机器人自动焊接需要控制多个参数，多变量纠缠组合爆炸且呈非线性，搜索空间复杂导致多焊接参数难以寻找最优工艺组合，焊接平直度不足。为此，提出机器人自动化焊接参数混合迭代二分器3（Iterative Dichotomiser 3，ID3）算法优化方法。通过收集焊丝直径与电弧电压等工艺参数的焊接数据，构建数据样本集合。引入模糊理论和犹豫模糊集理论改进ID3算法，通过考虑多个隶属度函数，捕捉多变量数据中的非线性和模糊性。利用混合ID3算法构建决策树模型，计算多变量质量指标数据的信息熵和信息增益，选择对焊接质量分类结果影响最大的工艺参数作为分裂属性，缩小搜索空间，降低分类的不确定性。通过犹豫模糊决策树分析不同参数组合对焊接质量的影响，选择最优的工艺参数组合。试验证明：该方法可有效构建针对焊缝熔宽、熔深和余高3个焊接质量指标的犹豫模糊决策树，完成机器人自动化焊接参数优化；应用该方法后，可有效提升焊接平直度。
关键词：机器人；自动化；焊接工艺；参数优化；混合ID3算法；犹豫模糊集
作者：昆明冶金高等专科学校 郝中波
Research on optimization of hybrid ID3 algorithm for robot automatic welding parameters
HAO Zhongbo
Kunming Metallurgical College, Kunming 650033, Yunnan, China
Abstract: Robot automatic welding requires controlling multiple parameters, and the entanglement and combination of multiple variables explode and exhibit non-linearity. The complex search space makes it difficult to optimize multiple welding parameters, resulting in insufficient welding flatness. Therefore, a hybrid ID3 algorithm optimization method for robot automation welding process parameters is proposed. Construct a data sample set by collecting welding data on process parameters such as wire diameter and arc voltage. Introducing fuzzy theory and hesitant fuzzy set theory to improve the ID3 algorithm, capturing nonlinearity and fuzziness in multivariate data by considering multiple membership functions. Using the hybrid ID3 algorithm to construct a decision tree model, calculate the information entropy and information gain of multivariate quality index data, select the process parameters that have the greatest impact on the classification results of welding quality as splitting attributes, narrowdown the search space, and reduce classification uncertainty. Analyze the impact of different parameter combinations on welding quality through hesitant fuzzy decision tree, and select the optimal process parameter combination. Experimental results have shown that this method can effectively construct a hesitant fuzzy decision tree for three welding quality indicators: weld width, weld depth, and residual height, and optimize the welding parameters for robot automation; After applying this method, the welding flatness can be effectively improved.
Keywords: robots; automation; welding process; parameter optimization; hybrid ID3 algorithm; hesitant fuzzy set
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