Benefits of industry 4.0 in foundry engineering’s greensand moulding process

Abstract: There is a need to introduce modern technologies to address inefficiencies in foundry engineering. The foundry industry is very old dating back as far as 1479. The early foundry engineers produced metal castings which were mainly cannons and bells. Foundries have been slow to adapt to disruptive technologies. However with the 4th industrial revolution foundries cannot afford to miss out. Foundry Engineering which is metal casting is under a lot of pressure from other competing manufacturing technologies. Forging, fabrications and 3D metal printing, plastic and composite materials are competitors to metal casting. The most common and cheapest way of producing castings is in greensand. This is due to the fact that it uses low cost raw materials. Though the process is cheaper than other casting processes. There is always a need for improving efficiencies in the means of production to compete with other manufacturing technologies. The 4th industrial revolution has become a pillar of improving competiveness in the metal casting process. This paper evaluates how the first cloud based green sand data analytic software Sandman plays a role in contributing towards the achievement of the sustainable development goals in African foundries. The greensand data analytic programme has been seen to be a key resource in driving for responsible consumption and production. This study will provide knowledge on the benefits of using a data analytic software in greensand moulding

Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0

The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK). This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing. A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems. Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital ‘sense to action’ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode. Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes

Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review

Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the process and product quality. Many frameworks for implementing CPS have been developed to structure the relationship between the digital and the physical worlds. However, there is no systematic review of the existing frameworks related to quality management in manufacturing CPS. Thus, our study aims at determining and comparing the existing frameworks. The systematic review yielded 38 frameworks analyzed regarding their characteristics, use of data science and Machine Learning (ML), and shortcomings and open research issues. The identified issues mainly relate to limitations in cross-industry/cross-process applicability, the use of ML, big data handling, and data security.publishedVersio

Concept of Socio-Cyber-Physical Work Systems for Industry 4.0

In this paper the concepts of advanced production systems based on the challenges that bring the new industrial revolution named - Industry 4.0 are presented. The presented concept of socio-cyber-physical work systems is based on connecting social, cyber and physical working environments into a single functional, productive entity of the appointed elementary socio-cyber-physical work system.The elementary socio-cyber-physical work system is a basic building block of the cyber-physical production systems at the manufacturing level. The cyber system of the elementary socio-cyber-physical work system enables autonomous decision-making and cooperation in the network system. The possibility of implementing the proposed concept is based on the introduction of agency technologies in the domain of modern production systems and the development of information-communication technologies for the advanced management and control of cyber-physical production systems. Some illustrative examples reflect the experimental results of a research work in the field of cyber-physical systems and demonstrate the potential possibilities of implementing the concept of socio-cyber-physical work systems in the real industrial environment

Survey on Additive Manufacturing, Cloud 3D Printing and Services

Cloud Manufacturing (CM) is the concept of using manufacturing resources in a service oriented way over the Internet. Recent developments in Additive Manufacturing (AM) are making it possible to utilise resources ad-hoc as replacement for traditional manufacturing resources in case of spontaneous problems in the established manufacturing processes. In order to be of use in these scenarios the AM resources must adhere to a strict principle of transparency and service composition in adherence to the Cloud Computing (CC) paradigm. With this review we provide an overview over CM, AM and relevant domains as well as present the historical development of scientific research in these fields, starting from 2002. Part of this work is also a meta-review on the domain to further detail its development and structure

Mass Production Processes

It is always hard to set manufacturing systems to produce large quantities of standardized parts. Controlling these mass production lines needs deep knowledge, hard experience, and the required related tools as well. The use of modern methods and techniques to produce a large quantity of products within productive manufacturing processes provides improvements in manufacturing costs and product quality. In order to serve these purposes, this book aims to reflect on the advanced manufacturing systems of different alloys in production with related components and automation technologies. Additionally, it focuses on mass production processes designed according to Industry 4.0 considering different kinds of quality and improvement works in mass production systems for high productive and sustainable manufacturing. This book may be interesting to researchers, industrial employees, or any other partners who work for better quality manufacturing at any stage of the mass production processes

Time Series Classification using Deep Learning for Process Planning: A Case from the Process Industry

Multivariate time series classification has been broadly applied in diverse domains over the past few decades. However, before applying the classification algorithms, the vast majority of current studies extract hand-engineered features that are assumed to detect local patterns in the time series. Therefore, the efficiency and precision of these classification approaches are heavily dependent on the quality of variables defined by domain experts. Recent improvements in the deep learning domain offer opportunities to avoid such an intensive hand-crafted feature engineering which is particularly important for managing the processes based on time-series data obtained from various sensor networks. In our paper, we propose a framework to extract the features in an unsupervised (or self-supervised) manner using deep learning, particularly stacked LSTM Autoencoder Networks. The compressed representation of the time-series data obtained from LSTM Autoencoders are then provided to Deep Feedforward Neural Networks for classification. We apply the proposed framework on sensor time series data from the process industry to detect the quality of the semi-finished products and accordingly predict the next production process step. To validate the efficiency of the proposed approach, we used real-world data from the steel industry

A cyber-physical system approach to zero-defect manufacturing in light-gauge steel frame assemblies

Recent advances in manufacturing research have set the stage for the industrial integration of zero-defect manufacturing strategies, aiming towards a more sustainable production paradigm. A systematically deployment guideline to implement zero-defect manufacturing is needed to transform the future cyber-physical factory floor. This paper describes a novel framework based on a well-known cyber-physical architecture that provides advanced information analytics, robust information flows, and data acquisition systems that support defect detection and prediction introduces repair technologies and sets up procedures for preventive maintenance. Through continuous inspection of product and equipment status during the manufacturing process, raw quality and tool health data from different sources is used to obtain key performance indicators. Statistical tools such as cross-correlation are then applied to quantify underlying relationships between product quality specifications and equipment health. The resulting correlations are then used to reduce non-conformance of products manufactured by implementation of preventive maintenance. A unified implementation for zero-defect manufacturing cyber-physical processes eases their integration in future Industry 4.0 facilities and validated in the context of offsite construction manufacturing of steel frame assemblies

Remanufacturing and Advanced Machining Processes for New Materials and Components

"Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems.