arXiv subjects

Ebru Turanoglu Bekar

Publications and source records attributed to Ebru Turanoglu Bekar.

2 recordsLinked to original sources

arXiv2026

Retrieval-grounded robot program generation and simulation-based correction via Model Context Protocol

Flexible manufacturing requires industrial robots to be reprogrammed rapidly as product variants change. This paper presents a language-model-based workflow that generates, validates, and iteratively corrects ABB RAPID robot programs from natural language task descriptions. A dual-stream retrieval-augmented generation (RAG) pipeline grounds code generation in verified technical documentation and production templates, reducing domain-specific errors produced by ungrounded language models. A custom Model Context Protocol (MCP) server connects the language-model client directly to ABB RobotStudio for automated code upload, simulation execution, and diagnostic feedback. The evaluation combines a 30-query retrieval benchmark, scoped code-generation checks, and RobotStudio case studies in a simulated pickand- place manufacturing cell. The simulation loop exposes execution failures that static and semantic checks alone cannot catch, including suction release-height errors, unreachable placement targets, and configuration-dependent recovery motions. The results show how RAG and MCP can connect grounded code generation with executable feedback from industrial robot simulation software, while reducing but not eliminating expert setup and final supervision.

cs.AI↗

arXiv2026

Machine Learning-Based Analysis of Critical Process Parameters Influencing Product Quality Defects: A Real-World Case Study in Manufacturing

Quality control is an essential operation in manufacturing, ensuring products meet the necessary standards of quality, safety, and reliability. Traditional methods, such as visual inspections, measurements, and statistical techniques, help meet these standards but are often time-consuming, costly, and reactive. With the advent of AI/ML, manufacturers can shift from reactive to proactive approaches in quality control. This study applies ML-based models for predictive quality control in a real-world manufacturing setting. The case company produces castings for powertrain components in heavy vehicles, where poor control of core-making process parameters leads to costly defects. ML models were developed by analyzing data from two core-making machines, their processes, and maintenance logs to identify parameters associated with casting defects, enabling the prediction and prevention of potential defects before they occur. The results demonstrated good accuracy rates, helping quality and production teams identify and eliminate defective cores and thereby improving product quality and production efficiency.

eess.SP↗