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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Development of surface texture evaluation system for highly sparse data-driven machining domains
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Development of surface texture evaluation system for highly sparse data-driven machining domains

机译：用于高稀疏数据驱动加工域的表面纹理评价系统的开发

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摘要

Dimensional, geometrical and surface texture tolerances are significant issues to be addressed by manufacturing industries. Dimensional and geometrical tolerance estimation systems are used by several machining industries, whereas surface texture tolerance estimation systems are rare. In general-purpose machines, several machining operations are performed and huge machining data are required for the development of surface texture tolerance estimation model. The necessity for sparse data modeling is the need of the hour and such modeling techniques reduce costly trial and error approaches. Shoulder milling operations are performed on mild steel workpieces. Experimentation is performed at diverse cutting conditions, and surface texture tolerances of the machined components are measured. Big data computation is being accomplished by contemporary tools compared to sparse data evaluation. The novelty of this work is to develop a system capable of surface texture tolerance evaluation from highly sparse learning data, as big data generation involves cost and time for experimentation. Flower Pollination Algorithm (FPA) models are developed for the highly sparse data for surface roughness estimation. Two different FPA techniques, namely, Maximum (Max) and Average (Avg) are used, and a comparison between the methods mentioned above is made. An operator-friendly adaptive performance enhancement system is developed to evaluate the corresponding surface texture tolerance for the given operating parameters.
机译：尺寸，几何和表面纹理公差是制造业所解决的重要问题。尺寸和几何公差估计系统由多种加工行业使用，而表面纹理公差估计系统很少见。在通用机器中，执行几种加工操作，并且需要巨大的加工数据来开发表面纹理公差估计模型。稀疏数据建模的必要性是需要时序，这种建模技术会降低昂贵的试验和误差方法。肩部铣削操作是在低碳钢工件上进行的。实验在不同的切割条件下进行，测量机加工部件的表面纹理公差。与稀疏数据评估相比，当代工具正在完成大数据计算。这项工作的新颖性是开发一种能够从高度稀疏的学习数据进行表面纹理公差评估的系统，因为大数据产生涉及实验的成本和时间。为表面粗糙度估计的高稀疏数据开发了花授粉算法（FPA）模型。使用两种不同的FPA技术，即，最大（最大值）和平均（AVG），并进行上述方法之间的比较。开发了一种操作员友好的自适应性能增强系统，以评估给定的操作参数的相应表面纹理公差。

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