Industry 4.0 was introduced in 2011 and since then has been perceived in multiple ways such as a vision, a paradigm, a scenario or as a digital revolution in production and service organizations. Even though Industry 4.0 is associated with great improvements for companies, there is still a lack of a uniform definition of the term Industry 4.0, especially when it comes to the transfer of knowledge from theoretical research to the implementation in organizations, which leads to confusion and disaffirmation. The lack of a clear structure and a holistic definition of the research topic Industry 4.0 inhibits the development of new business areas and new research approaches. To target this fundamental gap, a methodology is developed and the 338 most relevant publications are analyzed in the database of ScienceDirect starting from 2015. Based on those publications, the field of Industry 4.0 is structured. A consistent and comprehensive definition for Industry 4.0 is introduced by using a bibliometric analysis. Therefore, existing descriptions are decomposed into word fragments and analyzed. It is shown that this novel approach to find a definition for the term “Industry 4.0” does not yet exist. The aim is to provide a purely objective definition based on a statistical evaluation, without restricting the selection of publications to a specific research or business area. Based on those data, a new and ubiquitous definition of Industry 4.0 is formed, discussed and validated on practical examples.

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world’s population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple’s ARKit technology to accurately measure the distance from the user’s eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use.

Robot polishing is increasingly being used in the production of high-end glass workpieces such as astronomy mirrors, lithography lenses, laser gyroscopes or high-precision coordinate measuring machines. The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. Whilst the trend towards sub nanometre level surfaces finishes and features progresses, matching both form and finish coherently in complex parts remains a major challenge. With increasing optic sizes, the stability of the polishing process becomes more and more important. If not empirically known, the optical surface must be measured after each polishing step. One approach is to mount sensors on the polishing head in order to measure process-relevant quantities. On the basis of these data, machine learning algorithms can be applied for surface value prediction. Due to the modification of the polishing head by the installation of sensors and the resulting process influences, the first machine learning model could only make removal predictions with insufficient accuracy. The aim of this work is to show a polishing head optimised for the sensors, which is coupled with a machine learning model in order to predict the material removal and failure of the polishing head during robot polishing. The artificial neural network is developed in the Python programming language using the Keras deep learning library. It starts with a simple network architecture and common training parameters. The model will then be optimised step-by-step using different methods and optimised in different steps. The data collected by a design of experiments with the sensor-integrated glass polishing head are used to train the machine learning model and to validate the results. The neural network achieves a prediction accuracy of the material removal of 99.22%. Article highlights: First machine learning model application for robot polishing of optical glass ceramics The polishing process is influenced by a large number of different process parameters. Machine learning can be used to adjust any process parameter and predict the change in material removal with a certain probability. For a trained model,empirical experiments are no longer necessary Equipping a polishing head with sensors, which provides the possibility for 100% control

The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. With increasing optic sizes, the stability of the polishing process becomes more and more important. Parameters such as chemical stability of the slurry or tool wear are key elements for a deterministic computer controlled polishing (CCP) process. High sophisticated CCP processes such as magnetorheological finishing (MRF) or the ZEEKO bonnet polishing process rely on the stability of the relevant process parameters for the prediction of the desired material removal. Aim of this work is to monitor many process-relevant parameters by using sensors attached to the polishing head and to the polishing process. Examples are a rpm and a torque sensor mounted close to the polishing pad, a vibration sensor for the oscillation of the bearings, as well as a tilt sensor and a force sensor for measuring the polishing pressure. By means of a machine learning system, predictions of tool wear and the related surface quality shall be made. Goal is the detection of the critical influence factors during the polishing process and to have a kind of predictive maintenance system for tool path planning and for tool change intervals.

Robot polishing is increasingly used in the production of high-end glass work pieces such as astronomy mirrors, lithography lenses, laser gyroscopes or high-precision coordinate measuring machines. The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. Whilst the trend towards sub nanometre level surfaces finishes and features progresses, matching both form and finish coherently in complex parts remains a major challenge. With larger or more precise optics, the influence of process instabilities on the quality of the optics to be polished has a greater impact. Vibrations at a polishing head have a negative influence on the polishing result. These vibrations are caused by bearing damage, motors and other excitations (e.g. gears, belts). The aim of this work is the determination of vibrations at a polishing head and their avoidance strategies. Different bearing conditions are considered: new and perfect bearing, a bearing that has been in contact with polish (rust) and a bearing with repeatable damage (groove milled on the running surface). It can be shown that the frequencies of bearings affect the polishing tool. Furthermore, reasons for and against vibrations in the process are discussed. For the case of vibrationless machining, avoidance strategies were presented.