## 1 Frame of Reference
Through this work, a contribution is put forward to provide designers with an effective platform to make decisions in the \"what if\" scenarios to achieve satisficing solutions among the conflicting goals/requirements. To achieve this, an attempt is made to take advantage of the accuracy and precision in the prediction confidence of Artificial Neural Network (ANN) to address the issues discussed above. ANN-based models have the capability to learn any kind of functional relationship from experimental data within an arbitrary degree of accuracy, and this capability is not limited to a particular set or class of functional relationships. ANN-based models can steer engineering design decisions if it successfully and efficiently learns functional relationships during training and development using experimental data. Keeping in view the requirements discussed above, the primary contribution of this work is an inverse, goal-oriented method supported by a _Design Space Exploration Frame work_ (DSEF). DSEF is further supported by the ANN-based prediction model, which is capable of capturing the complex, non-linear functional relationships in all spaces of the PSPP, i.e., _process-structure_ (PS), _structure-property_ (SP) and _property-performance_ (PP). It starts directly from the end requirements and traces back the PSPP spaces in the inverse direction. The prediction model considers mechanical and thermal information successfully to predict phase distribution, microstructure, and end properties. For ANN-based prediction model development, due to the small number of data points in the datasets, a statistical technique (\(k-fold\ cross\ validation\)) has been utilized for optimal utilization of the data [49; 50; 51; 52; 53; 54; 55], \(k-fold\ cross\ validation\) helped us develop a robust ANN model to avoid the issue of overfitting, and it also helped us to explore different network topologies for our prediction model. \(k-fold\ cross\ validation\) helped successfully encapsulate the localized variations in the prediction capability of the model in the experimental space and in PSPP spaces. It allowed us to generate customized bars for uncertainty in the predictions made by the model. After developing the prediction model, its capability to make accurate predictions was rigorously measured and analyzed by exposing the prediction model to completely new and independent data. The efficacy of the method is illustrated by exploring the design/solution of all the PSPP spaces of the hot stamping process. We illustrate in this work that ANN-based methods can provide a better platform for making decisions in the chain of PSPP and relieving the designers of the hectic efforts to integrate the incomplete, inaccurate, and infield physical and empirical models that cause MPU, MSU, and PU in decision workflows. The ML-based prediction model can help designers to timely address the variations at every decision node in the design process. The outline of the paper is as follows. Section 2 presents a brief overview of the hot stamping process. Section 3 presents a description of the Design Space Exploration Framework (DSEF). Section 4 presents the ANN-based prediction model development and its validation. Section 5 presents an inverse goal-oriented design method for hot stamping. Section 6 presents the implementation of the proposed method and solution space exploration. Section 7 concludes our work with closing remarks.