Deep learning for smart manufacturing: Methods and applications

Data-driven smart manufacturing

“Industrie 4.0” and Smart Manufacturing - A Review of Research Issues and Application Examples

Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services. This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges. In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components --- models may be "entangled" in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable to other organizations.

Software engineering for machine learning: a case study

A systematic literature review of machine learning methods applied to predictive maintenance

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Machine learning in manufacturing: advantages, challenges, and applications

Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

Automated surface inspection of cold-formed micro-parts

Learning Defect Classifiers for Textured Surfaces Using Neural Networks and Statistical Feature Representations

Detecting surface defects is a challenging visual recognition problem arising in many processing steps during manufacturing. These defects occur with arbitrary size, shape and orientation. The challenges posed by this complexity have been combated with very special, runtime intensive and hand-designed feature representations. In this paper we present a machine vision system which uses basic patch statistics from raw image data combined with a two layer neural network to detect surface defects on arbitrary textured and weakly labeled image data. Evaluation on an artificial dataset with more than 6000 examples in addition to a real micro-cold forming process showed excellent classification results.

Forty Sixth CIRP Conference on Manufacturing Systems 2013 Learning defect classifiers for textured surfaces using neural networks and statistical feature representations

Daniel Weimer

Papers

Machine learning in manufacturing: advantages, challenges, and applications

Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection

Automated surface inspection of cold-formed micro-parts

Learning Defect Classifiers for Textured Surfaces Using Neural Networks and Statistical Feature Representations

Forty Sixth CIRP Conference on Manufacturing Systems 2013 Learning defect classifiers for textured surfaces using neural networks and statistical feature representations