Synthesis and Analysis of Quality Control Methods for Intelligent Processing of Polymeric Materials

Abstract

Global manufacturers of thermoplastic molded parts increasingly require 100% quality inspection levels that are difficult to achieve. While process complexity makes it difficult to attain the desired part properties during start-up. the stochastic nature of the process causes difficulty in maintaining part quality during production. This paper formally compares several alternative quality control methods that are currently utilized for processing of polymeric materials. To identify the technical issues associated with this goal, the injection molding process is described utilizing a control systems approach. Afterwards, four different methods of quality regulation are synthesized for injection molding: open loop quality control, statistical process control, trained parameter control, and on-line quality regression. For each strategy, the level of quality observability and controllability are determined against the dynamics of the manufacturing system. The results indicate that none of the quality regulation strategies have the underlying design architecture to deliver 100% quality assurance across a diverse set of application characteristics (quality requirements, material properties, mold geometries, and machine dynamics). As such, subsequent discussion focuses on defining the system requirements for achieving ‘intelligent’ processing of polymeric materials that are needed by industry.