Comprehensive machine data acquisition through intelligent parameter identification and assignment

In today’s highly competitive manufacturing environment, process data monitoring continues to be of high priority, but often relies on modern communication interfaces being provided by PLC manufacturers. This paper proposes an alternative approach in which data is acquired automatically from various PLC models through available interfaces. Multiple Machine Learning algorithms are incorporated to identify machine parameters, which are then assigned to appropriate machine information models. All functionalities can be provided by a dedicated hardware module or as software modules on IPCs. The proposed approach can be integrated into existing Industry 4.0 efforts to accelerate digitalization in challenging environments

Design and implementation of a holistic framework for data integration in industrial machine and sensor networks

Digitalization and connectivity trends in industrial plants and production equipment create vast and heterogeneous networks of data sources, data sinks and various communication protocols. Data fusion and evaluation of these resources result in high costs for data integration and maintenance. Therefore, we propose a new framework, called MyGateway, enabling effortless integration of heterogeneous data sources, their fusion within the framework and publication to data sinks as needed. For easy integration, deployment, and expansion of the framework we provide an implementation in JAVA using open-source adapters for common industrial protocols and a simple API for usage in user specified setups

Mechatronic Coupling System for Cooperative Manufacturing with Industrial Robots

Rising product variants and shortened product life cycles require more flexible and universally utilizable production systems and machines. Consequently, it can be expected that the importance of industrial robots in production will continuously increase, due to their suitability to take over the role of a universal production machine. However, robots are not yet able to fulfill this role. Industrial use of robots has so far been limited mainly to simple transport and handling tasks in the context of human-robot collaboration as well as highly repetitive automated tasks in the context of manufacturing and assembly. For universal use, robots must be capable to perform more demanding tasks in manufacturing with higher requirements on mechanical stiffness and accuracy. Therefore, this paper presents a mechatronic system to couple two robots to a parallel kinematic system to temporarily increase the mechanical stiffness. The coupled state of the robots allows load sharing, higher process forces and eventually higher precision. The overall goal is to enable robots to perform more demanding manufacturing tasks and thus to be utilized in a wider range of applications. Design requirements, the development approach and optimization methods of the first coupling module prototype will be presented and discussed. The next development steps, a future demonstration system and possible use cases for the coupling module will be shown in the outlook

Self-Aware LiDAR Sensors in Autonomous Systems using a Convolutional Neural Network

Autonomous systems, as found in autonomous driving and highly automated production systems, require an increased reliability in order to achieve their high economic potential. Self-aware sensors are a key component in highly reliable autonomous systems. In this paper we highlight a proof of concept (PoC) of a deep learning method that enables a LiDAR (Light detection and ranging) sensor to detect functional impairment. More specifically, a deep convolutional neural network (CNN) is developed and trained with labelled LiDAR data in the form of point clouds to classify the degree of impairment of its functionality. The results are statistically significant and can be regarded as a general classifier for objects within LiDAR data, applied to selected cases of sensor impairment. In detecting impairment and evaluating the correctness of the captured data, the sensor gains a basic form of self-awareness. The presented methods and insights pave the way for improved safety of autonomous systems by the means of more sophisticated “self-aware” neural networks