Operator Integrated – Concept for Manufacturing Intelligence

The industrial landscape is undergoing a series of fundamental changes, because of the advances in cutting-edge digital technologies. Under the framework of Industry 4.0 engineers have focused their effort on the development of new frameworks integrating digital technologies such as Big Data Analytics, Digital Twins, Extended Reality, and Artificial Intelligence, to upscale modern manufacturing systems, reduce uncertainties, and cope with the increased market volatility. However, in the upcoming industrial revolution, i.e., Industry 5.0, the research focus will be directed towards the new generation of human operators, the Operator 5.0. The purpose of this paper is to investigate the key technologies that will be the drivers towards the realization of the Operator 5.0 and to highlight the key challenges. Additional contribution is the proposal of a framework for the training and support of shopfloor technicians based on the utilization of Mixed Reality for manufacturing processes.

Section: Social Factory Architecturementioning

confidence: 99%

Operator 5.0: A Survey on Enabling Technologies and a Framework for Digital Manufacturing Based on Extended Reality

Mourtzis¹,

Angelopoulos²,

Panopoulos³

2022

“…Advanced manufacturing technologies require automation technology that is flexible, fast, and reliable, as it is an indispensable characteristic for their successful implementation [8]. At the present level of technology, CAPP systems are poised to bridge a significant gap in manufacturing tasks, representing another stride toward achieving complete automation in production systems.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Axes for Turning Parts

Erler,

Başar,

Brosius

2024

This paper delves into a critical aspect of Computer-Aided Production Planning (CAPP): the automated detection of the main rotational axis in turning parts within Computer-Aided Designs (CAD). The identification of the principal turning axis in CAD models presents numerous opportunities in the field of CAPP. In this study, the authors employ advanced surface segmentation techniques to analyse the surface geometry, pinpointing rotational surfaces within the CAD model. Subsequently, significant features are extracted from these identified rotational surfaces, and the necessary data for rotational centers are gathered. By fine-tuning the weighting of the data gathered, the approach can be tailored to suit various planning strategies. This approach has the potential to significantly enhance both the efficiency and accuracy of the automated production planning process for turning parts in CAPP.

“…Using machine learning one can improve modelling and implement the idea of an artificial operator learning on the basis of previous experience and adapting to changes in the machine tool operating conditions, including the thermal ones [49]. Machine learning also makes it possible to achieve precise thermal error compensation in real time.…”

Section: Gist Of Machine Learningmentioning

confidence: 99%

Application of Machine Learning in the Precise and Cost-Effective Self-Compensation of the Thermal Errors of CNC Machine Tools – A Review

Czwartosz¹,

Jędrzejewski²

2022

The current development of production engineering takes place through the innovative improvement of machine tools and machining processes at the constantly growing application of intelligent self-improvement functions. Machine learning opens up possibilities for machine tool self-improvement in real time. This paper discusses the state of knowledge relating to the application of machine learning for precise and cost-effective thermal error self-compensation. Data acquisition and processing, models and model learning and self-learning methods are also considered. Three highly effective error compensation systems (supported with machine learning) are analysed and conclusions and recommendations for future research are formulated.