Abstract
With growing demands on quality of produced parts, concepts like zero-defect manufacturing are gaining increasing importance. As one of the means to achieve this, industries strive to attain the ability to control product/process parameters through connected manufacturing technologies and model-based control systems that utilize process/machine data for predicting optimum system conditions without human intervention. Present work demonstrates an automated approach to process in-line measured data of tribology conditions and incorporate it within sheet metal forming (SMF) simulations to enhance the prediction accuracy while reducing overall modelling effort. The automated procedure is realized using a client-server model with an in-house developed application as the server and numerical computing platform/commercial CAD software as clients. Firstly, the server launches the computing platform for processing measured data from the production line. Based on this analysis, the client then executes CAD software for modifying the blank model thereby enabling assignment of localized friction conditions. Finally, the modified blank geometry and accompanied friction values is incorporated into SMF simulations. The presented procedure reduces time required for setting up SMF simulations as well as improves the prediction accuracy. In addition to outlining suggestions for future work, paper concludes by discussing the importance of the presented procedure and its significance in the context of Industry 4.0.
Highlights
Manufacturing practices are rapidly evolving with continual advancements in material sciences, production and information technology [1]
A control system consists of a sensor that collects data, a prediction model that can predict appropriate future state of the production system based on its current state and a controller that proposes required change to the production system to get product properties closer to the desired specifications [2]
The efficiency of a control system depends on several factors such as the measuring capability of the sensor, production systems response to change and robustness of prediction mechanism
Summary
Manufacturing practices are rapidly evolving with continual advancements in material sciences, production and information technology [1]. SMF is a complex nonlinear process with changing product/process conditions such as die deformations, tooling temperatures, material scatter, lubrication amounts and sheet mechanical properties [4] Due to such variations, it is difficult to guarantee the quality of a manufactured part a lot of new concepts endeavor on monitoring and controlling the production process [5]. This research intends to lower the threshold for building data-based simulation models and the time consumed for the same through a preliminary case study For this purpose, the case study has chosen to use pre-lube lubrication data on the blanks. The variation could be due to several factors such as transportation of the coil, coiling/uncoiling, contact between the sheet surfaces when the coil is rolled This variation is not accounted within SMF simulations at Volvo Cars today with a uniform lubricant amount assigned to the entire blank. The following section presents a detailed description of the proposed automated procedure
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