Machine tools are used to manufacture components with desired size, shape, and surface finish. The accuracy of machining is influenced by stiffness, structural damping, and long-term dimensional stability of the machine tool structures. Components machined using such machines exhibit more dimensional variations because of the excessive vibration during machining at higher speeds. Compared to conventional materials like cast iron, stone-based polymer composites such as epoxy granite have been found to provide improved damping characteristics, by seven to ten folds, due to which they are being considered for machine tool structures as alternate materials. The stiffness of structures made of epoxy granite can be enhanced by reinforcing with structural steel. The current work highlights the design and analysis of different steel reinforcements in the lathe bed made of the epoxy granite composite to achieve equivalent stiffness to that of cast iron bed for improved static and dynamic performances of the CNC lathe. A finite element model of the existing the cast iron bed was developed to evaluate the static (torsional rigidity) and dynamic characteristics (natural frequency) and the results were validated using the experimental results. Then finite element models of five different steel reinforcement designs of the epoxy granite bed were developed, and their static and dynamic behaviors were compared with the cast iron bed through numerical simulation using finite element analysis. The proposed design (Design-5) of the epoxy granite bed is found to have an improvement in dynamic characteristics by 4–10% with improved stiffness and offers a mass reduction of 22% compared to the cast iron bed, hence it can be used for the manufacture of the CNC lathe bed and other machine tool structures for enhanced performance.
This research is done to determine the optimum parameters to drill polytetrafluoroethylene (PTFE) and to investigate the effect of two-tier modeling for enhanced response in optimization. RSM model was done with L27 experimental design, considering speed (N), feed (f), and tool point angle (Ɵ). RSM data were further trained and tested using the Adaptive Neuro-Fuzzy Inference System (ANFIS), and β coefficient values were restructured to form revised RSM model. Both nonrevised RSM model and revised RSM model were used in Genetic Algorithm to locate the minimum surface roughness. ANFIS revised RSM model deviates from the experimental results by 2.6% and 2.86% for dry and wet condition; meanwhile, nonrevised RSM model deviates by 4.76% and 4.94%, respectively. The research concludes that two-tier modeling using RSM and ANFIS is better. Spindle speed of 1656 rpm, feed rate of 0.05 mm/min, and point angle of 100° are the optimum conditions to drill PTFE material where the best surface quality of 0.68 μm at wet drilling can be achieved.
Teaching learning-based optimization (TLBO) is a popular algorithm used to solve various optimization problems. Nevertheless, conventional TLBO and some improved variants tends to suffer with premature convergence due to rapid loss of population diversity, especially when handling the challenging optimization problems. Furthermore, it is not practical to tackle real-world multiobjective problems using prior approach given the frequent changes of customers' requirements. Motivated by these challenges, an improved variant known as Modified Multi-objective Teaching Learning Based Optimization-Refined Learning Scheme (MMTLBO-RLS) was proposed as a posterior approach to solve challenging multiobjective optimization problems, including the prediction of optimum turning parameters to machine Polyether ether ketone material (PEEK). Substantial modifications were introduced for teacher and learner phases of MMTLBO-RLS to achieve better balancing of exploration and exploitation searches without incurring excessive computational cost. For modified teacher phase of MMTLBO-RLS, each learner was guided by a unique teacher solution and unique mean position to perform searching with better diversity. Meanwhile, two new learning strategies are incorporated into the modified learner phase of MMTLBO-RLS, enabling all learners to enhance their knowledge more efficiently based on their learning preferences. A systematic approach was followed to develop modelling equations required for optimization. The developed algorithm was then employed in single objective optimization as well as multiobjective optimization to cater its performances in any real-world environment. The prediction model reports that surface roughness of 1.1042μm and material removal rate of 22.8991 cm 3 /minute can be achieved. The predicted results differ from validation results by less than 2.69% in any case of optimization. A benchmarking on the performance of MMTLBO-RLS in solving CEC 2009 multiobjective benchmark functions was further carried out with other seven meta-heuristic algorithms. The superior performance of MMTLBO-RLS proves that it is not only suitable to be used in industries to produce the parts of PEEK with supportive quality and quantity, but it is also able to solve other multiobjective optimization problems with competitive performances.
Drilling is a quite common operation being performed in the manufacturing of components. Instrumental response in drilling is geometrical accuracy and surface integrity of the drilled parts. For the application where geometrical tolerance is very small, an operation is to be carried out very carefully. If not, rejection of drilled samples will be higher and consequently production loss will be higher. The use of prediction model in this scenario is much more appropriate and cost-effective. This research aimed to apply extreme gradient boosting (XGBoost) regressor to develop a drilling prediction model. Drilling experiments were conducted after developing design of experiments with twenty-seven unique sets. Experimental data analysis was then carried out on experimental data sets that have features such as speed, feed, angle, hole length, and surface roughness. After correlation analysis, the k-fold cross validation method was applied for parameterisation. Hyperparameters estimated from the k-fold cross validation were then applied to train and test the XGBoost regressor-based machine learning (ML) model. It is concluded from the model evaluation metric (R2) that the XGBoost regressor model has resulted 0.89 before tuning and 0.94 after tuning of the model, which is higher than the polynomial regressor and support vector regressor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.