An industrial process may operate over a range of conditions to produce different grades of product. With a data-based model, as conditions change, a different process model must be developed. Adapting existing process models can allow using fewer experiments for the development of a new process model, resulting in a saving of time, cost, and effort. Process similarity is defined and classified based on process representation. A model migration strategy is proposed for one type of process similarity, family similarity, which involves developing a new process model by taking advantage of an existing base model, and process attribute information. A model predicting meltflow-length in injection molding is developed and tested as an example and shown to give satisfactory results. V

In the processing industries, operating conditions change to meet the requirements of the market and customers. For example, in injection molding, the same machine may be used with different molds and materials to make different parts. For each different mold, material, and machine combination, data-based process modeling must be repeated for the development of a new prediction model. This may involve the repetition of a large number of experiments, if common process characteristics are ignored. Obviously, this is inefficient and uneconomical. Although operating conditions may be different for different processes, certain process behaviors and characteristics are common under these conditions. The effective use and extraction of these common process behaviors and characteristics can allow fewer experiments for the development of new process model, resulting in a savings of time, cost, and effort. With this as the key objective, a modeling method is proposed for process modeling. This includes information extraction from a base model, a design of experiments (DOE) for the new process, assessment of difference, model migration strategy, and verification for the new model. As an illustrative example, this paper demonstrates model development for new molds to predict the width of an injection-molded part, taking advantage of an existing model.

In the processing industries, operating conditions change to meet the requirements of the market and customers. Under different operating conditions, data-based process modeling must be repeated for the development of a new process model. Obviously, this is inefficient and uneconomical. Effective use and adaptation of the existing process model can reduce the number of experiments in the development of a new process model, resulting in savings of time, cost, and effort. In this paper, a particular process similarity, inclusive similarity, is discussed in detail. A model migration strategy for processes with this type of similarity is developed to model a new process by taking advantage of existing models and data from the new process. The new model is built by aggregating the existing models using a bagging algorithm. As an illustrated example, the development of a new soft-sensor model for online prediction of melt-flow length for new mold geometry for an injection molding process is demonstrated by taking advantage of existing models for different molds.