Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing

• URL: http://arxiv.org/abs/2202.02003v1
• Date: Fri, 4 Feb 2022 07:22:29 GMT
• Title: Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing
• Authors: Patrick Link, Miltiadis Poursanidis, Jochen Schmid, Rebekka Zache, Martin von Kurnatowski, Uwe Teicher, Steffen Ihlenfeldt
• Abstract summary: This study introduces a general methodology for building quality prediction models with machine learning methods on small datasets. The proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such machine learning models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with machine learning methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input-output relationship to be learned. The proposed methodology is applied to a brushing process with $125$ data points for predicting the surface roughness as a function of five process variables. As opposed to conventional machine learning methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. Another merit of the proposed methodology is that, in contrast to most conventional machine learning methods, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.

Related papers

• Machine Learning for predicting chaotic systems [0.0]
  We show that well-tuned simple methods, as well as untuned baseline methods, often outperform state-of-the-art deep learning models. These findings underscore the importance of matching prediction methods to data characteristics and available computational resources.
  arXiv  Detail & Related papers  (2024-07-29T16:34:47Z)
• PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [71.63186089279218]
  This paper introduces a pre-trained model-based continual learning toolbox known as PILOT. On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt. On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
  arXiv  Detail & Related papers  (2023-09-13T17:55:11Z)
• Tailoring Machine Learning for Process Mining [5.237999056930947]
  We argue that a deeper insight into the issues raised by training machine learning models with process data is crucial to ground a sound integration of process mining and machine learning. Our analysis of such issues is aimed at laying the foundation for a methodology aimed at correctly aligning machine learning with process mining requirements.
  arXiv  Detail & Related papers  (2023-06-17T12:59:51Z)
• Stop overkilling simple tasks with black-box models and use transparent models instead [57.42190785269343]
  Deep learning approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process. Deep learning strategies often outperform traditional models in terms of accuracy.
  arXiv  Detail & Related papers  (2023-02-06T14:28:49Z)
• Uncertainty Estimation in Machine Learning [0.0]
  In machine learning the model complexity and severe nonlinearity become serious obstacles to uncertainty evaluation. The latest example of a pre-trained model is the Generative Pre-trained Transformer 3 with hundreds of billions of parameters and a half-terabyte training dataset.
  arXiv  Detail & Related papers  (2022-06-03T16:11:11Z)
• Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization [101.32332941117271]
  Decision making algorithms are used in a multitude of different applications. Deep learning approaches that use highly parametric architectures tuned from data without relying on mathematical models are becoming increasingly popular. Model-based optimization and data-centric deep learning are often considered to be distinct disciplines.
  arXiv  Detail & Related papers  (2022-05-05T13:40:08Z)
• Distilling Interpretable Models into Human-Readable Code [71.11328360614479]
  Human-readability is an important and desirable standard for machine-learned model interpretability. We propose to train interpretable models using conventional methods, and then distill them into concise, human-readable code. We describe a piecewise-linear curve-fitting algorithm that produces high-quality results efficiently and reliably across a broad range of use cases.
  arXiv  Detail & Related papers  (2021-01-21T01:46:36Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough Patents [0.0]
  We introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance. We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility. We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon.
  arXiv  Detail & Related papers  (2020-03-30T13:06:25Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)

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.