Cognitive Manufacturing in Industry 4.0 toward Cognitive Load Reduction: A Conceptual Framework

Abstract

Cognitive manufacturing utilizes cognitive computing, the industrial Internet of things (IoT), and advanced analytics to upgrade manufacturing processes in manners that were not previously conceivable. It enables associations to improve major business measurements, for example, productivity, product reliability, quality, and safety, while decreasing downtime and lowering costs. Considering all the facts that can prejudice the manufacturing performance in Industry 4.0, the cognitive load has received more attention, since it was previously neglected with respect to manufacturing industries. This paper aims to investigate what causes cognitive load reduction in manufacturing environments, i.e., human–computer interaction technologies that reduce the identified causes and the applications of cognitive manufacturing that use the referred technologies. Thus, a conceptual framework that links cognitive manufacturing to a reduction of the cognitive load was developed.

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

“…In industry 4.0 real scenarios, the operator may be subject to task changes, task difficulty, task shared with a robot, and interruptions (for instance, noise) [35,36]. In these cases, understanding the cognitive load types (intrinsic, extraneous, germane) and the relationship with mental workload could be useful for the improvement of the human information processing system, human performance and the effectiveness of the overall system [37,38].…”

Efficient Practices of Cognitive Technology Application for Smart Manufacturing

“…It robotizes reactions towards its findings and offers practical information being able to steadily deliver updated knowledge to decision-makers. Cognitive manufacturing covers four powerful applications, which are reliability and performance management or asset performance management (APM), process and quality improvement, optimization of resources, and supply chain optimization [10,21,22]. These applications are presented in Table 1 encompassing the information about the categories of cognitive technologies and gains of manufacturers embedding technological solutions.…”

Human–Machine Integration in Processes within Industry 4.0 Management

“…Extensive and intensive research has shown that on the one hand, humans in shop-floor management environments in I4.0, have a holistic problem-solving capability where several brain areas are activated for problem solving [ 44 , 45 , 46 ], however humans have a limit to the cognitive load they can compute that affects their performance [ 47 , 48 ]. On the other hand, with the advent of artificial intelligence, machines are increasingly capable of performing a massive processing of information that can make up for human deficiencies: one approach is to use the machine, having greater computational capacity to reduce the cognitive load of humans [ 49 , 50 , 51 ], another approach has been to create a semantic framework that allows for machine recommendations for human problem-solving related to manufacturing tasks [ 52 , 53 ], while other scholars have proposed an open source web-based protocol to enhance inter-operability between human and machine assets [ 54 ]. The problem with all these proposals can be summarized in the fact that they try to adapt either the machine to man or vice versa, and as a natural consequence, they obviate a symbiosis between both forms of information computation.…”