International Journal of Robotics and Machine Learning Technologies

Predictive Modelling of Surface Roughness in Manufacturing A Study Using Multiple Machine Learning Techniques

Rakesh Mittapally — 2025-02-28

This study provides an in-depth study advanced machining processes and their optimization using various machine learning algorithms. The study focuses on key machining parameters such as cutting speed (m/min), feed rate (mm/rev), and cutting depth (mm), and rotation speed (RPM), investigating their effects on surface roughness (Ra) in manufacturing operations. This research addresses emerging challenges in modern manufacturing, particularly in the processing of advanced engineering materials for the aerospace, automotive, and precision industries.These algorithms were selected for their ability to manage complex and non-linear relationshipsin manufacturing data and for their proven performance in predictive modelling. The study explores how these methods can overcome traditional limitations in process planning and optimization, especially in situations where conventional empirical models are inadequate.Special attention is paid to the theoretical foundations of each algorithm, in which linear regression serves as a basic model, random forest regression provides improved predictive capabilities through ensemble learning, and support vector regression provides robust optimization through its ε-insensitive loss function approach. The research also explores the important relationship between machine parameter optimization and surface quality, emphasizing the importance of parameter optimization in achieving desired surface properties while maintaining production efficiency.This study advances the field by providing a structured methodology machine parameter optimization, particularly relevant to computer-aided process planning and advanced manufacturing processes. These findings have significant implications for industries requiring high-precision manufacturing, providing insights into How can machine learning methods be used effectively? optimize machining processes, reduce production costs, and improve surface quality in modern manufacturing operations.

Innovative Fabrication of Advanced Robots Using The Waspas Method A New Era In Robotics Engineering

Nagababu Kandula — 2025-03-12

Introduction: The fabrication of advanced robots represents a pivotal intersection of cutting-edge materials science, artificial intelligence, and innovative manufacturing techniques. These robots are designed to perform complex tasks autonomously, from industrial automation and healthcare assistance to space exploration and disaster response. With breakthroughs in AI, 3D printing, and nanotechnology, modern robots are becoming more intelligent, agile, and capable than ever before. However, the rise of these machines also raises important questions about societal impacts, ethical considerations, and job displacement. The ongoing advancements in robot fabrication promise to reshape industries and redefine the role of automation in human life.

Research significance: The significance of research in the fabrication of advanced robots lies in its transformative potential across numerous sectors. It drives innovation in automation, improving efficiency and precision in industries like manufacturing, healthcare, and logistics. Advanced robots can address complex societal challenges, such as providing personalized healthcare, performing dangerous tasks, and enhancing disaster recovery. Research also enables the integration of cutting-edge technologies like AI, nanotechnology, and materials science, pushing the boundaries of robotics capabilities. Furthermore, it tackles ethical, social, and economic implications, guiding responsible innovation to ensure positive societal impact while mitigating job displacement and other risks.

Methodology: The fabrication of advanced robots involves a multi-disciplinary methodology combining materials science, manufacturing techniques, and artificial intelligence (AI). The process starts with designing robot structures using lightweight, durable materials like composites and metals. Additive manufacturing (3D printing) and precision machining are employed to create complex components. Sensors, actuators, and processors are integrated to enable movement and functionality. AI and machine learning models are embedded for autonomous decision-making, adapting robot behaviors to dynamic environments. Testing and iterative prototyping ensure performance, reliability, and safety. Finally, robots undergo optimization for energy efficiency, user interaction, and task-specific capabilities in their intended applications.

Alternative: R-Alpha, R-Beta, R-Gamma, R-Delta, R-Epsilon

Evaluation preference: Precision (B1), Speed (B2), Durability (B3), Energy Efficiency (B4)

Assessing The Role of Ai In Robotics A Dematel Methodology Approach

Suresh Pandipati — 2025-02-28

The future of AI-powered robotics promises a major transformation across various industries, offering remarkable potential to automate complex processes, boost human capabilities, and redefine numerous sectors. By utilizing machine learning, computer vision, and natural language processing, AI-driven robots can interact with their surroundings, make informed decisions, and learn from their experiences, all without direct human oversight. This fusion of robotics and AI enables machines to tackle delicate tasks such as performing surgeries in healthcare, as well as advancing manufacturing and logistics operations. In the near future, AI-powered robots are expected to become increasingly autonomous, adaptable, and proficient in managing tasks within ever-changing environments. Breakthroughs in machine perception will allow robots to better comprehend and respond to the world around them, enhancing safety and effectiveness. Furthermore, advancements in human-robot collaboration may lead to robots working alongside humans in sectors like education, hospitality, and services, improving productivity and user interactions. However, the rise of AI-powered robotics also raises important ethical, legal, and social concerns, such as job loss and privacy issues. To ensure these technologies benefit society, careful integration will be essential. Ultimately, AI-powered robots are set to play a crucial role in shaping the future, transforming how we live and work.

Research significance: The future of AI-driven robotics has the potential to revolutionize various industries, including healthcare, manufacturing, logistics, and agriculture. Research in this area concentrates on improving robot autonomy, decision-making, and collaboration with humans through advanced AI technologies. As robots become smarter, they will be able to carry out intricate tasks with greater accuracy, flexibility, and efficiency, boosting productivity and safety. This research also tackles issues such as ethical concerns, reliability, and human-robot emotional interaction. Progress in AI robotics promises innovative, practical, and sustainable solutions, transforming industries and enhancing overall quality of life.

Methodology: The approach to studying the future of AI-driven robotics focuses on exploring how artificial intelligence integrates with robotic systems. This involves examining progress in machine learning, computer vision, and natural language processing, which allow robots to complete intricate tasks independently. Researchers investigate technological innovations such as enhanced sensors, edge computing, and human-robot interactions. They also address ethical, societal, and economic implications, such as the effects of automation, labor markets, and safety. To forecast trends, challenges, and opportunities in AI robotics, experts use case studies, simulations, and interviews, offering a well-rounded view of its future potential

Alternative: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics

Evaluation preference: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics

Results: Hardware advancements are rising to the top, while regulation and ethics are being pushed to the bottom

Chatbots for ERP User Support Using AI

Veeresh Dachepalli — 2025-03-14

The need for effective user assistance methods has increased due to the quick development of enterprise resource planning (ERP) systems. Traditional support models often strain resources and incur significant costs. This paper introduces AI-driven chatbots as a solution to streamline ERP user support, reduce overhead, and enhance user satisfaction. Leveraging advanced transformer-based Natural Language Processing (NLP) models, the proposed chatbot architecture facilitates real-time, accurate query resolution. Through a detailed exploration of methodology, implementation outcomes, and system capabilities, this study demonstrates the potential of AI chatbots to revolutionize ERP user assistance.

Comparative Analysis of Machine Learning Models for Laptop Price Prediction An Evaluation of Linear Regression, Histogram Gradient Boosting, and XGBoost Approaches

Satyanarayana Ballamudi — 2025-03-10

In the rapidly evolving landscape of technology-driven commerce, laptops have become indispensable for both personal and professional applications, with a vast array of models presenting varied specifications and features. The intricate interplay of hardware configurations and pricing frameworks underscores the necessity for robust predictive models that empower consumers and manufacturers to make well-informed choices. This study delves into the critical challenge of accurately forecasting laptop prices by evaluating three machine learning methodologies: Linear Regression (LR), Histogram Gradient Boosting Regression (HGBR), and XGBoost Regression (XGBR). The research's importance is rooted in its capacity to refine pricing strategies, bolster market efficiency, and provide consumers with deeper insights into the value dynamics associated with different laptop specifications.The study leveraged an extensive dataset comprising 1,303 laptop entries, each characterized by 11 pivotal attributes encompassing processor type, RAM, storage capacity, screen dimensions, and graphical performance. Analytical techniques encompassed correlation assessment, feature significance determination, and comparative evaluation of model efficacy, employing key performance indicators such as the R² coefficient, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).XGBoost demonstrated a clear dominance over other predictive models, securing an R² value of 0.93559 on training data and 0.77524 on testing data. This superiority was further underscored by its markedly lower error margins, with an RMSE of 9,334.9 for training data, starkly contrasting the significantly higher 23,506.3 observed in Linear Regression. A thorough correlation analysis pinpointed RAM and processor specifications as the most decisive variables influencing price determination.

The study asserts that ensemble learning methodologies, particularly XGBoost, represent the most dependable strategy for forecasting laptop prices. Nonetheless, the research highlights key areas for refinement, especially in narrowing the discrepancy between training and testing performance. These insights hold substantial implications for stakeholders in the laptop industry, paving the way for the advancement of more sophisticated predictive frameworks. Furthermore, the study enriches the broader discourse on consumer electronics pricing, emphasizing the transformative role of machine learning in optimizing market dynamics and strategic decision-making.