Development of a knowledge-based predictive model to estimate the welding process time in single part production systems

The lack of repetition effect in the single part production in metal industry limits an exact determination of the necessary process parameters (e.g. welding time) for production planning and furthermore restricts the application of production planning systems. This work discusses a methodology to improve the prediction accuracy of welding time in single part production systems. In order to determine the actual process parameters of simple welding process characteristic indicators are identified. These indicators are stored as process features and used during the process planning phase. To achieve this target, the configuration and integration of a Product Data Management (PDM) and a Business Intelligence System is necessary. In the case of simple welding processes these indicators are calculated through mathematical formulas, which are developed on the basis of welding parameters. But this methodology can't be used for complex welding processes with many influence factors. For this kind of processes other prediction models are developed on the basis of analytics methodology. After validation of these models, they are integrated in a data warehouse system and work automatically within a knowledge-based circuit. The accuracy of the indicators continuously improves through newly acquired data (learning effect). This methodology supports single part producers to improve their production planning quality in metal industry. (C) 2016 The Authors. Published by Elsevier B.V.