Processing of soft matter and composites: integration of material sensors with process models and intelligent control algorithms
This paper describes the development of an intelligent process-control system for the production of fiber-reinforced organic polymeric composites. The composite material consists of a polymer matrix (polyamide resin F174) and high-modulus quartz-fiber reinforcement. This composite material has good mechanical properties at high temperatures, and possesses a low dielectric constant, making it suitable for applications in missile radomes. The problem is that the raw materials are chemically reactive, and the process-control system must enable adaptation to variations in the temperature-time exposure of the raw materials and/or variations which may occur in the materials received from different suppliers. The uniqueness of the control system lies in that it is self-directing, and relies on information derived from sensors (laser fiber-optic probes and dielectric sensors) placed within the material. In addition, a materials-transformation model based on the chemical kinetics of the polymerization process calculates a number of key polymer parameters, such as degree of imidization, degree of cure, molecular weight distribution, and polydispersity ratio in situ. The collective ability to collect high-quality sensor information, to run sophisticated but robust process models in real time, to make complex decisions using artificial intelligence (AI), and to implement these decisions for controlling the structure of the actual material being processed represents a significant breakthrough in materials and process capability in this field. The focus of this work, measuring and controlling the physical and chemical properties of the material, rather than the physical attributes of the processing machinery, is an important paradigm shift.
artificial intelligence spectroscopic sensors chemical kinetic and transport models process control object-oriented programming