An IIoT machine model for achieving consistency in product quality in manufacturing plants

TL;DR

This paper introduces an IIoT-based machine model that leverages data-driven algorithms for real-time monitoring and control to ensure consistent product quality in manufacturing plants.

Contribution

It presents a novel IIoT framework with algorithms for predicting product quality and recommending machine control actions, validated with real industrial data.

Findings

High accuracy in product quality prediction

Effective real-time monitoring and control

Potential for reducing manual intervention

Abstract

Consistency in product quality is of critical importance in manufacturing. However, achieving a target product quality typically involves balancing a large number of manufacturing attributes. Existing manufacturing practices for dealing with such complexity are driven largely based on human knowledge and experience. The prevalence of manual intervention makes it difficult to perfect manufacturing practices, underscoring the need for a data-driven solution. In this paper, we present an Industrial Internet of Things (IIoT) machine model which enables effective monitoring and control of plant machinery so as to achieve consistency in product quality. We present algorithms that can provide product quality prediction during production, and provide recommendations for machine control. Subsequently, we perform an experimental evaluation of the proposed solution using real data captured from a food processing plant. We show that the proposed algorithms can be used to predict product quality with a high degree of accuracy, thereby enabling effective production monitoring and control.