SYSTEM UNDERSTANDING OF HIGH PRESSURE DIE CASTING PROCESS AND DATA WITH MACHINE LEARNING APPLICATIONS

Overview:

  • Title: SYSTEM UNDERSTANDING OF HIGH PRESSURE DIE CASTING PROCESS AND DATA WITH MACHINE LEARNING APPLICATIONS
  • Author: David J. Blondheim, Jr.
  • Publication Year: Fall 2021
  • Publishing Journal/Academic Society: Colorado State University (Dissertation)
  • Keywords: Die casting, machine learning, systems engineering, Industry 4.0, data framework, unsupervised machine learning, anomaly detection, process control.

Research Background:

  • Social/Academic Context of the Research Topic: Die casting is a complex manufacturing system widely used for producing near net shape castings. Despite its long history, a comprehensive systems engineering approach to define the process and utilize the data generated in each cycle is lacking. Existing research often focuses on a narrow scope of critical parameters within die castings.
  • Limitations of Existing Research: Narrow focus on limited data parameters in die casting research has led to limited success and applicability of machine learning in production foundries. Process optimization literature often uses poorly chosen experimental design inputs and ranges, and lacks complexity of real-world die casting applications for quality prediction.
  • Necessity of the Research: There is a need for a systems engineering perspective to understand the die casting process and data comprehensively. A data framework is necessary to manage the large volume of data generated and identify meaningful applications of machine learning to improve process control and quality in die casting.
Figure 2: Example V8 engine block die casting (photo permission from Mercury Marine)
Figure 2: Example V8 engine block die casting (photo permission from Mercury Marine)
Figure 14: Biscuity, runner, gating, and venting system on casting (photo permission from Mercury Marine)
Figure 14: Biscuity, runner, gating, and venting system on casting (photo permission from Mercury Marine)

Research Purpose and Research Questions:

  • Research Purpose: To investigate the die casting process from a systems engineering perspective and demonstrate meaningful ways to apply machine learning to enhance system understanding, process control, and data utilization in the die casting industry.
  • Key Research Questions:
    • How can a systems engineering approach define the critical processes and data framework in die casting?
    • How can machine learning be meaningfully applied to the complex die casting system to improve process control and quality?
    • Can unsupervised machine learning provide value by automatically monitoring data and identifying anomalies in die casting?
  • Research Hypotheses: Unsupervised machine learning, applied within a systems engineering framework for die casting, can provide value by automatically monitoring data and identifying anomalies, leading to process control improvement and better utilization of data generated by the die casting process.

Research Methodology

  • Research Design: Dissertation research involving literature review, systems engineering analysis, case studies, and experimental studies.
  • Data Collection Method: Data collected from production die casting processes, including equipment settings, time-series data from injection and other systems, thermal images, and casting quality data.
  • Analysis Method: Systems engineering approach to define the die casting system and data framework. Statistical analysis (Wilcoxon Signed Rank Test) for experimental data. Machine learning algorithms including k-means clustering and autoencoders for case studies.
  • Research Subjects and Scope: High pressure die casting process, data generated from die casting operations, case studies conducted at Mercury Marine.

Main Research Results:

  • Key Research Results:
    • Die casting process is defined as a complex system with network structure, adaptive, self-organizing, and nonlinear characteristics.
    • A comprehensive data framework for die casting is developed, categorizing data into Design Parameter Data, Input Settings Data, Output – Discrete Data, Output – Time-Series Data, and Cycle Time Analysis Data.
    • Data volume in die casting is several orders of magnitude larger than what is currently used in the industry.
    • Unsupervised machine learning, particularly anomaly detection using autoencoders, provides value in monitoring die casting processes and identifying anomalies.
    • Case studies demonstrate the application of unsupervised machine learning for process monitoring, anomaly detection in thermal images and time-series data, and process optimization.
    • Stochastic nature of porosity formation is confirmed, and limitations of traditional supervised machine learning approaches for quality prediction in die casting are highlighted.
  • Statistical/Qualitative Analysis Results: Wilcoxon Signed Rank Test showed no statistically significant difference in critical injection parameters between best and worst samples, indicating stochastic nature of porosity. Case studies demonstrated the effectiveness of unsupervised machine learning in anomaly detection and process understanding.
  • Data Interpretation: The complexity of die casting and the stochastic nature of defect formation necessitate a shift from traditional supervised machine learning for quality prediction to unsupervised methods for process monitoring and anomaly detection. A systems engineering approach and comprehensive data utilization are crucial for effective application of machine learning in die casting.
  • Figure Name List:
    • Figure 1: Example die cast cell layout
    • Figure 2: Example V8 engine block die casting
    • Figure 3: Metal delivery: dosing furnace (left), 2-axis ladle (center), and 7-axis robot ladle (right)
    • Figure 4: Die and chamber diagram
    • Figure 5: Waves entrap air when slow shot speed is too slow
    • Figure 6: Turbulent waves form when slow shot speed is too fast
    • Figure 7: Correct wave formation allows all air to escape from the chamber
    • Figure 8: Example shot injection velocity and pressure graph
    • Figure 9: Die shown with hydraulic cylinders and slides in (left) and out (right) positions
    • Figure 10: Die example with ejection pins in the out position
    • Figure 11: Hot water unit (left) and jet cool unit (right) in production
    • Figure 12: Spray systems: 2-axis manifold (left) and 6-axis robot (right)
    • Figure 13: Key nomenclature of features within the die
    • Figure 14: Biscuity, runner, gating, and venting system on casting
    • Figure 15: Simulated metal fluid flow during injection
    • Figure 16: Complex thermal cooling lines in die design
    • Figure 17: Example of hot water and jet cool lines on a moving half of a large die
    • Figure 18: Complexity in Die Casting
    • Figure 19: Systems engineering “V” Diagram based on [60]
    • Figure 20: Decomposition of hierarchical systems
    • Figure 21: Die casting context diagram
    • Figure 22: Tooling Management system diagram
    • Figure 23: Generic closed-loop feedback system
    • Figure 24: Example programmed set points for shot profile
    • Figure 25: Example shot profile output
    • Figure 26: Thermal imaging camera in production die cast environment
    • Figure 27: Example thermal image on stationary half (left) and moving half (right)
    • Figure 28: Example extraction cell with multiple robots and post-die cast processing
    • Figure 29: Sawed sample casting for X-ray
    • Figure 30: Example X-ray gradings 1 through 3
    • Figure 31: 9 best samples (Grade 1)
    • Figure 32: 9 worst samples (Grade 3)
    • Figure 33: Simulated results of predicted porosity zone
    • Figure 34: Volume of liquid in the sample area as predicted by MAGMA
    • Figure 35: Example of overlapping data space
    • Figure 36: Examples of HPDC porosity
    • Figure 37: Sample X-Ray images of porosity
    • Figure 38: Classification issue due to binary acceptance specifications example
    • Figure 39: Example of simulated predicted porosity zone
    • Figure 40: Sequential castings showing stochastic porosity formation
    • Figure 41: Classification issue due to stochastic defect formation example
    • Figure 42: Classification issue due to secondary process variation example
    • Figure 43: Combination of elements example
    • Figure 44: Bias-Variance tradeoff graph
    • Figure 45: Traditional confusion matrix based on counts
    • Figure 46: Normalized confusion matrix based on percentage
    • Figure 47: Example calculations on traditional and normalized confusion matrices
    • Figure 48: Example of different accuracy calculations
    • Figure 49: Bias-Variance tradeoff graph with Critical Error Threshold
    • Figure 50: Increased inherent error
    • Figure 51: Increased bias
    • Figure 52: Artificial intelligence, machine learning, and deep learning hierarchy
    • Figure 53: Example 2-class confusion matrix
    • Figure 54: Six challenges of machine learning in die casting
    • Figure 55: Example 2D laser barcode on a die casting
    • Figure 56: Hughes Phenomenon visually depicted
    • Figure 57: Generic IT architecture for machine learning implementation
    • Figure 58: Reducing scrap rates drives a need for increased accuracy and a lower CET target
    • Figure 59: Porosity defect uncovered after machining
    • Figure 60: Gate break-in defect
    • Figure 61: Thermal imaging system installed on die cast machine
    • Figure 62: Thermal image example with data conversion
    • Figure 63: 2D barcode and serial number example
    • Figure 64: Intensification Pressure data over experimental run
    • Figure 65: Example of a linear regression model
    • Figure 66: Example of an overfitted model
    • Figure 67: Example of a neural network structure
    • Figure 68: Example of a SVM classification in two dimensions
    • Figure 69: Elbow Method for determining number of clusters
    • Figure 70: Cluster Assignment with 7 Clusters
    • Figure 71: Boxplot and Individual Plots of Average Slow Shot Velocity and Intensification Pressure
    • Figure 72: Matrix scatter plot of key parameters by clusters
    • Figure 73: Matrix scatter plot of key parameters by result (Good v. Bad)
    • Figure 74: Scatter plots of intensified pressure versus average slow shot velocity
    • Figure 75: Boxplot and Individual Plots of Average Slow Shot Velocity and Intensification Pressure comparing clusters with historical scrap data
    • Figure 76: Three-step algorithm for anomaly detection
    • Figure 77: Visualization of steady state image stack with mean and standard deviation matrices
    • Figure 78: Mathematical code for different anomaly identification per color layer
    • Figure 79: Thermal image camera system install on die cast machine [45]
    • Figure 80: Thermal image temperature matrix and grayscale image
    • Figure 81: Pre-crop versus post-crop thermal image (stationary half)
    • Figure 82: Comparison of scaled pixel thermal images (moving half)
    • Figure 83: F-Scores per cluster size
    • Figure 84: Sequence of cluster assignments
    • Figure 85: Time-based sequence of cluster assignments
    • Figure 86: Mean and standard deviation images for cluster #1
    • Figure 87: Histogram of mean image of cluster #1 versus cluster #3
    • Figure 88: Anomaly detection on warmup shot
    • Figure 89: Anomaly detection on die with water turned off
    • Figure 90: Mean and stand deviation images of two process settings
    • Figure 91: Typical range point settings for slow and fast shot velocities
    • Figure 92: Four example profiles with the same average
    • Figure 93: Anomaly detection using Standard Cosine Similarity
    • Figure 94: Anomaly detection using Modified Cosine Similarity
    • Figure 95: Distribution of Cosine Similarity calculation values
    • Figure 96: Home screen of Azure web page showing multiple machines running
    • Figure 97: Detailed shot screen with anomalous shot detected
    • Figure 98: Set points for shot profiles
    • Figure 99: Changing setpoints on a ramped slow shot velocity profile
    • Figure 100: Examples of identical slow shot velocity averages with different profiles
    • Figure 101: Simple neural network
    • Figure 102: Deep learning example
    • Figure 103: Autoencoder components
    • Figure 104: Example MNIST handwritten digits
    • Figure 105: Plot raster example
    • Figure 106: Data preparation
    • Figure 107: Digit predictions per example
    • Figure 108: Example 5s from training data set
    • Figure 109: Mean absolute error (MAE) based on autoencoder trained on 5s only
    • Figure 110: Visual and error examples from test data
    • Figure 111: 5s identified as anomalies by autoencoder
    • Figure 112: Example spray flow rate profile
    • Figure 113: Raw SQL data storage examples
    • Figure 114: Training data clusters
    • Figure 115: Original input and recreated profile
    • Figure 116: MAE of test samples
    • Figure 117: Illustrations of anomalies
    • Figure 118: Theoretical anomaly detection profile graphs
    • Figure 119: Specific gravity measurements and X-ray images on select BoB and WoW castings
    • Figure 120: Example furnace temperature data
    • Figure 121: Slides author presented at November 2019 NADCA Chapter 12 meeting
    • Figure 122: CT scan of porosity in casting from Chapter 3 study

Conclusion and Discussion:

  • Summary of Main Results: This dissertation establishes die casting as a complex system requiring a systems engineering approach and highlights the limitations of traditional machine learning applications focused solely on quality prediction. It demonstrates the value of unsupervised machine learning for anomaly detection and process control, utilizing a comprehensive data framework. The research emphasizes the stochastic nature of porosity and the challenges of defect classification due to human factors and process variations.
  • Academic Significance of the Research: This work bridges the gap between theoretical machine learning solutions and practical applications in complex manufacturing environments like die casting. It contributes to the understanding of complexity theory in manufacturing systems and provides a novel data framework for die casting. The research also introduces the concept of Critical Error Threshold (CET) for evaluating the financial viability of machine learning applications.
  • Practical Implications: The findings suggest a shift in focus from supervised quality prediction to unsupervised anomaly detection for immediate value in die casting. The developed data framework and anomaly detection algorithms offer practical tools for foundries to improve process control, reduce downtime, and better utilize the vast amounts of data generated. The case studies provide concrete examples of successful machine learning implementation in a production setting.
  • Limitations of the Research: The research primarily focuses on unsupervised machine learning and anomaly detection. Further research is needed to explore the full potential of supervised machine learning with improved data quality and comprehensive data sets. The case studies are limited to a specific die casting process and foundry, and further validation across different processes and foundries is needed. The complexity of die casting and the stochastic nature of porosity formation pose inherent limitations to achieving perfect predictability.

Future Follow-up Research:

  • Directions for Follow-up Research:
    • Industrialization and implementation of the developed anomaly detection algorithm on the shop floor.
    • Further research into feature importance to identify critical process parameters influencing casting quality.
    • Investigation of density in predicted porosity zones and its correlation with casting quality.
    • Exploration of recurrent neural networks and other advanced machine learning techniques to capture time-dependent data characteristics.
    • Development of improved methods for data collection, traceability, and ground truth data acquisition, particularly for porosity detection using CT scanning.
    • Further investigation into the concept of Critical Error Threshold and its application in various manufacturing settings.
  • Areas Requiring Further Exploration:
    • Reducing inherent error by incorporating more comprehensive data and process understanding into machine learning models.
    • Mitigating bias by improving defect classification accuracy through enhanced inspection methods and operator training.
    • Addressing the challenges of data imbalance and limited data sets in die casting.
    • Overcoming non-technical barriers to machine learning adoption in manufacturing, such as cultural resistance and skillset gaps.

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List of Abbreviations

  • AFS – American Foundry Society
  • AI – Artificial Intelligence
  • BoB – Best of Best
  • CET – Critical Error Threshold
  • CT Scanning – Computed Tomography Scanning
  • HPDC – High Pressure Die Casting
  • IIoT – Industrial Internet of Things
  • IT – Information Technology
  • INCOSE – International Council on Systems Engineering
  • ML – Machine Learning
  • MQTT – Message Queuing Telemetry Transport
  • NADCA – North American Die Casting Association
  • NN – Neural Network
  • OPC UA – Open Platform Communications Unified Architecture
  • OT – Operational Technology
  • PLC – Programmable Logic Controller
  • PSI – Pounds per Square Inch
  • RPM – Revolutions per Minute
  • SE – Systems Engineering
  • SoS – Systems-of-Systems
  • SVM – Support Vector Machine
  • WoW – Worst of worst

Copyright:

  • This material is David J. Blondheim, Jr.'s paper: Based on SYSTEM UNDERSTANDING OF HIGH PRESSURE DIE CASTING PROCESS AND DATA WITH MACHINE LEARNING APPLICATIONS.
  • This material was summarized based on the above paper, and unauthorized use for commercial purposes is prohibited.
  • Copyright © 2025 CASTMAN. All rights reserved.
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