基于原料特征的数字化酸洗分级模型构建
Construction of Pickling Process Model Based on Strip Characteristics
本文通过对实验钢表面氧化铁皮的微观形貌及物相组成的分析,基于酸洗失重和酸洗行为明确影响酸洗效率的主要因素,结合生产实践建立基于原料特征的酸洗分级数学模型。结果表明硅含量较高且卷取温度较高的带钢表面氧化铁皮厚度较高,同时在基体附近存在以硅元素为主的合金富集层,该富集层存在明显的对基体的钉扎作用,结合酸洗失重分析明确氧化铁皮厚度、物相组成和硅在基体附近的富集是影响酸洗效率的主要因素。以卷取温度和硅含量为变量建立酸洗分级数学模型,以模型计算值为酸洗难度评价标准并匹配相应的酸洗工艺数据库,实现对不同酸洗难度的产品进行精细化酸洗工艺匹配,应用后酸洗效果良好,推进酸洗技术向工业智能化及数字孪生方向转型。
In this paper,the morphology and phase composition of the oxide layer were analyzed,the main factors affecting the pickling efficiency were identified by the weight loss experiment and pickling behavior analysis,and the pickling process model based on oxide layer characteristics was established.The results show that the thickness increase as the silicon content and coiling temperature increase,there is an alloy enrichment layer mainly composed of Si near the strip,it has an obvious pinning effect on the strip.The phase composition and the enrichment of silicon near the strip are the main factors affecting the pickling efficiency.A pickling classification model is established with the coiling temperature and silicon content as variables.The calculated values of the model are used as the criteria to match the pickling process database.The pickling process matches well after the application of pickling classification model,the surface quality after pickling is good and this model contributes to the intelligence and digital twin application of pickling technology.
蔡顺达;孙荣生;刘军友;王金星;宋利伟;邹明聪;
海洋装备用金属材料及其应用国家重点实验室,辽宁鞍山,114009##鞍钢集团钢铁研究院,辽宁鞍山 114009;海洋装备用金属材料及其应用国家重点实验室,辽宁鞍山,114009##鞍钢集团钢铁研究院,辽宁鞍山 114009;鞍钢股份有限公司冷轧厂,辽宁 鞍山 114021;鞍钢股份有限公司冷轧厂,辽宁 鞍山 114021;海洋装备用金属材料及其应用国家重点实验室,辽宁鞍山,114009##鞍钢集团钢铁研究院,辽宁鞍山 114009;海洋装备用金属材料及其应用国家重点实验室,辽宁鞍山,114009##鞍钢集团钢铁研究院,辽宁鞍山 114009;
热轧表面氧化铁皮 合金富集层 酸洗分级 酸洗工艺匹配 数学模型
Hot rolled surface oxide layer Alloy enrichment layer Pickling classification Pickling process matching Mathematical model
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