Deep learning in manufacturing predicting and preventing manufacturing defects - Sumit Sinha
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The document discusses using deep learning and artificial intelligence to predict and prevent manufacturing defects. It presents a framework that uses CAE simulation and deep learning to model the relationship between input control parameters and output system responses for multi-stage production systems. This enables intelligent root cause analysis of defects by estimating input parameters given an output. The approach aims to solve challenges like high dimensionality, data uncertainty, and model transferability. Software called Deep Learning for Manufacturing is presented that implements solutions like adaptive sampling and 3D CNNs to address these challenges. Potential competitive advantages include improved quality, productivity, and reduced costs through increased root cause analysis capabilities.
Deep learning in manufacturing predicting and preventing manufacturing defects - Sumit Sinha
- 1. DEEP LEARNING FOR MANUFACTURING Predicting and Preventing Manufacturing Defects Get Connected: When CAE Simulation Meets Artificial Intelligence Authors: Sumit Sinha, Pasquale Franciosa, Manoj Babu, Emile Glorieux, Darek Ceglarek Presented by: Sumit Sinha WMG, University of Warwick, Coventry, UK 4th February 2020 | Professor Lord Bhattacharya Building (NAIC), WMG | University of Warwick, Coventry, CV4 7AL
- 2. AGENDA FRAMEWORK Overview of the problem and the proposed solution framework 01 SOFTWARE How the library “Deep Learning for Manufacturing (dlmfg)” helps solve these challenges 03 CHALLENGES Challenges faced in application of Artificial Intelligence within manufacturing systems 02 COMPETITIVE ADVANTAGE Potential benefits that can be expected on application of CAE and AI software within the closed- loop framework 04 2
- 3. OBJECTIVE: Diagnosing root causes of quality defects generated and propagated in multi-stage systems CHALLENGES: (1) Generation and propagation of defects (2) Large parameters space (3) Multi-physics nature of quality defects TYPICAL APPLICATIONS: CONCEPT: TYPICAL TOPICS: (1) Zero-defect manufacturing (2) Variation reduction (3) Quality control (4) Tooling and process optimisationAutomotive Aerospace System 𝑌 = 𝐹(𝑋) 𝑋 Input control parameters 𝑌 Output system response 𝑇 Process constraints FRAMEWORK 3
- 4. FRAMEWORK 4 Intelligent & Automated Root Cause Analysis (RCA) for multi-stage production/assembly systems Goal Analysis of a system: Forward Propagation 𝑋 Takes as input a set of control parameters & incoming material 𝑌 Gives as output a system response or component 𝑋 Input control parameters have to be estimated 𝑌 Given the output is known 𝑇 Constraints such as specification limits Estimating 𝑭 enables: Support System Optimization Variation Reduction Estimating 𝑭−𝟏 enables: Intelligent Root Cause Analysis of Defects Preventive Control Actions System 𝑌 = 𝐹(𝑋) 𝑇 Synthesis of a system: Backward Propagation System 𝑋 = 𝐹−1 (𝑌) Process Capability Improvement Support System Optimization CAE Simulation – Variation Response Method Artificial Intelligence: Deep Learning for Manufacturing Constraints such as specification limits
- 5. 4. DL Deployment X 𝑌 ? N Y Adaptive sampling 3. DL Training1. Training Data 2. CAE Simulation Set X and compute YReduce uncertainty Optimise DL prediction Model training iteration: 1-st 5 FRAMEWORK MAIN APPLICATION AREA: Multi-stage Production Systems
- 6. 4. DL Deployment X 𝑌 ? 3. DL Training1. Training Data 2. CAE Simulation N Y Adaptive sampling Set X and compute YReduce uncertainty Optimise DL prediction Model training iteration: i-th 6 FRAMEWORK MAIN APPLICATION AREA: Multi-stage Production Systems
- 7. 4. DL Deployment X 𝑌 ? Set X and compute Y N Y Adaptive sampling 3. DL Training1. Training Data 2. CAE Simulation Reduce uncertainty Optimise DL prediction Ready to be used in field Model training iteration: N-th Model ready to be deployed! 7 FRAMEWORK MAIN APPLICATION AREA: Multi-stage Production Systems
- 8. CHALLENGES - OVERVIEW 8 The software aims to solve the key challenges related to Training data and the Deep Learning Model 1. Architecture Selection 2. System Collinearity 3. Model Transferability 3. Deep Learning Model 1. System Dimensionality 2. Uncertainty Quantification 3. Large dataset and storage 1. Training Data 1. Model fidelity 2. IT deployment infrastructure 3. Real-time data gathering 4. Model Deployment 1. Model Fidelity 2. Model Completeness 3. Computational Time 2. CAE Simulation
- 9. 9 CHALLENGES – Training Data Quantifying the uncertainty in the data Uncertainty Quantification The stochastic nature of the system and uncertainty due to noise needs to be quantified in the model parameters For a car door assembly with 200 parameters training data to account for 1.2 × 1030 scenarios is required Multi-stage systems have a large number of potential root causes System Dimensionality A door assembly has up to 200 input parameters that can lead to 2200 situations the output for example can be cloud of point data with up to 1 million points 1 million points 1.2 × 1030 Scenarios Storage and access of large cloud-of-point data Large Dataset and Storage Given the dimensionality of the system and nature of cloud-of-point data the data size can go up to 500 GB for a single case study 𝑋 𝑠 𝑖 𝑌 𝑠 𝑖
- 10. 10 CHALLENGES - DEEP LEARNING MODEL Same outputs can have different set of inputs System Collinearity Transferring knowledge from one application to a ‘similar’ application Model Transferability Selection and optimization of model architecture Architecture Selection Given various iterations of positioning, clamping fastening and re-positioning in multi-stage systems, the outputs are collinear with different inputs Each component assembly has a different configuration hence a different set of process parameters Different input data types require different architectures for feature extraction 3D CNN – Point Cloud Data 2D CNN – Image Data LSTM– Time-Series Data • • • • • • • • Input (Point Cloud) Output (Root Cause) Root Cause Root Cause Transfer Learning
- 11. SOFTWARE - Overview Python Based TensorFlow Backend Fully- Documented** Object- Oriented Open-Source Platform Independent GitHub Project* GPU Enabled GitHub Project *: https://github.com/sumitsinha/Deep_Learning_for_Manufacturing Documentation**: https://sumitsinha.github.io/Deep_Learning_for_Manufacturing/html/index.html 11 In the pipeline: Distributed Computing Advance Multi-Stage Case Studies
- 12. SOFTWARE - NOVELTY Gaussian Mixture based Multi-Mode Output Model Used to handle the collinearities present within a system Active Learning Adaptive sampling strategies to handle the dimensionality of the system 3D CNN Architecture* Optimized 3D Convolutional neural network model architectures 12 The library includes implementation of novel research and development to solve the stated challenges *Sinha, S., Glorieux, E., Franciosa, P., & Ceglarek, D. (2019, June). 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds. In Multimodal Sensing: Technologies and Applications (Vol. 11059, p. 110590B). International Society for Optics and Photonics. Probabilistic Model using Bayesian Inference Used to quantify the uncertainty of predictions using approximate Bayesian inference Uncertainty Quantification System CollinearitySystem Dimensionality Architecture Selection
- 13. SOFTWARE - SOLUTIONS Data Convergence Study Used to quantify the amount of data required for model training Transfer Learning Consists of routines and guidelines for transferring knowledge between different cases and quantifying the similarity The library also includes datasets and key modules for various tasks not included in standard deep learning libraries, that help solve the model transferability challenges Datasets Supervised learning datasets for various assembly case studies Pre-Trained Models Consists of trained neural network models that can be directly leveraged for application using marginal transfer learning 13
- 14. SOFTWARE – CASE STUDY Stage 1 – Positioning Stage 2 – Clamping Stage 3 – Fastening Stage 4 – Release Data If data is collected at stage 3, the model converges with 1000 training samples with a root mean square error (RMSE) of 0.14 and R-squared value of 0.97 Target: Intelligent Root Cause Analysis for two-part assembly 0.46 0.27 0.19 0.18 0.16 0.16 0.16 0.15 0.15 0.15 0.14 0.14 0.14 0.14 0.13 0.41 0.86 0.94 0.95 0.96 0.96 0.96 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0 0.2 0.4 0.6 0.8 1 1.2 No of Training Samples Mean Absolute Error_avg Root Mean Squared Error_avg R Squared_avg Testing is done up to double of the specification limit used for training 14
- 15. 15 COMPETITIVE ADVANTAGE Improvement in quality due to increased RCA capabilities Improvement in productivity due to reduction in scrap Reduction of machine downtime due to rapid automated RCA Reduction of manual expertise for RCA of defects Application and integration of CAE Simulation with deep learning ensures early estimation and prediction of process parameter variations hence they can be prevented from manifesting into product defects Result
- 16. COMPETITIVE ADVANTAGE Process data sensors for all parameters Only Product Data Product Data + CAE Simulation + Artificial Intelligence High RCA Capabilities • High costs • Difficult to setup within an online system Directly obtain process data for each parameter without the need for any learning Only having product data can only be used for monitoring and not RCA • Limited RCA capabilities/only monitoring • Requirement of manual expertise • No/limited data for AI model training • Low costs • High RCA Capabilities • Strategic sensor placement Use AI model trained on real and simulated data to model relationship and suggest minimum additional process sensors in various sub-stages of the system 1 2Current Scenario 16
- 17. 17 THANK-YOU CONTACT Does anyone have any questions? [email protected] +44 (0) 7570200029 IMC, WMG, University of Warwick linkedin.com/in/sumit-sinha-32891956 MAIN SOFTWARE CONTRIBUTORS : Sumit Sinha (Warwick University), Pasquale Franciosa (Warwick University), Manoj Babu (Warwick University), Emile Glorieux (Warwick University), Darek Ceglarek (Warwick University) Acknowledgements Dr. Pasquale Franciosa Prof. Darek Ceglarek WMG, University of Warwick, UK WMG, University of Warwick, UK Email: [email protected] / [email protected] M: +44(0) 7440022523 / 44(0) 7824540721 T: +44(0)24765 73422 / 44(0) 24765 72681