A Hybrid Data-Based and Model-Based Approach to Process Monitoring and Control in Sheet Metal Forming

Abstract

The ability to predict and control the outcome of the sheet metal forming process demands holistic knowledge of the product/process parameter influences and their contribution in shaping the output product quality. Recent improvements in the ability to harvest in-line production data and the increased capability to understand complex process behaviour through computer simulations open up the possibility for new approaches to monitor and control production process performance and output product quality. This research presents an overview of the common process monitoring and control approaches while highlighting their limitations in handling the dynamics of the sheet metal forming process. The current paper envisions the need for a collaborative monitoring and control system for enhancing production process performance. Such a system must incorporate comprehensive knowledge regarding process behaviour and parameter influences in addition to the current-system-state derived using in-line production data to function effectively. Accordingly, a framework for monitoring and control within automotive sheet metal forming is proposed. The framework addresses the current limitations through the use of real-time production data and reduced process models. Lastly, the significance of the presented framework in transitioning to the digital manufacturing paradigm is reflected upon.

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

“…The main issue associated with data-driven approaches is the requirement for large quantities of historical, verified data to provide reliable performance [ 121 ]. Data-driven approaches require a large set of sensor data for each type of fault to train the model before the system’s deployment.…”

Industry 4.0 in Metal Forming Industry Towards Automotive Applications: A Review

“…In automotive industry, the integration of I-IoT and automated supply chain was proposed for optimising the metal forming productivity, forming process, product quality and economic feasibility [1,42]. The I-IoT is getting more importance and visibility in metal forming industry because it led to better formability and precision of the formed products [41].…”

“…Experimental results demonstrate that an average prediction accuracy of over 96% was achieved in the actual production line. In addition, a hybrid data-driven and model-based framework for establishing a combined monitoring and control system was proposed in the complicated automotive sheet metal forming processes [42]. This framework used the retrieved data (such as the material information and coatings) and the captured real-time metal forming data (such as temperature, sheet thickness and tribology behaviours) as an input for the process models to predict the product/process settings as the output, then this would be utilised for optimising the product properties or process settings.…”

Degradation analysis of dynamic properties for plain concrete structures under mixed-mode fracture conditions via an improved cohesive crack approach

“…These models, ranging from simple analytical formulations to complex finite element simulations, play a pivotal role in understanding the behavior of structures under various loading conditions and in predicting their response to damage. In the scientific literature, damage identification methodologies are typically categorized into data-based and model-based approaches [1][2][3]. Methods falling under the former category depend on static and dynamic data collected either on demand, periodically, or continuously over time during the inspection phase of monitored structures.…”