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MANUFACTURING COST REDUCTION THROUGH THE USE OF PREDICTIVE CONTROL TECHNIQUES FOR SMES IN DISCONTINUOUS MANUFACTURE OF POLYMER AND TEXTILE PRODUCTS

Ziel


Project objectives were to create machine and plant control systems for small and medium enterprises (SME) in the injection moulding and textile production industries. These controls would be applied to noncontinuous production and help reduce costs. The project was designed to improve control in 2 ways: at the machine level where product data were used to adjust machine settings and at plant level where algorithms were to be produced for scheduling work.
A model for part of the injection moulding process was developed. It uses machine settings as functional inputs and selected part qualities as functional outputs. The data acquisition system that will accept inputs and apply outputs to the injection moulding machine has also been developed and installed.
A control structure that uses manipulated inputs, disturbance variables, cascaded controllers and intermediate variables has been designed. This control algorithm is being validated.
Additionally, a workshop resource controller has been developed. It attempts to load a workshop in the most cost effective manner, while respecting the constraints of available resources. A real time data acquisition system, which will supply essential data to the workshop controller, has been developed and installed.
A warning detection system that will predict future plant performance based on the current flow rates and buffer stock levels has been prototyped and is currently being developed.
THE PROJECT OBJECTIVE IS TO REDUCE PRODUCTION COSTS IN SMALL MANUFACTURING ENTERPRISES (SMES) MANUFACTURING POLYMER AND TEXILES BY APPLYING PREDICTIVE CONTROL TECHNIQUES TO DISCONTINUOUS OR DISCRETE PART MANUFACTURING PROCESSES.SUCH TECHNIQUES ARE CURRENTLY WELL ESTABLISHED IN CONTINUOUS PROCESS INDUSTRIES BUT IN THEIR INFANCY IN DISCONTINUOUS MANUFACTURING.

THE AIM OF PREDICTIVE CONTROL IS TO USE REAL TIME INFORMATION TO CONTINUOUSLY FEED A MODEL OF THE PROCESS IN ORDER TO PREDICT IT'S FUTURE STATE. KNOWLEDGE OF A PROCESS'FUTURE STATE ALLOWS THE APPROPRIATE CONTROL STRATEGY TO BE ADOPTED IN ORDER TO REDUCE COSTS.

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