AI-Powered Lubrication Strategies for Thread Forming | 
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Adaptive spray jet control to increase process reliability and tool life
| Journal | Industry 4.0 Science |
| Issue | Volume 42, 2027, Edition 3, Pages 76-83 |
| Bibliography | Share | Cite | Download |
Abstract
Thread forming requires precise lubricant application because high contact pressures and process temperatures strongly influence tool loading, friction, and process stability. Although minimum quantity lubrication (MQL) systems are widely used, current spray-based approaches can still suffer from spray losses, insufficient wetting of the thread grooves, and unstable droplet transport. This article presents a concept for adaptive precision lubrication in thread forming based on computational fluid dynamics (CFD)-supported flow analysis, experimental validation, and artificial intelligence (AI)-assisted optimization. The focus is on droplet size, spray jet geometry, nozzle position, ambient flow conditions, and their influence on wetting intensity. Preliminary simulation-based investigations indicate that data-driven optimization can help identify wetting deficiencies and support the development of future control strategies for resource-efficient lubricant application.
Keywords
AI-based control, droplet dynamics, fluid mechanics, lubricant application, process stability, resource efficiency, Ressourceneffizienz, thread forming, tool wear
Article
Thread forming is widely established in manufacturing as a cost-effective and reliable process for producing internal threads. In contrast to thread cutting, thread forming is a chipless process in which the material is plastically deformed rather than removed. This results in smooth thread flanks, increased strength, and improved surface quality [1, 2]. However, the high contact pressure between the tool and the workpiece poses a challenge for process control. A suitable process-specific lubrication strategy is crucial for minimizing friction and heat generation, reducing tool wear, and ensuring process stability [3]. The current state of the art comprises …
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