Inconel-800 is a superalloy having moderate strength, sufficient resistance to oxidation at high temperature and carburization. Work hardening nature of the material makes it very hard to machine using traditional methods. Wire-cut electrical discharge machining (WEDM) is most widely applied for difficult-to-machine materials because of its capability to produce jobs with minute accuracy and precision. Time taken to process job is the major drawback of the WEDM technique. Therefore, the need arose for formulating a stable predictive model which reduces human effort and maximizes production. The comprehensive intention of this study is to develop robust and stable multi-objective decision-making and soft computing models for selecting the best machining parameters and forecasting the machining performance of Inconel-800 superalloy. Taguchi’s method is applied for designing the experimental phase of the study. The computational part is a three-stage process. The first stage is selecting the best machining parameters of WEDM by multi-criteria decision-making (MCDM) technique. A novel MCDM technique is proposed that is based on the concept of risk minimization. The second stage involves the formulation of predictive model using soft computing technique like response surface methodology (RSM). The performance of the technique was scrutinized on a statistical platform to check the gullibility of the model. The final stage is the trade-off analysis by multi-objective RSM (MORSM) which strategically draws balance among the multiple outputs of the problem. Finally, the proposed MCDM and MORSM models are applied for selecting the best and optimal combination of machining Inconel-800 by WEDM technique.

Inconel-800是一种具有中等强度，在高温下具有足够的抗氧化性和渗碳性的高温合金。材料的加工硬化性质使其很难使用传统方法进行机加工。线切割放电加工（WEDM）由于其能够以微小的精度和精度来生产工件，因此最广泛地应用于难加工的材料。处理工作所花费的时间是WEDM技术的主要缺点。因此，需要建立一种稳定的预测模型，该模型可以减少人的工作量并使产量最大化。本研究的总体目的是开发健壮且稳定的多目标决策和软计算模型，以选择最佳的加工参数并预测Inconel-800高温合金的加工性能。Taguchi的方法用于设计研究的实验阶段。计算部分是一个三阶段过程。第一阶段是通过多准则决策（MCDM）技术选择WEDM的最佳加工参数。提出了一种基于风险最小化概念的新型MCDM技术。第二阶段涉及使用诸如响应面方法（RSM）的软计算技术来制定预测模型。在统计平台上仔细检查了该技术的性能，以检查模型的信度。最后阶段是通过多目标RSM（MORSM）进行权衡分析，该策略从战略上在问题的多个输出之间取得平衡。最后，