在如今的制造业中，对零部件精度的要求越来越高。传统的数控加工方式在面对复杂的加工任务和高精度要求时，往往会遇到一些瓶颈。而AI技术的出现，为解决这些问题提供了新的思路和方法。它能够处理大量的数据，进行复杂的分析和决策，从而优化数控加工过程，提高加工精度。    

　　通过AI算法建立多轴数控机床空间误差模型、多失效模式下可靠度模型和敏感度模型，以及机床总成本模型。以最小敏感度和最低成本为目标，将可靠性作为约束，优化机床几何参数误差，从根本上解决多轴数控机床几何误差获取问题和机床主要传输组件精度等级确定问题，进而提高机床加工精度保持性。比如科易网提供的基于稳健设计的多轴数控机床加工精度保持性优化方法就是类似思路。    

　　利用AI技术实时获取电导率和温度等数据，并与标准数据进行对比，动态调整放电电流和电压等加工参数。像南通格美申请的基于AI的电火花成型优化系统专利，通过这种方式提升了加工精度与响应速度，能更精确地适应介质的变化，有效预防因参数不匹配引发的加工缺陷。    

　　运用卷积神经网络等AI技术对工件表面缺陷进行自动识别和分类，并根据识别结果针对性地调整脉冲频率和持续时间等参数，细化加工质量控制，确保工件的表面质量达到更高标准。南通格美专利中的表面质量优化模块就采用了这种方式。    

　　目前，AI在数控精度优化方面已经取得了一定的成果，越来越多的企业开始尝试应用AI技术来提升加工精度和效率。它不仅提高了产品的质量，减少了废品率，还能降低生产成本，提高企业的竞争力。    

The   method of AI-optimized numerical control accuracy    

　　In   today's manufacturing industry, the requirements for the precision of   components are getting higher and higher. Traditional numerical control   machining methods often encounter some bottlenecks when dealing with complex   processing tasks and high-precision requirements. The emergence of AI   technology has provided new ideas and methods for solving these problems. It   can handle a large amount of data, conduct complex analysis and   decision-making, thereby optimizing the numerical control machining process   and improving machining accuracy.    

　　The   spatial error model, reliability model and sensitivity model under multiple   failure modes of multi-axis CNC machine tools, as well as the total cost   model of the machine tools are established through AI algorithms. With the   goal of minimizing sensitivity and the lowest cost, and taking reliability as   a constraint, the geometric parameter errors of the machine tool are   optimized to fundamentally solve the problem of obtaining geometric errors of   multi-axis CNC machine tools and determining the accuracy grades of the main   transmission components of the machine tool, thereby improving the retention   of machining accuracy of the machine tool. For instance, the multi-axis CNC   machine tool machining accuracy retention optimization method based on robust   design provided by Keyi Network follows a similar approach.    

　　By   using AI technology to obtain real-time data such as conductivity and   temperature, and comparing them with standard data, processing parameters   such as discharge current and voltage can be dynamically adjusted. For   instance, the patent for the AI-based electrical discharge forming   optimization system applied for by Nantong Gemei has enhanced processing   accuracy and response speed through this approach. It can more precisely   adapt to changes in the medium and effectively prevent processing defects caused   by parameter mismatch.    

　　By   applying AI technologies such as convolutional neural networks, surface   defects of workpieces are automatically identified and classified. Based on   the recognition results, parameters such as pulse frequency and duration are   adjusted in a targeted manner to refine the quality control of processing and   ensure that the surface quality of workpieces reaches a higher standard. The   surface quality optimization module in Nantong Gemei's patent adopts this   approach.    

　　At   present, AI has achieved certain results in the optimization of numerical   control accuracy. More and more enterprises are beginning to try to apply AI   technology to improve processing accuracy and efficiency. It not only   improves the quality of products, reduces the rate of defective products, but   also lowers production costs and enhances the competitiveness of enterprises.    

• 上一篇：霸刀分享- 焊接铣刀的优点
• 下一篇：霸刀分享-玉石雕刻机的设计原理