Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form Surfaces

Abstract

Deep learning has demonstrated high accuracy for 3D object shape error modeling necessary to estimate dimensional and geometric quality defects in multi-station assembly systems (MAS). Increasingly, deep learning-driven Root Cause Analysis (RCA) is used for decision-making when planning corrective action of quality defects. However, given the current absence of scalability enabling models, training deep learning models for each individual MAS is exceedingly time-consuming as it requires large amounts of labelled data and multiple computational cycles. Additionally, understanding and interpreting how deep learning produces final predictions while quantifying various uncertainties also remains a fundamental challenge. In an effort to address these gaps, a novel closed-loop in-process (CLIP) diagnostic framework underpinned algorithm portfolio is proposed which simultaneously enhances scalability and interpretability of the current Bayesian deep learning approach, Object Shape Error Response (OSER), to isolate root cause(s) of quality defects in MAS. The OSER-MAS leverages a Bayesian 3D U-Net architecture integrated with Computer-Aided Engineering simulations to estimate root causes. The CLIP diagnostic framework shortens OSER-MAS model training time by developing: (i) closed-loop training to enable faster convergence for a single MAS by leveraging uncertainty estimates of the Bayesian 3D U-net model; and, (ii) transfer/continual learning-based scalability model to transmit meta-knowledge from the trained model to a new MAS resulting in convergence using comparatively less training samples. Additionally, CLIP increases the transparency for quality-related root cause predictions by developing interpretability model which is based on 3D Gradient-based Class Activation Maps (3D Grad-CAMs) and entails: (a) linking elements of MAS model with functional elements of the U-Net architecture; and, (b) relating features extracted by the architecture with elements of the MAS model and further with the object shape error patterns for root cause(s) that occur in MAS. Benchmarking studies are conducted using six automotive-MAS with varying complexities. Results highlight a reduction in training samples of up to 56% with a loss in performance of up to 2.1%.

A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

“…This step has a direct impact on learning-driven solution design. Performance targets such as accuracy, interpretability, scalability, training and prediction duration are determined depending on the proposed system [52]- [54]. Some learning models, mostly based on deep learning, are not obvious in terms of model transparency and functionality.…”

Development of a neural network structure for identifying begin-end points in the assembly process

“…Wang et al [5] proposed deep learning as a method for supporting visual observation of human workers' movements. Ceglarek et al [6] propose a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of the assembled products and [7] use deep learning for 3D object shape error modelling and estimate dimensional and geometric quality defects in multi-station assembly systems. Image based robot manipulator control system was developed by Copot et al [8], and particularly visual control robot manipulator using image moments.…”

Understanding Overlap in Automatic Root Cause Analysis in Manufacturing Using Causal Inference

“…Manufacturing is highly competitive (Choudhary et al 2009) and managing manufacturing operations can be very complex. This complexity is increasing (Dong et al 2019) as new measures are adopted that lead to a data-intensive environment (Abdelrahman and Keikhosrokiani 2020;Sinha et al 2021). Manufacturing companies should solve their operational problems efficiently and permanently in order to remain competitive.…”

“…To increase the efficacy and the efficiency of RCA, several studies developed solutions that take advantage of the increasing volume of data generated in manufacturing environments (Choudhary et al 2009;Sinha et al 2021). These solutions, named Automatic Root Cause Analysis (ARCA), use data mining and machine learning algorithms to automatically search for patterns in data that allow analysts to detect the root causes of problems more efficiently.…”