On Efficient Powder Manufacturing Using Process Control Methods and Cybernetics: A Background, Results and Policy Case Study

Abstract

Powder manufacturing processes involve a systematic processing of particulate material towards forming a final product which needs to meet specific quality standards. A means towards ensuring that standards are met in a manufacturing plant is to apply self-regulating intelligent machinery that can take mitigatory actions in the presence of a process deviation or unsatisfactory process behavior. Given a case study of an open-loop batch powder agglomeration process, this work synthesizes the process of how a feedback element can be applied towards the powder manufacturing plant in order to allow for self-regulation and process optimization which, in turn, would increase process efficiency, minimize waste and ensure that the overall product quality meets production targets. Focusing on a proposed cybernetic loop with a feedback element, a vital component is the ability to measure key quality attributes of the powder mixture, which is crucial towards ensuring the control algorithm is able to implement the most reflective and appropriate control strategy on the powder process; thus, this work discusses heavily the sensing and associated signal processing component of the cybernetic loop. The results from the reviewed signal processing scenarios which comprised the in-process material spanned estimation accuracies of 3–10%, depending on the scenario that was considered. This paper also addresses the feared dystopian view of mass redundancy and job losses in the age of the introduction of enhanced automation technology within a manufacturing process, where it is distilled what kinds of knock-on effects can be expected as a result of the technology, in addition to means by which policy makers can contribute towards ensuring that the adaptation and transition process is as smooth as possible.