Maraging steel is a special alloy exhibiting excellent combination of ultra-high strength with considerable ductility. Thus, such steel become significant worldwide for strategic sectors (like nuclear, aerospace and defence) where stringent quality standards with respect to chemistry and properties is compulsory. Manufacturing of these steel is extremely difficult, and vacuum induction melting (VIM) furnace plays most significant part as a primary melting unit. There are a large number of processing parameters to manufacture these special alloys and on top of that, these type of vacuum furnaces are connected with various valves, motors, sensors along with safety systems, which lead to involvement of additional interdependent process variables. Accordingly, building logical agent is often difficult because the developer requires possessing the complete and intricate knowledge of all the agents, which is viable only for deterministic environment. In case of nonlinear condition, the data generation and collection in digital form is a very useful resource of valuable information for meaningful process optimization-related investigation by developing intelligent model from databases. In this perspective, data-driven optimization using evolutionary algorithms is effective tool in order to optimize parameters related to melting time of manufacturing maraging steel through critical analysis of the data. Data of 130 heats collected from VIM 6.5 Tonne furnace and the considered parameters are weight of carbon added, leak rate of system, complete meltdown time, carbon–oxygen–nitrogen pct in opening sample, carbon–oxygen–nitrogen pct in final stages of melting, refining time, leak rate during melting, tapping temperature, melt duration and tap to start time between previous and present melting and lining life. Out of aforementioned fifteen parameters, minimization of refining time and melt duration is the objectives of the present investigation. Evolutionary neural network has been used as a primary optimization algorithm for present investigation which is coupled with predator–prey genetic algorithm (PPGA). Optimization using PPGA has been achieved successfully and obtained the trade-off between refining time and melt duration. The analyses of all input parameters reveal that the complete meltdown time and carbon addition at start are the two main parameters which can affect the melting and refining time greatly. Putting extra care on these two parameters will greatly affect the melting time of maraging steel in VIM furnace.

马氏体时效钢是一种特殊合金，具有超高强度与可延展性的出色结合。因此，这种钢在战略领域（如核能，航空航天和国防）在世界范围内变得举足轻重，而这些领域在化学和性能方面都必须遵守严格的质量标准。这些钢的制造极为困难，真空感应熔炼（VIM）炉作为主要的熔炼单元发挥着最重要的作用。制造这些特殊合金的工艺参数很多，最重要的是，这些类型的真空炉与各种阀门，电机，传感器以及安全系统相连，这导致涉及其他相互依赖的工艺变量。因此，建立逻辑代理通常很困难，因为开发人员需要拥有所有代理的完整而复杂的知识，这仅对确定性环境可行。在非线性条件下，通过从数据库开发智能模型，数字形式的数据生成和收集是非常有用的有价值信息资源，可用于有意义的过程优化相关研究。从这个角度来看，使用进化算法进行数据驱动的优化是有效的工具，可以通过对数据进行严格分析来优化与制造马氏体时效钢的熔化时间相关的参数。从6.5吨VIM炉中收集的130炉热量的数据，考虑的参数包括添加的碳的重量，系统的泄漏率，完全熔化的时间，打开样品中的碳-氧-氮pct，碳-氧-氮pct在熔融的最后阶段，精炼时间，熔融过程中的泄漏率，出钢温度，熔融持续时间和从开始到现在的熔融和衬里寿命之间的开始时间。在上述15个参数中，精炼时间和熔体持续时间的最小化是本研究的目标。进化神经网络已被用作本研究的主要优化算法，并与捕食者－猎物遗传算法（PPGA）相结合。使用PPGA进行的优化已成功实现，并在精炼时间和熔融持续时间之间取得了平衡。对所有输入参数的分析表明，完整的熔化时间和开始时的碳添加是可以极大地影响熔化和精炼时间的两个主要参数。