Data privacy compliance benefits for organisations - a cyber-physical systems and Internet of Things study

The protection of people’s privacy is both a legal requirement and a key factor for doing business in many jurisdictions. Organisations thus have a legal obligation to get their privacy compliance in order as a matter of business importance. This applies not only to organisations’ day-to-day business operations, but also to the information technology systems they use, develop or deploy. However, privacy compliance, like any other legal compliance requirements, is often seen as an extra burden that is both unnecessary and costly. Such a view of compliance can result in negative consequences and lost opportunities for organisations. This paper seeks to position data privacy compliance as a value proposition for organisations by focusing on the benefits that can be derived from data privacy compliance as it applies to a particular subset of information technology systems, namely cyber-physical systems and Internet of Things technologies. A baseline list of data privacy compliance benefits, contextualised for CPSs and IoT with the South African legal landscape is proposed.http://www.springer.comseries/7899hj2021Informatic

Condition-Based Predictive Maintenance in the Frame of Industry 4.0

Part 6: Intelligent Diagnostics and Maintenance SolutionsInternational audienceThe emergence of Industry 4.0 leads to the optimization of all the industrial operations management. Maintenance is a key operation function, since it contributes significantly to the business performance. However, the definition and conceptualization of Condition-based Predictive Maintenance (CPM) in the frame of Industry 4.0 is not clear yet. In the current paper, we: (i) explicitly define CPM in the frame of Industry 4.0 (alternatively referred as Proactive Maintenance); (ii) develop a unified approach for its implementation; and, (iii) provide a conceptual architecture for associated information systems

An approach of development smart manufacturing metrology model as support industry 4.0

The framework for smart manufacturing metrology model (S3M), are based on integration of digital product metrology information through metrological identification, application artificial intelligence techniques and generation of global/local inspection plan for coordinate measuring machine (CMM). S3M has an extremely expressed requirement for better control, monitoring and data mining. Limitations still exist in data storages, networks and computers, as well as in the tools for complex data analysis, detection of its structure and retrieval of useful information. This paper will present recent results of our research on building of S3M as support Industry 4.0. Presented approach to S3M development includes four levels: (i) mathematical model of the measuring sensor path, which establishes a connection between the coordinate systems; (ii) generating the needed set of information to integrate the given tolerances and geometry of the parts by applying an ontological knowledge base; (iii) the application of AI techniques such as ACO and GA to optimize the measurement path, numbers of measuring part setup and configuration of the measuring probes; (iv) simulation of measurement path for a collision check. After simulation of the measurement path and visual checks of collisions, the path sequences are generated in the control data list for appropriate CMM. The experiment was successfully carried out on the examples of prismatic part and two turbine blades or its free-form measuring surfaces

Ontology in Holonic Cooperative Manufacturing: A Solution to Share and Exchange the Knowledge

Cooperative manufacturing is a new trend in industry, which depends on the existence of a collaborative robot. A collaborative robot is usually a light-weight robot which is capable of operating safely with a human co-worker in a shared work environment. During this cooperation, a vast amount of information is exchanged between the collaborative robot and the worker. This information constructs the cooperative manufacturing knowledge, which describes the production components and environment. In this research, we propose a holonic control solution, which uses the ontology concept to represent the cooperative manufacturing knowledge. The holonic control solution is implemented as an autonomous multi-agent system that exchanges the manufacturing knowledge based on an ontology model. Ultimately, the research illustrates and implements the proposed solution over a cooperative assembly scenario, which involves two workers and one collaborative robot, whom cooperate together to assemble a customized product

The Paradigm of Pit - Stop Manufacturing

The context in which manufacturing companies are operating is more and more dynamic. Technological and digital innovations are continuously pushing manufacturing systems to change and adapt to new conditions. Therefore, traditional planning strategies tend to be inadequate because both the context and short - term targets are continuously changing. Indeed, one of the goals of manufacturing companies is to keep manufacturing systems efficiently running, and reduce and control the impact of disruptive events, that may originate from different sources, not always known or well defined. In order to do so, manufacturing systems should be kept relatively close to the current optimal condition, while, at the same time, taking into account information about future possible events, which may require new optimal conditions. In fact, the reaction time to the change must be short, in order to remain competitive in the market. In addition companies to be competitive should lead the introduction of changes therefore they have to be both reactive and proactive. From this analysis, the new paradigm of ‘pit - stop manufacturing’ is introduced, in which the overall goal is to dynamically keep the manufacturing system close to an improvement trajectory, instead of statically optimizing the system. It is shown how the ‘pit - stop manufacturing’ deals with various aspects of current manufacturing systems, therefore providing novel research questions and challenges