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Process monitoring and data mining for quality control are insufficient in modern manufacturing as they lack the capability to anticipate defects before they occur. This research aims to develop a novel closed-loop quality control methodology which links defect identification with root cause analysis and corrective action for assembly systems.

It is based on the integration of in-process measurement and data mining with multi-physics variation simulation analysis through the development of simulation-driven surrogate models and closed-loop control strategy.

Duration: 5-6 years, Budget: £ 2,002,994

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Project Poster

Industrial & Research Objectives

The overall goal is to develop, implement, demonstrate, and pilot in the industry, a systematic closed-loop quality improvement methodology with in-process adaptive monitoring capable of self-recovery from 6-sigma quality defects via:

(i) defects detection,

(ii) root cause analysis; and,

(iii) corrective actions and preventive actions (CAPA) synthesis.


The broad impact of the project is the integration of reconfigurable assembly systems with closed-loop in-process quality control. The results of the project can help to eliminate, reduce and correct defects before they occur. This will lead to increased productivity and product quality and shortened design phase.


The proposed approach comprises of three major thrusts:

(i) In-process adaptive measurement system

(ii) root cause analysis with corrective and preventive actions

(iii) multi-physics variation simulation

These three thrusts are integrated into three interlinked closed-loops:

(i) feedback-to-design in closed-loop (CL1)

(ii) real-time quality control in closed-loop (CL2)

(iii) real-time measurement control in closed-loop (CL3)


Figure 1. Closed-loop quality improvement methodology with in-process adaptive measurement

(i) In-process adaptive measurement is achieved through Optimal Coverage Path Planning and Spatio-Temporal Adaptive Measurement
Figure 2: Optimal Coverage Path Planning

Figure 3:Spatio-Temporal Adaptive Measurement

(ii) root cause analysis with corrective and preventive actions is achieved through an integration of multi-physics simulation and artificial intelligence techniques such as 3D Deep Convolutional Neural Networks
Figure 4:Root Cause Analysis using Deep Learning



To aid research, implementation across industry and university and reproducibility of results the research has been implemented as open-source toolboxes within Matlab and Python.

Industrial use-cases

Implementation in Measurement Stations

Figure 5: Adaptive In-Line Measurement System Using Blue Light Optical Gage

Figure 6: Adaptive In-Line Measurement System Using Blue Light Optical Gage

Implementation in Laser Welding
It is planned to develop both physical and virtual demonstrators. It is intended to demonstrate the technology in both (i) automotive industry and (ii) aerospace
Figure 7. Example of in-process adaptive quality improvement for remote laser welding assembly system

Figure 8. Examples of in-process adaptive measurement stations