Inverse Design of Manufacturing Process Chains

Abstract

Finding the input specifications to obtain the specified performance of a component being designed is an essential activity of a designer. However, obtaining solutions for this inverse problem is a complex task; especially when there are multiple steps with many-to-one mappings at each step in the forward problem. This complexity is further augmented in the presence of uncertainty of the parameters and models used. The typical heat treatment process involves multiple steps and the same outcome can possibly be achieved through multiple routes. Obtaining suitable process parameters for desired final properties such as the hardness profile is a requirement of the process design. In this work an error metric called the Hyper Dimensional Error Margin Index (HD_EMI) is used for inverse process chain design where the objective is to obtain the process set points in a sequence of heat treatment operations involving carburization, quenching and tempering processes. We have validated the solution of inverse problem by solving the detailed forward problem. The application procedure used in this study to solve inverse problem is simple and results obtained are encouraging for further exploration of HD_EMI.

Definition and Exploration of the Integrated CO2 Mineralization Technological Cycle

“…Development and implementation of ICM is akin to an inverse design problem (Kulkami et al, 2014), whose solving prerequisite is the mapping and characterization of all the links within and between the three ICM layers from Figure 1. The search for realistic solutions to the ICM inverse design problem is a general optimization problem (Eden et al, 2004).…”

Process Chain-Oriented Design Evaluation of Multi-Material Components by Knowledge-Based Engineering

“…In contrast to empirical methods for process modeling, as in Ref. [34], a pre-thinking of the solution space is required. This must be described in advance by explicit constraints.…”

“…Within the manufacturing domain, many knowledge-based approaches can be used to formalize entire process chains explicitly and in a machine-readable manner. In doing so, they are able to derive inferences with respect to a process and process chain specification [33][34][35][36][37]. However, these approaches are generally not suitable for design synthesis, where there is no feedback of analysis results into product design.…”