Project based at
Lead Partner Organisation TBC
Quality assurance is a vital component of manufacturing processes that eliminates product flaws and offers outstanding goods that meet the end consumer’s criteria. This Project would like to identify and fill up the gap in the intersections between Human factors in a collaborative manufacturing workplace and how that will support the industry to make sure that the quality is assured and how interpreted data can be used to evaluate compliance requirements, especially for the highly regulated industries. Theoretically, this research is expected to recommend a framework and guidelines for the current literature and body of knowledge to fill up the missing link between the COBOT manufacturing process and business integration. To shed light on this the proposed framework will be tested in Business and the quantitative research method will be used to validate the data into business usable outputs to address this gap, this study will evaluate possible technical solutions which need to be implemented. Furthermore, gaps will be identified from viewpoints of industrial requirements and the state-of-art research for COBOT programming to ensure the quality of multi-sensor data.
Manufacturing settings tend to be defined by a high degree of uncertainty; hence, defects are unavoidable. When non-conformance to product standards is identified, defects “occur.” The only action that can be taken is to minimize flaws as much as possible without sacrificing system performance. Zero-Defect refers to the number of faults at the conclusion of a product’s production process or its manufacturing life. In this project, we aim to present comprehensive guidelines for incorporating human factors into quality assurance in the era of COBOT-based manufacturing, as well as to modify ZDM theory, which can be used to model the quality-checking process, in order to compare the efficacy of manual quality assurance, Robotic quality assurance, and COBOT quality assurance. To assure the reproducibility and openness of the review process, a thorough systematic literature review (SLR) was conducted. The final model is then extracted using the opinions of experts and several filtration processes, which will be described in the article.
Principal Supervisor: Dr Lee Clemon