning model outperformed the other Machine Learning models with reasonable accuracy (R2 = 0.999 for training and 0.914 for testing). Cloud-based serverless Web App and Android App integrated with the Gradient Boosting Regressor based fuel consumption prediction model were developed for the real-time fuel consumption prediction and monitoring of a tractor during field operations. The developed Machine Learning model predicted fuel consumption with a Mean Absolute Percentage Error of 11.43% during real-time field experiments with Mouldboard plough operation. The field validation showed the generalisation ability and efficacy of the developed model, and it can be implemented as a user advisory system for real-time energy-efficient agricultural operations.
Uniform seed distribution within the row is the prime objective of precision planters for better crop growth and yield. Inclined plate planters are generally used for sowing bold seeds like maize, ...groundnut, chickpea, and their operating parameters like the forward speed of operation, the seed metering plate inclination, and the seed level in the hopper affect the cell fill and subsequently the uniform seed distribution. Therefore, to achieve precise seed distribution, these parameters need to be optimized. In the present study, out of the different optimization techniques, a new intelligent optimization technique based on the integrated ANN-PSO approach has been used to achieve the set goal. A 3–5-1 artificial neural network (ANN) model was developed for predicting the cell fill of inclined plate seed metering device, and the particle swarm optimization (PSO) algorithm was applied to obtain the optimum values of the operating parameters corresponding to 100% cell fill. The most appropriate optimal values of the forward speed of operation, the seed metering plate inclination, and the seed level in the hopper for achieving 100% cell fill were found to be 3 km/h, 50-degree, and 75% of total height, respectively. The proposed integrated ANN-PSO approach was capable of predicting the optimal values of operating parameters with a maximum deviation of 2% compared to the experimental results, thus confirmed the reliability of the proposed optimization technique.
•ANN model was developed to predict cell fill of inclined plate seed metering device.•PSO was used to obtain optimal values of seed metering device's operating parameters.•ANN-PSO algorithm is feasible to determine optimal values of operating parameters.
•A perforated pooled circular stepped cascade (PPCSC) aerator was developed with maximum aeration efficiency using hybrid ANN-PSO technique.•A 3–6-1 ANN model coupled with particle swarm optimization ...(PSO) technique was used for optimizing the geometric and dynamic parameters of PPCSC aerator.•The optimized values of the consecutive step width ratio, the ratio of perforation diameter to the bottom-most radius, and the water flow rate were found to be 1.15, 0.0027 and 0.0167 m3/s, respectively.•The developed PPCSC aerator showed a 25.79% and 68.89% increase in the SAE as compared to the PCSC and CSC aerators, respectively.
Artificial aeration system for aquaculture ponds becomes essential to meet the oxygen requirement posed by the aquatic species. The performance of an aerator is generally measured in terms of standard aeration efficiency (SAE), which is significantly affected by the different geometric and dynamic parameters of the aerator. Therefore, to enhance the aeration performance of an aerator, these parameters need to be optimized. In the present study, a perforated pooled circular stepped cascade (PPCSC) aerator was developed, and the geometric and dynamic parameters of the developed aerator were optimized using the hybrid ANN-PSO technique for maximizing its aeration efficiency. The geometric parameters include consecutive step width ratio (Wi-1/Wi) and the perforation diameter to the bottom-most radius ratio (d/Rb), whereas the dynamic parameter includes the water flow rate (Q). A 3–6-1 ANN model coupled with particle swarm optimization (PSO) approach was used to obtain the optimum values of geometric and dynamic parameters corresponding to the maximum SAE. The optimal values of the consecutive step width ratio (Wi-1/Wi), the perforation diameter to the bottom-most radius ratio (d/Rb), and the water flow rate (Q) for maximizing the SAE were found to be 1.15, 0.002 7 and 0.016 7 m3/s, respectively. The cross-validation results showed a deviation of 3.07 % between the predicted and experimental SAE values, thus confirming the adequacy of the proposed hybrid ANN-PSO technique.
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