The search for suitable manufacturing methods and the selection of biocompatible material with good mechanical properties is still a major challenge in implant development. polyethylene terephthalate glycol (PETG) is a thermoplastic extensively utilized in biomedical applications, like tissue engineering, dental, scaffolds and surgery, because of its biocompatibility. Fused deposition modeling (FDM) is gaining importance in wide range of applications for developing custom shaped medical implants. This study aimed to fabricate a cranial implant using the optimized parameters of 3D printed PETG for good mechanical properties. The research investigates the optimization of key printing parameters like layer height, line width and print speed for PETG material by utilizing Box Behnken Design (BBD). Analysis suggests that the influential parameters of FDM are layer height and line width, which significantly influence tensile and compressive strength. The analysis of variance (ANOVA) showed that a layer height of 0.12 mm, line width of 0.77 mm and print speed of 25.75 mm/s indicated the increased value of tensile and compressive strength, i.e., 51.18 MPa and 52.33 MPa, respectively. The effectiveness of the RSM model was confirmed using the validation experiment, with errors less than 2%. Additionally, this study presents the process framework for the development of customized cranial implants by using computed tomography (CT) scan data of the patient. The 3D printed implant tested under uniaxial compressive load shows an average peak value of 1088 N. The goal of this research is to assist surgeons in overcoming clinical challenges faced while selecting materials and in-house production of patient-specific implants. A further evaluation of the presented technology is recommended for its potential use in clinical trials.

Conventional trajectory planning methods for robotic fruit harvesting mainly rely on static geometric heuristics and often overlook critical sensory and task-specific variables such as fruit morphology and end-effector compatibility. These limitations make traditional approaches less effective in real-world agricultural settings, where conditions are unpredictable and fruits require careful, adaptive handling. Moreover, most existing studies do not incorporate a Convolutional Neural Network (CNN) to detect confidence in the planning process, often treating perception and motion planning as isolated components rather than a unified system. To overcome these challenges, this study proposes a data-driven approach to trajectory optimization that integrates visual perception based on CNN confidence levels, gripper type with different actuation technologies, and fruit orientation, parameters that significantly influence harvesting efficiency. Two multivariate regression models were developed, one specifically for firm fruits such as oranges and the other for soft fruits such as strawberries. The models predict trajectory length using three input variables: CNN detection confidence, actuator type, which includes three-finger and two-finger grippers, and fruit orientation angles ranging from 50 degrees-130 degrees. The non-linear influence of orientation is captured through polynomial terms. A total of 46 experimental trials were conducted for each fruit type using a robotic platform under controlled conditions. The regression outputs revealed that CNN confidence had a strong influence on trajectory length reduction, while orientation had a more severe impact on strawberries due to their delicate structure. In comparison to baseline trajectories, the optimized A* planner, guided by regression coefficients, curtailed trajectory lengths by 11% for strawberries and 14% for oranges. Moreover, the positional accuracy incre ased by 15% and 12%, respectively. The higher predictive accuracy was attained by the models (R2= 0.89 and 0.82; RMSE = 3.2 cm and 4.7 cm for strawberries and oranges, respectively). These results demonstrate that heuristic planning, combined with statistical modeling, enhances motion reliability and spatial efficiency in autonomous fruit picking.

Reducing damage rates is paramount for optimizing the efficiency of fruit harvesting robots and advancing their journey towards commercial viability. Despite the crucial role that damage rates play in determining fruit quality and marketability, there is a notable lack of comprehensive and in-depth studies analyzing this aspect, especially within the context of fruit harvesting robots. Most research tends to prioritize metrics such as success rate and accuracy of fruit picking, leaving the examination of damage rates relatively overlooked. This study fills this gap by conducting a thorough examination of the factors contributing to damage rates in fruit harvesting robots, including the causes of damage, the types and sizes of bruises incurred, and the impact of occlusion, illumination conditions, and end effector orientation. Additionally, the research investigates strategies for minimizing damage rates, offering insights into optimizing fruit harvesting techniques to reduce potential damage. Occlusion, illumination, and gripper angle were found to significantly influence fruit damage. Specifically, a 10 % increase in occlusion raised damage by 1.18 %, a 100 Lumen/m2 increase in illumination reduced damage by 10.5 %, and deviation from the optimal 90 degrees gripper angle increased damage by 1.8 % per 10 degrees shift. Overall, proper fruit orientation reduced damage by 40 %, minimal occlusion by 36 %, and optimal illumination by 25 %. A multiple linear regression model explained the variance in damage rate (R2 = 0.924) and achieved a low RMSE of 1.85 %, demonstrating high predictive accuracy and validating the model's reliability in quantifying the influence of harvesting parameters. By investigating these aspects and exploring strategies for minimizing damage, the study aims to advance fruit harvesting robotics and contribute to the successful commercialization of this technology.

Human comfort and safety are the most important criterion in the manufacturing of an automobile, for this reason, every manufacturing industry assures the reliability and quality of components utilized in the automobile industry. Air Conditioner (A/C) is an essential part of an automobile that significantly contributes to human comfort and safety. It is essential to remove the failures in the manufacturing of A/C to enhance quality and reliability. In this research, a fuzzy failure mode and effects analysis (Fuzzy-FMEA) technique has been established to analyze and eradicate the risks of 16 possible failures in suction hose manufacturing of automobile A/C. It starts from defining, categorizing, and evaluating all risk failures and then ranking them by assigning fuzzy linguistic variables by the team of experts. To validate the proposed technique, the air conditioner suction hose manufacturing process for automobiles is considered as a case study. The highest value of RPN was obtained for the multiple failure modes F1 (Fluxing on the outer surface of the flange), F2 (Coating having dust particles), F6 (Weak Sleeve locking), and F7 (hard locking and clamping dies) using conventional FMEA technique. The highest values of RPN were obtained for the potential failure modes F5 (coating length less than standard), F1, and F3 using Fussy-FMEA. The results show that this Fuzzy-FMEA technique is effective and reasonable to control quality and enhance productivity and reliability.

The Aluminum alloy AA7075 workpiece material is observed under dry finishing turning operation. This work is an investigation reporting promising potential of deep adaptive learning enhanced artificial intelligence process models for L18 (6133) Taguchi orthogonal array experiments and major cost saving potential in machining process optimization. Six different tool inserts are used as categorical parameter along with three continuous operational parameters i.e., depth of cut, feed rate and cutting speed to study the effect of these parameters on workpiece surface roughness and tool life. The data obtained from special L18 (6133) orthogonal array experimental design in dry finishing turning process is used to train AI models. Multi-layer perceptron based artificial neural networks (MLP-ANNs), support vector machines (SVMs) and decision trees are compared for better understanding ability of low resolution experimental design. The AI models can be used with low resolution experimental design to obtain causal relationships between input and output variables. The best performing operational input ranges are identified for output parameters. AI-response surfaces indicate different tool life behavior for alloy based coated tool inserts and non-alloy based coated tool inserts. The AI-Taguchi hybrid modelling and optimization technique helped in achieving 26% of experimental savings (obtaining causal relation with 26% less number of experiments) compared to conventional Taguchi design combined with two screened factors three levels full factorial experimentation.

Using modern machines like robots comes with its set of challenges when encountered with unstructured scenarios like occlusion, shadows, poor illumination, and other environmental factors. Hence, it is essential to consider these factors while designing harvesting robots. Fruit harvesting robots are modern automatic machines that have the ability to improve productivity and replace labor for repetitive and laborious harvesting tasks. Therefore, the aim of this paper is to design an improved orange-harvesting robot for a real-time unstructured environment of orchards, mainly focusing on improved efficiency in occlusion and varying illumination. The article distinguishes itself with not only an efficient structural design but also the use of an enhanced convolutional neural network, methodologically designed and fine-tuned on a dataset tailored for oranges integrated with position visual servoing control system. Enhanced motion planning uses an improved rapidly exploring random tree star algorithm that ensures the optimized path for every robot activity. Moreover, the proposed machine design is rigorously tested to validate the performance of the fruit harvesting robot. The unique aspect of this paper is the in-depth evaluation of robots to test five areas of performance that include not only the accurate detection of the fruit, time of fruit picking, and success rate of fruit picking, but also the damage rate of fruit picked as well as the consistency rate of the robot picking in varying illumination and occlusion. The results are then analyzed and compared with the performance of a previous design of fruit harvesting robot. The study ensures improved results in most aspects of the design for performance in an unstructured environment.

Carrying out the dual objectives of computing the optimized tilt angles for multiple locations in Pakistan along with recommending a generalized approach for the estimation of optimum tilt angle for any location in country, this study was conducted. Python code was developed for the optimization purpose of daily, monthly, seasonally, biannually and annual solar tilt angle. Detailed analysis was presented for one location (Karachi) and a comparative analysis of optimum tilt angle conditions has been conducted for all investigated sites. Daily optimum tilt angles were found to vary from −12.0° to 55.5°. Similarly, monthly optimized tilt angles were found to vary from −11.5° (in June) to 55.0° (in December). Furthermore, seasonal tilt angles were found as 46.0 °, 2.0 °, 0.5 ° and 45.0 ° in winter, spring, summer and autumn respectively. Finally, annual optimum tilt angle (fixed throughout the year) was found as 25.5° (closer to the latitude value of Karachi). It has been found that daily adjustment of tilt angle causes a gain of 6.85% in annual energy yields when compared with fixed angle throughout the year. Similarly, energy gains were found as 0.15%, 1.24% and 6.83%, when tilt angle was adjusted daily as compared to monthly, seasonal and biannual adjustment cases respectively. The optimum tilt angle for three pairs of locations having comparable latitudes were compared and it was found that the sites having comparable latitudes do not have any marked difference in their optimum tilt angle values.

Pakistan is fighting a long‐standing energy crisis with an unbalanced, fossil‐fuel dominated energy mix mired with climatic catastrophes. World Wide Fund for Nature (WWF), in its fight against climate change, has proposed a global energy model that illustrates a shift to renewable energy completely by the year 2050. In this study, we have scaled down that energy model for Pakistan to demonstrate country level implementation of WWF's vision. Scenario‐based energy model of Pakistan is developed in this research using LEAP Software. Four scenarios namely, Business‐As‐Usual (BAU), Alternative and Renewable Energy (ARE), Green Energy (GE) and Advanced Sustainable Energy (ASE) are developed. The BAU scenario is based on government's existing policies and plans, and ASE scenario shows the crux of WWF's Global vision on Pakistan's scale. ARE and GE are intermediate scenarios reflecting a transition from BAU to ASE. Simulation results demonstrated the demand in ASE is halved as compared to BAU and its energy mix is homogeneous and completely renewable. In conclusion, ASE gave country‐level insights into WWF's vision towards the ways to contend climate change. This work inspires country‐scale modeling of WWF's vision for other countries as well.

The objective of this work is to develop empirical correlations describing Diffuse Fraction (DF) as a function of (1) Sunshine Fraction (SF), (2) Clearness Index (CI) and (3) both SF and CI. Four years instantaneously measured data was changed to monthly data at five locations belonging to five different climatic regions in Pakistan which was used as training dataset and nine correlations for each location (a total of forty-five) were formulated and their performance was assessed. Moreover, nine general empirical models were developed using the entire dataset (11 years) for five locations which were termed as Generalized Correlations (GCs). These GCs were validated by applying them to five other locations and comparing the generated results with measured results for those locations (validation dataset). The best model among GCs were found as GC8 which was, then applied to compute DF for five more locations for which short term (8 months) measured data was also available and thus a reasonable comparison could be made. Results showed that (1) new models were better than literature models, (2) GCs correlations were found in good agreement and (3) 2nd degree multivariate polynomial models are the best performance models with minimum errors e.g. Mean Absolute Biased Error (MABE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Sum of Square of Relative Error (SSRE) and Standard Relative Error (SRE) for GC8 were estimated as 0.018, 6.397, 0.021, 0.006 and 0.022 respectively (all values for Karachi).

In order to meet the growing agricultural demands, modern machinery needs to be deployed and cheaper energy supply needs to be ensured. With advancements in artificial intelligence, fruit harvesting robots can improve both the quality and productivity of fruit picking and increase orange fruit exports. However, to ensure effective working of the fruit harvesting robot, an energy source that is both efficient and cost-effective is necessary. This paper aims at designing of a solar energy system for a lightweight fruit harvesting robot for orange orchards in Pakistan and conducting a feasibility study for the deployment of the robot for remote agricultural land. The site for fruit harvesting robot employment is decided by irradiance using random forest regression. Solar system sizing is done based on the design and energy requirements of the fruit harvesting robot. The Homer Pro software is used for simulation of the system to analyse the potential of using solar system for fruit harvesting robot in Sargodha, Pakistan. The results show that compared to hybrid system, a stand-alone system is a more cost effective, reliable, and efficient option with a payback time of 3.36 years and levelized cost of energy being $0.085 per unit kWh. This study proves that solar energy is a viable and cheaper solution for using modern agricultural machineries, like fruit harvesting robot, in remote areas in developing countries, like Pakistan, to enhance productivity and improving quality of the produce.

The safety of automobile design is crucial to protect drivers, passengers, and other road users, ensuring reliable performance, accident prevention, and regulatory compliance.The rear axle housing in automobiles serves as a protective enclosure for the rear axle assembly, providing support, protection, and structural integrity for the drivetrain components. This research paper employs Failure Modes and Effects Analysis (FMEA) to comprehensively identify and assess common failures in the design and manufacturing process of rear axle housings in automobiles. The study highlights five critical failure modes, namely less durability, faulty blank, hole position out, burred edges, and bore position out. Through the application of recommended solutions, including design optimization, maintenance of shear blades, and the use of jigs, substantial reductions in Risk Priority Number (RPN) values were achieved. Specifically, the RPN values for the aforementioned failure modes were significantly decreased from 280 to 40, 256 to 48, 378 to 21, 378 to 45, and 392 to 56, respectively. Implementation of these solutions promises several benefits, such as enhanced customer satisfaction, reduced failure rates, elevated product/process quality and reliability, streamlined processes, and optimized resource utilization. This study emphasizes the significance of continuous improvement efforts in the automotive industry and demonstrates the pivotal role of FMEA as an invaluable tool for achieving and sustaining these improvements.

Purpose Path planning is an essential part in designing of an agricultural robot. The path planning algorithms for fruit harvesting robots vary in performance, based on different environments, obstacles, and constraints. This research aims to analyze and evaluate the most commonly used path planning algorithms by fruit harvesting robots in the past 10 years to assess the robot’s performance. The primary objective behind the comparative analysis of path planning algorithms is to ascertain which algorithm demonstrates better performance in terms of reaching the target fruit in the shortest time, requiring the least amount of computing resources, and being able to navigate around obstacles effectively. Hence, the study determines which path planning algorithm is the most efficient for the application of fruit harvesting robot. Method In this study, four common path planning algorithms were evaluated namely A-star, Probabilistic Road Map, Rapidly exploring Random Tree, and improved Rapidly exploring Random Tree. Three cases were examined for performance. The first case deals with performance based on varying orientations of fruit within the workspace. The second case investigates the performance in the presence of obstacles in the path, and the third case caters to performance due to varying distances of robot and the fruit. Matlab software was used for creating simulation environment for testing. Run time, path length, standard deviation, and total task time were obtained for each case and statistical analysis was done. Results It was found that improved Rapidly exploring Random Tree performed better in terms of path length and gave an optimal path as compared to the other algorithms due to its rewiring feature by an average of 21%. Run time of Rapidly exploring Random Tree was better than the other three algorithms. Conclusion Four most commonly used path planning algorithm were analyzed for performance for fruit harvesting robot for three different cases. Despite the variations in performance across different scenarios, the results confirmed that the improved Rapidly exploring Random Tree algorithm outperformed all other algorithms under the given constraints.

Agricultural robots play a crucial role in ensuring the sustainability of agriculture. Fruit detection is an essential part of orange-harvesting robot design. Ripe oranges need to be detected accurately in an orchard so they can be successfully picked. Accurate fruit detection in the orchard is significantly hindered by the challenges posed by the illumination and occlusion of fruit. Hence, it is important to detect fruit in a dynamic environment based on real-time data. This paper proposes a deep-learning convolutional neural network model for orange-fruit detection using a universal real-time dataset, specifically designed to detect oranges in a complex dynamic environment. Data were annotated and a dataset was prepared. A Keras sequential convolutional neural network model was prepared with a convolutional layer-activation function, maximum pooling, and fully connected layers. The model was trained using the dataset then validated by the test data. The model was then assessed using the image acquired from the orchard using Kinect RGB-D camera. The model was then run and its performance evaluated. The proposed CNN model shows an accuracy of 93.8%, precision of 98%, recall of 94.8%, and F1 score of 96.5%. The accuracy was mainly affected by the occlusion of oranges and leaves in the orchard’s trees. Varying illumination was another factor affecting the results. Overall, the orange-detection model presents good results and can effectively identify oranges in a complex real-time environment, like an orchard.

In this paper, a first- and second-law analysis of vapor compression refrigeration is presented to estimate and propose the replacement of R134 with working fluids having less global warming potential (GWP) and less exergy destruction and irreversibilities. Six different refrigerants were studied, namely, R717, R1234yf, R290, R134a, R600a, and R152a. A thermodynamic model was designed on Engineering Equation Solver (EES) software, and performance parameters were calculated. The model was deployed on all six refrigerants, while the used output parameters of performance were cooling capacity, coefficient of performance, discharge temperature, total exergy destruction, relative exergy destruction rates of different components, second-law efficiency, and efficiency defect of each component. The performance parameters were estimated at different speeds of the compressor (1000, 2000, and 3000 rpm) and fixed condenser and evaporator temperatures of 50 °C and 5 °C, respectively. The isentropic efficiency of the compressor was the same as the volumetric efficiency, and it was taken as 75%, 65%, and 55% at the compressor speeds of 1000 rpm, 2000 rpm, and 3000 rpm, respectively. A comparison of the performance parameters was presented by importing the results in MATLAB. It was found that the compressor had the highest exergy destruction compared to the other components. It was found that R152 was the refrigerant with zero ozone depletion potential (ODP) and a GWP value of 140 with less exergy destruction and irreversibilities. Moreover, it was easy to use R152a with good thermodynamic characteristics. It is estimated that R152a is a suitable replacement for R134a, as it can be used with few modifications.

Purpose Winglets play a major role in saving fuel costs because they reduce the lift-induced drag formed at the wingtips. The purpose of this paper is to obtain the best orientation of the winglet for the Office National d’Etudes et de Recherches Aérospatiales (ONERA) M6 wing at Mach number 0.84 in terms of lift to drag ratio. Design/methodology/approach A computational fluid dynamics analysis of the wing-winglet configuration based on the ONERA M6 airfoil on drag reduction for different attack angles at Mach 0.84 was performed using analysis of systems Fluent. First, the best values of cant and sweep angles in terms of aerodynamic performance were selected by performing simulations. The analysis included cant angle values of 30°, 40°, 45°, 55°, 60°, 70° and 75°, while for the sweep angles 35°, 45°, 55°, 65° and 75° angles were used. The aerodynamic performance was measured in terms of the obtained lift to drag ratios. Findings The results showed that slight alternations in the winglet configuration can improve aerodynamic performance for various attack angles. The best lift to drag ratio for the winglet was achieved at a cant angle of 30° and a sweep angle of 65°, which caused a 5.33% increase in the lift to drag ratio. The toe-out angle winglets as compared to the toe-in angles caused the lift to drag ratio to increase because of more attached flow at its surface. The maximum value of the lift to drag ratio was obtained with a toe-out angle (−5°) at an angle of attack 3° which was 2.53% greater than the zero-toed angle winglet. Originality/value This work is relatively unique because the cant, sweep and toe angles were analyzed altogether and led to a significant reduction in drag as compared to wing without winglet. The wing model was compared with the results provided by National Aeronautics and Space Administration so this validated the simulation for different wing-winglet configurations.

This study aims to model, analyze, and evaluate performance of a flexible manufacturing system, constituting a carousel-based manufacturing and assembly cells layout, configured to produce mixed-model multiple products employing inter-/intra-cellular routing flexibility in which manufacturing and assembly resources are subject to working and failure modes. A hierarchical colored Petri net model is developed to analyze performance of the flexible manufacturing system. Colored Petri net modeling experiments have been conducted to evaluate the system performance for throughput, cycle time, and work-in-process. The system performance has been investigated in relation to material supply and handling system, process execution, and production resources reliability variables. Different input factors are considered for simulation modeling such as mean machining time, mean loading/unloading time, mean assembly time, buffer capacity, material supply inter-arrival time, number of operations between failures, and mean time to repair for production resources; a variation in input factors has shown a significant impact on system performance measures. The colored Petri net–based modeling, simulation, and analysis approach has been demonstrated as an efficient method for carousel-based mixed-model configured flexible manufacturing system.

Cutting down the reliance on fossil fuels and utilization of wind energy as green energy source requires detailed resource exploration using some probability distribution. In contrast to literature methods which are based on first and second moment of Weibull Probability Distribution (WPD) for its parametric estimation, the new method proposed in this study (called Method of Four Moments Mixture, MFMM) combines the effect of first four moments of WPD. This model is based on the squared deviation (deviation of sample from population) of first four moments; minimization of which using Nelder-Mead algorithm estimates parameters of WPD. In order to assess its comprehensive effectiveness, this method has been validated using large dataset i.e. five years wind data measured at 50 m height for thirty-six stations (in Pakistan) for parametric estimation and it has been compared with six past methods using MAPE, RMSE and R-2. To rank all seven methods, Global Performance Indicator (GPI) was evaluated and it was found that MFMM is the best method for all stations. Therefore, it can be effectively used in wind resource assessment of various geographical regions in the world. (C) 2022 Elsevier Ltd. All rights reserved.

Current work focusses on the wind potential assessment in South Punjab. Eleven locations from South Punjab have been analyzed using two-parameter Weibull model (with Energy Pattern Factor Method to estimate Weibull parameters) and five years (2014–2018) hourly wind data measured at 50 m height and collected from Pakistan Meteorological Department. Techno-economic analysis of energy production using six different turbine models was carried out with the purpose of presenting a clear picture about the importance of turbine selection at particular location. The analysis showed that Rahim Yar Khan carries the highest wind speed, highest wind power density, and wind energy density with values 4.40 ms −1 , 77.2 W/m 2 and 677.76 kWh/m 2 /year, respectively. On the other extreme, Bahawalnagar observes the least wind speed i.e. 3.60 ms −1 while Layyah observes the minimum wind power density and wind energy density as 38.96 W/m 2 and 352.24 kWh/m 2 /year, respectively. According to National Renewable Energy Laboratory standards, wind potential ranging from 0 to 200 W/m 2 is considered poor. Economic assessment was carried out to find feasibility of the location for energy harvesting. Finally, Polar diagrams drawn to show the optimum wind blowing directions shows that optimum wind direction in the region is southwest.