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泰勒-大猩猩部队优化了深度学习网络,用于表面粗度估计
Syed Jahangir Badashah1, Shaik Shafiulla Basha2, Shaik Rafi Ahamed3
1Professor ECE Department, Sreenidhi Institute of Science and Technology, Hyderabad, India.
概括
一个新的基于优化器的泰勒-大猩猩部队深度神经模糊网络 (基于泰勒-GTO的DNFN) 估计了无接触的加工表面粗度. 这种方法提供了精确的表面粗度评估,克服了传统的接触式 stylus profilometry 的局限性.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 表面粗度评估对于加工产品的质量至关重要.
- 接触式笔造型测量是常见的,但会导致表面退化.
- 需要一种非接触式,准确的方法来估计表面粗度.
研究的目的:
- 提出一种新的,非接触表面粗度评估技术.
- 开发一个深度神经模糊网络 (DNFN),通过混合算法优化粗度估计.
- 评估拟议的基于泰勒-GTO的DNFN模型的性能.
主要方法:
- 一种混合优化算法,Taylor-Gorilla Troops Optimizer (Taylor-GTO),是通过结合泰勒序列和大猩猩的部队优化器来开发的.
- 一个深度神经模糊网络 (DNFN) 被训练使用泰勒-GTO算法进行表面粗度估计.
- 该方法包括预处理,数据增强,特征提取,特征融合和粗度估计.
主要成果:
- 提议的基于泰勒-GTO的DNFN模型在表面粗度估计中实现了高精度.
- 该模型展示了最小的平均绝对误差 (0.403),平均平方误差 (0.416) 和根平均平方误差 (1.149).
- 开发的技术有效地估计了表面粗度,而不会导致工件退化.
结论:
- 基于Taylor-GTO的DNFN提供了一种可靠和准确的非接触方法来评估表面粗度.
- 这种方法克服了传统的基于接触的方法的局限性,防止表面损伤.
- 该研究强调了混合AI优化技术在精密制造质量控制中的潜力.
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