Feature Engineering for Machine Learning using a Software-Based Approach for Machining Operations

Dementyev, Alexander; Kolář, Petr; Benesch, Manfred; Teicher, Uwe; Pätzold, R.; Ihlenfeldt, Steffen

doi:10.1007/978-3-030-78424-9_58

Cited by 2 publications

(1 citation statement)

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“…The flow chart shown in Figure 6 illustrates the application of A Holistic Quality Assurance Approach on the example of Tool Condition Monitoring. For more details about this example see [51].…”

Section: Evaluation Of the Conceptmentioning

confidence: 99%

A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

2021

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With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

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Section: Evaluation Of the Conceptmentioning

confidence: 99%