Smart Manufacturing: Quality Control Perspectives

Abstract

Quality Control (QC) is a guideline or set of procedures intended to ensure that a manufactured product or performed service adheres to a defined set of quality criteria or meets the requirements of the client or customer. Smart manufacturing is where the work is interfaced work pieces and associated tools that include logistics operations, Cyber Physical Systems, Artificial Intelligence, and Big Data Analytic tools. These form the norm of manufacturing operations to generate large amounts of data, which are used for analysis and prediction. Therefore, help to optimise the quality of manufacturing operations and manufactured products. The change in technologies have, however, altered the traditional way of manufacturing process as well as QC systems. Therefore, to address the challenge of data reliability, the sensors, actuators and instruments used at various levels of integration in the manufacturing process often operating under adverse physical conditions need to provide adequate levels of data accuracy and precision. Methodologically, the Chapter followed critical literature review on QC concepts and Industry 4.0 revolution, thereby culminating into conceptual framework of QC in Smart Manufacturing, which is the main contribution of this Chapter.

Blockchain-Enabled Open Quality System for Smart Manufacturing: Applications and Challenges

“…The fourth industrial revolution, where there are lots of development in many sectors, quality management have also made advances through the use of smart devices that are linked to internal and external networks of data, i.e., Industrial Internet of Things (IIoT) that works automatically without human intervention. The technologies of this modern era are capable of communication both with the products (smart products) and their environment, and, thus, can notice any defect or delay that could impede the manufacturing processes [14][15][16]. Vertical integration in a smart manufacturing environment allows transferring control of quality and data diagnostics from the shop floor straight to each level of decision making and vice versa, which makes the products traceable and the error visible to the next process [17].…”

Using deep learning for quality control in cyber-manufacturing

“…A significant capability-big data analytics-is demanded in cyber manufacturing, which helps to make intelligent decisions by discovering hidden patterns, unknown correlations, or other valuable information on large amounts of data coming from various types of resources. Therefore, intelligent quality control systems are anticipated, where automation takes over most of the operations or tasks that were routine tasks performed by human [15]. They should detect any slight defects and delays that could hamper manufacturing processes and then communicate the same to the shop floor, using fitted sensors [16].…”