In recent years, applications of Machine Learning and Artificial Intelligence are gaining momentum to the production researchers to analyze the complex interdependencies present in the production dataset. The manufacturers have started to incorporate machine learning approaches to the production process & predictive algorithms to fine-tune the quality of the product. The objective of the proposed work is to apply classification and regression algorithms to analyze the input process parameters for the pack boronizing process of SS410. To prepare the dataset, 9 experiments were carried out and the test specimens having 55 mm & thickness of 10 mm are pack boronized using the boronizing agent 325 mesh size. This process is carried out in 4.5 kW ‘INDFURR’ electric furnace with varying input parameters of temperature, time and gas pressure. The output parameters are boronizing thickness and microvickers hardness. The SEM and optical microscopic images of the specimen confirm the formation of the boronizing layer. To find the influence parameters, it is analyzed using ANOVA and Decision Tree algorithm. Both the techniques confirmed that time as the most significant parameter for boronizing thickness. For surface hardness, time & temperature are the major influencing parameters. Various regression models from machine learning were formulated to find the relationship between variables. Among these models, multilayer perception produced maximum correlation co-efficient & minimum root mean square error.

近年来，机器学习和人工智能的应用正在为生产研究人员分析生产数据集中存在的复杂相互依赖性提供动力。制造商已开始将机器学习方法纳入生产过程和预测算法，以微调产品质量。拟议工作的目标是应用分类和回归算法来分析 SS410 包渗硼工艺的输入工艺参数。为了准备数据集，进行了 9 次实验，并使用 325 目大小的渗硼剂对 55 毫米和 10 毫米厚的试样进行包硼化。该过程在 4.5 kW 'INDFURR' 电炉中进行，具有不同的温度、时间和气体压力输入参数。输出参数是渗硼厚度和显微维氏硬度。样品的 SEM 和光学显微图像证实了渗硼层的形成。为了找到影响参数，使用方差分析和决策树算法对其进行分析。这两种技术都证实了时间作为渗硼厚度的最重要参数。对于表面硬度，时间和温度是主要影响参数。制定了机器学习中的各种回归模型来寻找变量之间的关系。在这些模型中，多层感知产生了最大的相关系数和最小的均方根误差。