典型零件智能磨削工艺软件关键技术研究现状与展望OA

Significance:Grinding,as the final machining method for typical parts such as camshafts,machine tool spindles,and bearings,is critical to meeting their stringent requirements for high precision and surface quality.These components are characterized by complex geometric profiles,low structural rigidity,and high material hardness,which often lead to challenges such as grinding chatter,thermal burns,and excessive wheel wear.Furthermore,the selection of grinding process plans for typical parts remains predominantly reliant on operator experience,making them prone to human-induced empirical errors or inaccuracies that compromise grinding efficiency and quality.To address these issues,it is imperative to encapsulate grinding process knowledge into scalable and reusable industrial software,replacing human decision-making and control.While software serves as the medium,its core value lies in integrating human expertise,intellectual insights,and data-driven artificial intelligence.Therefore,leveraging grinding process software for typical parts to systematically reuse process knowledge by utilizing existing process and machining data from manufacturing enterprises is critical to achieving efficient,high-quality,and intelligent grinding operations.Progress:The intelligent grinding process software for typical parts derives new process solutions through reasoning mechanisms such as process problem definition,data retrieval,case matching,empirical knowledge/rules,and artificial intelligence algorithms.It then automatically generates NC grinding programs to guide machining operations.Key modules include a fundamental database,process knowledge repository,process definition,decision optimization,and automated programming.(1)Fundamental database&process knowledge repository:By systematically organizing data from grinding processes(e.g.,machine tools,grinding wheels,consumables)and incorporating engineers'empirical expertise,these modules provide robust data support for optimal grinding process selection.(2)Process definition module:This module standardizes the expression of process problems by detailing workpiece physical properties,quality requirements,material types,and geometric features,thereby generating process problem models as computable instances.(3)Decision optimization module:Leveraging the rough set and case-based reasoning(RS-CBR)technique integrated with three-way decision theory,this module performs attribute reduction,weight calculation,similarity evaluation,and case assessment to identify optimal process instances.When existing process instances fail to meet requirements,rule-based reasoning(RBR),neural networks,or heterogeneous ensemble learning methods are employed to intelligently infer or map grinding parameters,generating or refining reference solutions.(4)Automated programming module:Supported by technologies such as toolpath planning,machine motion analysis,interpolation algorithms,and speed control,this module automates the transition from design to machining by integrating workpiece-specific NC machine tool specifications,enabling the automated generation of NC programs.Conclusions and prospects:Currently,the integrated application of software with CNC grinding machines demonstrates promising results in enhancing production efficiency and machining quality for typical parts,underscoring its significant theoretical and practical value.However,most functionalities of existing intelligent grinding process software primarily focus on process parameter optimization,lacking dynamic perception and analysis of the grinding process.This limitation hinders their ability to address the failure of pre-optimized solutions caused by real-time changes in process states.Consequently,it is imperative to advance research on multi-information fusion perception during grinding,intelligent operational condition cognition,and data-model fusion-driven dynamic modeling methodologies.These innovations enable intelligent perception and dynamic prediction of grinding processes for typical parts,further elevating the adaptability and intelligence of grinding process software to ensure robust performance under varying machining conditions.