The Role and Impact of Robotics Integration in Precision Machining and Manufacturing: A Comprehensive Review

The role of robotics in precision machining and manufacturing has undergone significant evolution over time, as technological advancements have empowered robots to execute a diverse array of tasks with remarkable accuracy and uniformity. This comprehensive review provides an overview of the historical progression of robotics in the specified field, evaluates its present status, analyzes practical case studies of its applications, and investigates potential future avenues for development. Despite the notable progressions in the domain of robotics, there persist certain deficiencies and inadequacies. Several factors need to be considered in relation to the adoption of robotics technology, namely the significant financial investment required, the potential for job displacement resulting from automation, and the necessity of skilled personnel to effectively operate and maintain these machines. In order to bridge these gaps, it is imperative to undertake more research and development endeavors. Future research efforts may focus on developing economically feasible robotics solutions, specifically designed to meet the requirements of small and medium-sized organizations. Moreover, it is crucial to investigate approaches aimed at mitigating the adverse consequences of employment relocation. Furthermore, it is imperative to emphasize the need of establishing training programs that focus on providing workers with the necessary skills and knowledge to proficiently operate and maintain robots systems. This paper provides a thorough analysis of the application of robots in precision machining and manufacturing, with a focus on the potential of robotics to improve efficiency, accuracy, and flexibility in manufacturing operations

Harnessing Machine Learning, Blockchain, and Digital Twin Technology for Advanced Robotics in Manufacturing: Challenges and Future Directions

This paper digs into robots’ revolutionary role in the industrial landscape, highlighting present uses and future trends while addressing ongoing problems. It investigates how machine learning is altering industrial processes, increasing efficiency and production while simultaneously highlighting the challenges of data needs and model interpretability. The evaluation investigates the promise of blockchain technology in enhancing industrial security and transparency, while also recognizing the hazards of possible attacks and smart contract vulnerabilities. The transformational influence of additive manufacturing, particularly 3D printing, is discussed, as well as the constraints connected with printing speed, product quality, and material availability. The study emphasizes the potential of new materials such as bio-based polymers and 2D heterostructures in the advancement of robotic systems. Despite these encouraging achievements, the assessment finds gaps in existing research and suggests future strategies for maximizing the potential of these technologies in the industrial industry

Experimental Investigation Of EDM Die Sinking Process Parameters On Aluminium Alloy 5083 Using Design Of Experiment

In this paper, the results of surface roughness (Ra) and material removal rate (MRR) are presented based on experimental studies of Electrical Dischar ge Machining (EDM) process parameters. Pulse ON time, pulse OFF time, peak current, gap voltage and jump speed are the selected input parameters and the experiments were conducted with Aluminium Alloy 5083 as a workpiece, copper as an electrode and the response variables are surface roughness (Ra) and material removal rate (MRR). Design of Experiment and Analysis of Variance (ANOVA) were applied to identify the optimum settings.The result shows that the significant factors for the value of surface roughness (Ra) and material removal rate (MRR) are pulses ON time and peak current

Re-exploration of ε-greedy in deep reinforcement learning

This paper presents re-exploration as a method for improving the existing method for balancing the exploration/exploitation problem integral to reinforcement learning. The proposed method uses a ε-greedy method called “decreasing epsilon,” which reiterate the method after a certain period of episodes in the middle of the learning. The experiment was conducted using Turtlebot3 simulation under the Robot Operating System (ROS) environment. The evaluation involved comparing the existing method, which is pure exploitation (totally greedy), conventional ε-greedy method and proposed method, which is decreasing-epsilon with the re-exploration method. The preliminary results indicate that applying re-exploration method is easier to implement and yet able to improve the reward obtained with in shorter time (episode) compared to the conventional method