Experimental Investigation of the Implications of Model Granularity for Design Process Simulation

Abstract

Determining a suitable level of description, or granularity, for a product or process model is not straightforward, especially since granularity can manifest in multiple ways, but it is important to capture important elements in the model without building models that are too large to understand. This article investigates the implications of model granularity choices by simulating the design process of a diesel engine on different levels of detail, comparing the results and exploring ways to account for the differences. It uses two Design Structure Matrix (DSM) models for change prediction in a diesel engine at different levels of granularity to run simulations of the design process. Changes are a major source of rework and lead to frequent rescheduling of design tasks. The incremental nature of product development as well as design changes and their propagation complicate design process planning further. Process simulation may provide support in such contexts when it is based on an appropriate description of the product. The article shows that while coarse models can give an indication of likely process behavior, they miss potentially significant iteration loops.

A dynamic model for engineering change propagations in multiple product development stages

“…Karniel and Reich demonstrated that the logic differences between process models required the simulation-based decision-making to select correct concurrent process models according to the specific process attributes (Karniel and Reich, 2009). To investigate the implications of granularity on the outcomes of process models, the simulation-based method was adopted to compare the results and explore ways to account for the differences by modeling the product development process as design structure matrices (DSMs) at different granularity levels (Maier et al , 2019). Suzuki et al presented a simulation-based process modeling tool to identify the fragility of product-based design processes and the tolerance of function-based design processes to overload situations (Suzuki et al , 2012).…”

“…Due to the space constraint, the figures showing optimization processes are not included. Sequential and concurrent change propagation simulations are both adopted to compare their different change propagation dynamics (Barthelemy et al , 2005; Karniel and Reich, 2009; Suzuki et al , 2012; Maier et al , 2019). In the simulations, the initial change effect for initiated changes and the threshold value for propagations are assigned with 0.6 and 0.01, respectively.…”

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization

“…During this time, the approaches to creating digital models and digital twins of complex multiscale systems have proven their effectiveness [19][20][21]. The key factors in the multiscale systems of digital model design are reliability, determined by validation and verification [22]; the definition of valid interfaces and links to the experimental process [23]; the determination of the final model's details at the stage of formalizing the complex system's representation [24].…”