The broader impact/commercial potential of this project is that it addresses the injection molding industry which generates over $100 billion in revenue in the US alone. The proposed multivariate shrinkage sensor promises a leap forward from the current use of in-mold pressure/temperature sensors for indirect estimation of molded part dimensions and quality. Direct measurement of the shrinkage along with pressure and temperature in situ from the proposed sensor will enable improved process and quality control methods for injection molding which will eliminate the burden of frequent post-molding inspections and allow fully automated manufacturing. As a result, over 30 % of manufacturing cost and time will be saved while achieving 100% quality assurance even for the parts with tighter specifications. The proposed sensor will help heavily regulated industries such as medical devices and automobile manufacturing in achieving significant time reduction for process set-up and stabilization, and improvement in parts quality while meeting stringent regulatory requirements. Each percentage point improvement in productivity due to cycle time reductions, yield improvements, and related automation corresponds to cost savings of more than $1 billion per year in the United States alone while reducing the energy consumption and environmental waste related to molding, discarding, and recycling of defective plastic parts.This Small Business Innovation Research Phase I project provides a creative and original concept: a multivariate design for measuring shrinkage along with pressure and temperatures via a conventionally packaged device. Furthermore, knowledge and understanding are expected to be advanced in the development and validation of improved on-line models for estimating molded part shrinkage in real-time. Previous research did not have access to in-mold shrinkage data and so propagated faulty initial conditions throughout the shrinkage analysis. Due to the lack of direct observability and controllability of the molded part dimensions, most molders rely on the use of cavity pressure traces or part weight measurements as estimators of the part dimensions. However, there still is no technology or research available to monitor and predict the shrinkage on-line during production to improve, to optimize and to control parts quality and processing parameters without using expensive instrumentation. The outcome of this Phase I project will fill this gap and develop a fully functional multivariate shrinkage sensor which will enable measurement of molded product dimensions in situ prior to ejection from the mold.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
