Abstract
Diagnosis systems for laser processing are being integrated into industry. However, their readiness level is still questionable under the prism of the Industry’s 4.0 design principles for interoperability and intuitive technical assistance. This paper presents a novel multifunctional, web-based, real-time quality diagnosis platform, in the context of a laser welding application, fused with decision support, data visualization, storing, and post-processing functionalities. The platform’s core considers a quality assessment module, based upon a three-stage method which utilizes feature extraction and machine learning techniques for weld defect detection and quality prediction. A multisensorial configuration streams image data from the weld pool to the module in which a statistical and geometrical method is applied for selecting the input features for the classification model. A Hidden Markov Model is then used to fuse this information with earlier results for a decision to be made on the basis of maximum likelihood. The outcome is fed through web services in a tailored User Interface. The platform’s operation has been validated with real data.
Highlights
Laser material processing includes a set of non-conventional machining [1] and joining methods [2] which have been well established in modern manufacturing
With zero-defect manufacturing (ZDM) in mind, wrapping these processes with the appropriate infrastructure and tools for monitoring, quality diagnosis, and adaptive control [5] is of utmost importance
This paper proposes a 3-Stage Quality Assessment (3SQA) method based on machine learning techniques to allow for an algorithm that is able to capture the complex relations between various measurements and defects [28]
Summary
Laser material processing includes a set of non-conventional machining [1] and joining methods [2] which have been well established in modern manufacturing. With zero-defect manufacturing (ZDM) in mind, wrapping these processes with the appropriate infrastructure and tools for monitoring, quality diagnosis, and adaptive control [5] is of utmost importance This way, the processes and the systems will be able to harmonize with the requirements of Industry 4.0, as depicted in the Fig. 1. The current study presents a web-based quality diagnosis platform for laser processing and mainly for welding and Additive Manufacturing (AM) applications, based on a 3-Stage Quality Assessment (3SQA) method. It incorporates feature extraction and machine learning (ML) techniques for defect classification and weld quality prediction. The concept of integration into the cognitive factory of the future is discussed, while an outlook for the future is provided
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