Machine learning for automated quality control in injection moulding manufacturing

• URL: http://arxiv.org/abs/2206.15285v1
• Date: Thu, 30 Jun 2022 13:44:48 GMT
• Title: Machine learning for automated quality control in injection moulding manufacturing
• Authors: Steven Michiels, C\'edric De Schryver, Lynn Houthuys, Frederik Vogeler, Frederik Desplentere
• Abstract summary: Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. In this study, simulated data was used to develop a predictive model for the product quality of an injection moulded sorting container. The achieved accuracy, specificity and sensitivity on the test set was $99.4%$, $99.7%$ and $94.7%$, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. As the labelling of extensive, real-world process data is costly, however, the use of simulated process data may offer a first step towards a successful implementation. In this study, simulated data was used to develop a predictive model for the product quality of an injection moulded sorting container. The achieved accuracy, specificity and sensitivity on the test set was $99.4\%$, $99.7\%$ and $94.7\%$, respectively. This study thus shows the potential of ML towards automated QC in injection moulding and encourages the extension to ML models trained on real-world data.

Related papers

• Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies [0.0]
  This study investigates the impact of modifying $gamma$ on model efficiency and accuracy for the prediction of vertical excitation energies using the QeMFi benchmark dataset. A novel error metric, error contours of MFML, is proposed to provide a comprehensive view of model error contributions from each fidelity. The results indicate that high model accuracy can be achieved with just 2 training samples at the target fidelity when a larger number of samples from lower fidelities are used.
  arXiv  Detail & Related papers  (2024-10-15T08:35:00Z)
• From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning [52.257422715393574]
  We introduce a self-guided methodology for Large Language Models (LLMs) to autonomously discern and select cherry samples from open-source datasets. Our key innovation, the Instruction-Following Difficulty (IFD) metric, emerges as a pivotal metric to identify discrepancies between a model's expected responses and its intrinsic generation capability.
  arXiv  Detail & Related papers  (2023-08-23T09:45:29Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Machine Learning Force Fields with Data Cost Aware Training [94.78998399180519]
  Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation. Even for the most data-efficient MLFFs, reaching chemical accuracy can require hundreds of frames of force and energy labels. We propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
  arXiv  Detail & Related papers  (2023-06-05T04:34:54Z)
• Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge [0.0]
  This paper proposes a transfer learning based approach to address this issue. A ML model is trained on a large amount of computationally inexpensive data from a physics-based process model (source) and then fine-tuned on a smaller amount of costly experimental data (target) Despite extreme functional and quantitative inaccuracies in the source our approach reduces the model development cost by years, experimental cost by 56-76%, computational cost by orders of magnitude, and prediction error by 16-24%.
  arXiv  Detail & Related papers  (2023-04-29T10:54:57Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• Surrogate Modelling for Injection Molding Processes using Machine Learning [0.23090185577016442]
  Injection molding is one of the most popular manufacturing methods for the modeling of complex plastic objects. We propose a baseline for a data processing pipeline that includes the extraction of data from Moldflow simulation projects. We evaluate machine learning models for fill time and deflection distribution prediction and provide baseline values of MSE and RMSE metrics.
  arXiv  Detail & Related papers  (2021-07-30T12:13:52Z)
• Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving [80.6725125503521]
  Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest. We present a use case of using ML models for detecting faults during automated screwdriving operations.
  arXiv  Detail & Related papers  (2021-07-05T11:46:00Z)
• Towards the Automation of a Chemical Sulphonation Process with Machine Learning [0.0]
  This paper presents the results of applying machine learning methods during a chemical sulphonation process. We used data from process parameters to train different models including Random Forest, Neural Network and linear regression. Our experiments showed that it is possible to predict those product quality values with good accuracy, thus, having the potential to reduce time.
  arXiv  Detail & Related papers  (2020-09-25T10:56:41Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.