Session: Research Posters

Paper Number: 165801

Path Planning for Drilling Large and Medium-Sized Workpieces Based on a Greedy Algorithm 

With the growing demand for logistics and passenger transportation, the aerospace industry is expanding, while the shift toward carbon neutrality is accelerating the adoption of wind power generation. Both trends necessitate the production of large-scale components, increasing the demand for efficient machining of large workpieces. However, current machining technologies, such as gantry-type machine tools and long NC processing machines, are constrained by their fixed installation, which limits the size of workpieces they can process. To overcome these limitations, this research aims to develop a system in which multiple autonomous robotic machining units collaborate to perform flexible and efficient machining tasks. The objective is to establish a machining path generation method that optimizes hole drilling and riveting processes on complex surfaces, ultimately integrating this method into Siemens NX CAD/CAM to streamline manufacturing operations.
This research contributes to scientific and engineering advancements by introducing an automated, mobile machining system capable of adapting to various workpiece sizes and machining shapes. The development of an optimized toolpath generation algorithm ensures minimal travel distance, reduced energy consumption, and improved productivity. Additionally, by integrating the algorithm into Siemens NX CAM through NX Open API, this study enables a seamless workflow from 3D modeling to machining path generation and NC programming within a unified platform. The proposed system enhances manufacturing flexibility and efficiency, making it particularly relevant for industries requiring large-scale, high-precision machining, such as aerospace and renewable energy.
To achieve these objectives, the research focuses on optimizing machining paths using combinatorial optimization techniques. The study explores various pathfinding algorithms, including exact methods such as brute-force search and the Held-Karp algorithm, as well as heuristic methods like the genetic algorithm, ant colony optimization, and greedy algorithms. While exact methods guarantee optimal solutions, they become computationally infeasible as problem size increases. Heuristic approaches, on the other hand, offer practical alternatives but may lead to suboptimal solutions. To address this challenge, hybrid methods, such as an irregular greedy algorithm and a penalty-based greedy algorithm, were introduced to improve solution quality by reducing the likelihood of getting trapped in local optima.
Preliminary results demonstrate that the greedy and penalty-based greedy algorithms achieve the shortest travel distances but are susceptible to local optima. The irregular greedy algorithm introduces randomness to enhance solution diversity but results in longer travel distances. The genetic algorithm explores a broader solution space, potentially yielding better global optima. However, in the targeted machining path, the number of points is excessively large, preventing the solution from converging effectively. Given these findings, an optimized combination of irregular greedy and parameter tuning is necessary for real-time industrial applications.
For practical implementation, the optimized path generation algorithm has been integrated into Siemens NX CAM using the NX Open API. This integration allows for direct extraction of hole positions from CAD models, automated toolpath calculations, and NC program generation within a single software platform. By consolidating these processes, the system simplifies real-world adoption and enhances machining efficiency.
In conclusion, this research proposes an efficient and flexible machining path generation system for large-scale workpieces, leveraging optimization algorithms to aim for the minimization of travel distance, machining time, and energy consumption. The successful integration of this algorithm into Siemens NX CAM provides a streamlined workflow for automated machining operations. Future work will focus on enhancing algorithm efficiency, expanding machining capabilities beyond hole drilling and riveting, and conducting real-world industrial validations to refine the system’s practicality. By advancing autonomous machining technology, this research contributes to the future of smart manufacturing and large-scale production automation.

Presenting Author: Ryoga Ota Kanazawa Institute of Technology

Presenting Author Biography: Name: Ryoga Ota
Affiliation: Kanazawa Institute of Technology
Position: Graduate Student
Major: Mechanical Engineering
Program: Master’s Course
Research Field: Production Systems
Experience: Conference presentations in Japan

Authors:

Ryoga Ota Kanazawa Institute of Technology
Hajime Endo Kanazawa Institute of Technology
Yoshitaka Morimoto Kanazawa Institute of Technology
Akio Hayashi Kanazawa Institute of Technology
Yoshiharu Kitaguchi GIKEN CO., LTD.
Yutaro Yasui GIKEN CO., LTD.
Taro Matsuzaki GIKEN CO., LTD.