Maintenance Strategies to Reduce Downtime Due to Machine Positional Errors

Manufacturing strives to reduce waste and increase Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime competently and similarly identify the machines’ performance. Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a powerful metric of manufacturing performance incorporating measures of the utilisation, yield and efficiency of a given process, machine or manufacturing line. It supports TPM initiatives by accurately tracking progress towards achieving “perfect production.” This paper presents a review of maintenance management methodologies and their application to positional error calibration decision-making. The purpose of this review is to evaluate the contribution of maintenance strategies, in particular TPM, towards improving manufacturing performance, and how they could be applied to reduce downtime due to inaccuracy of the machine. This is to find a balance between predictive calibration, on-machine checking and lost production due to inaccuracy. This work redefines the role of maintenance management techniques and develops a framework to support the process of implementing a predictive calibration program as a prime method to supporting the change of philosophy for machine tool calibration decision making. Keywords—maintenance strategies, down time, OEE, TPM, decision making, predictive calibration

Full, hybrid and platform complementarity: Exploring the industry 4.0 technology-performance link

Literature has increasingly recognized that manufacturing companies should implement a synergic bundle of solutions to fully exploit the potential of Industry 4.0 (I4.0), rather than opting for a scattered technological adoption. Enabling I4.0 technologies, such as cloud computing, artificial intelligence, and additive manufacturing, can be implemented through various combinations to achieve different impacts on a company's performance. But what are the possible ways of combining I4.0 technologies into bundles, and do these ways actually help to achieve a performance that outperforms the adoption of single technologies? This study aims to identify the potential patterns of the technological complementary of I4.0 by considering enabled applications and performance outcomes. We interviewed 13 Italian experts in the I4.0 field, and then combined the obtained information with secondary data collected from more than 150 I4.0 use cases, as well as from websites, reports and press releases. By adopting a systems theory lens, the results of the analysis have allowed us to identify the specific performance effects of both scattered and joint technological adoptions in different application areas. Interestingly, specific examples of I4.0 complementarities emerged, namely full, hybrid and platform complementarity. This study contributes to the growing research on I4.0 outcomes by extending the concept of technological complementary within the I4.0 context. Results show that bundles of technologies have a broader effect on performance than when the same technologies are adopted in isolation, but also that single technologies can impact specific applications and the overall performance of a firm via a systematic I4.0 transformation path

Blockchain Technology Helps Maintenance to Stop Climate Change

The development and interest in Industry 4.0 together with rapid development of Cyber Physical Systems has created magnificent opportunities to develop maintenance to a totally new level. The Maintenance 4.0 vision considers massive exploitation of information regarding factories and machines to improve maintenance efficiency and efficacy, for example by facilitating logistics of spare parts, but on the other hand this creates other logistics issues on the data itself, which only exacerbate data management issues that emerge when distributed maintenance platforms scale up. In fact, factories can be delocalized with respect to the data centers, where data has to be transferred to be processed. Moreover, any transaction needs communication, be it related to purchase of spare parts, sales contract, and decisions making in general, and it has to be verified by remote parties. Keeping in mind the current average level of Overall Equipment Efficiency (50%) i.e. there is a hidden factory behind every factory, the potential is huge. It is expected that most of this potential can be realised based on the use of the above named technologies, and relying on a new approach called blockchain technology, the latter aimed at facilitating data and transactions management. Blockchain supports logistics by a distributed ledger to record transactions in a verifiable and permanent way, thus removing the need for multiple remote parties to verify and store every transaction made, in agreement with the first “r” of maintenance (reduce, repair, reuse, recycle). Keeping in mind the total industrial influence on the climate change, we can expect that with the aid of the new advancements the climate change can be if not totally stopped at least reduced, and contribute to the green economy that Europe aims for. The paper introduces the novel technologies that can support sustainability of manufacturing and industry at large, and proposes an architecture to bind together said technologies to realise the vision of Maintenance 4.0.info:eu-repo/semantics/publishedVersio

A Framework for Improving Manufacturing Overall Equipment Effectiveness

The main motivation for improving productivity is to develop and implement manufacturing methods and concepts that provide stable, flexible and low cost production with high quality levels.  However in the pursuit of increasing competitiveness may lead to plant downtime; which must be minimized wherever possible.  Overall equipment effectiveness (OEE) is a metric to measure equipment performance and effectiveness and therefore, reduce equipment cost of ownership (COO). OEE topic has become progressively popular and widely used as a research discussion in operation management.  However, OEE framework for previous studies was developed on a piecemeal basis. This paper presents a new and complete conceptual framework that illustrates the most important factors that influence and contribute to OEE improvement.  The comprehensive framework is able to provide effective guidance and direction to industry practitioner on how to improve OEE

New Trends in the Use of Artificial Intelligence for the Industry 4.0

Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0

Cyber-physical manufacturing systems: An architecture for sensor integration, production line simulation and cloud services

none9noThe pillars of Industry 4.0 require the integration of a modern smart factory, data storage in the Cloud, access to the Cloud for data analytics, and information sharing at the software level for simulation and hardware-in-the-loop (HIL) capabilities. The resulting cyber-physical system (CPS) is often termed the cyber-physical manufacturing system, and it has become crucial to cope with this increased system complexity and to attain the desired performances. However, since a great number of old production systems are based on monolithic architectures with limited external communication ports and reduced local computational capabilities, it is difficult to ensure such production lines are compliant with the Industry 4.0 pillars. A wireless sensor network is one solution for the smart connection of a production line to a CPS elaborating data through cloud computing. The scope of this research work lies in developing a modular software architecture based on the open service gateway initiative framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis and enable both HIL and cloud computing capabilities. The CPS architecture was initially tested using HIL tools before it was deployed within a real manufacturing line for data collection and analysis over a period of two months.openPrist Mariorosario; Monteriu' Andrea; Pallotta Emanuele; Cicconi Paolo; Freddi Alessandro; Giuggioloni Federico; Caizer Eduard; Verdini Carlo; Longhi SauroPrist, Mariorosario; Monteriu', Andrea; Pallotta, Emanuele; Cicconi, Paolo; Freddi, Alessandro; Giuggioloni, Federico; Caizer, Eduard; Verdini, Carlo; Longhi, Saur

The effect of Total Productive Management practices on manufacturing performance through SECS/GEM Standard for electronic contract manufacturing companies (TOC, Abstract, chapter 1 and Reference only)

In an environment of intense global competition, it pays to consider both creative and proven systems that can be used to bring about effective and efficient manufacturing operation. Many electronic contract manufacturing companies have put forth huge amounts of effort and resources to achieve precise and reliable measurement of equipment performance. The objective of a concise measurement is to optimise this piece of asset for every dollar invested. However, it has failed on numerous attempts to achieve the desirable result due to hardware limitations, low degrees of data accuracy and the need for manual intervention. Integrating Total Productive Maintenance (TPM) methodology with SEMI Equipment Communication Standard (SECS) with Generic Equipment Model (GEM) enables data acquisition in a concise manner and keeps track of all real-time transactions that have taken place between the operator and the equipment. To achieve this integration process, a fast-track TPM implementation approach is required by re-engineering the TPM implementation process. The Re-Engineered TPM approach comprises of three TPM pillars (Asset Productivity (AP), Autonomous Maintenance (AM) and Planned Maintenance (PM)) instead of the original eight pillars. Apart from three TPM pillars, also included are SECS/GEM standard, direct and indirect labour utilisation hours, material and overhead cost. The main objective of this study is to determine whether the re-engineering effort, based on these three TPM pillars, SECS/GEM standard together with labour and cost, are able to minimise losses in production process and have positive impact on Output (Manufacturing Performance). The study also aims at evaluating whether the SECS/GEM standard integration with Autonomous Maintenance has the capability of real-time monitoring equipment performance on the production floor. Furthermore, the study aims to assess the impact on productivity, namely, the Output (Manufacturing Performance). The three years, monthly data for the study was collected from ten Electronic Contract Manufacturing (ECM) companies in Johor, Malaysia. The data was analysed through descriptive statistics, regression analysis and panel data analysis. Based on the panel data analysis, the Hausman Test revealed that the Fixed Effects model was found to be the optimal model for this study. The result shows that six independent variables were significant, while one independent variable was not. The insignificant independent variable was SECS/GEM standard integration with Autonomous Maintenance. Further analysis was conducted through a qualitative study. The additional analysis shows that ECM companies do not fully understand the possible application of the SECS/GEM standard integration with Autonomous Maintenance in their manufacturing environment. Therefore, minimum effort was deployed by ECM companies in incorporating this standard into their equipment maintenance platform. However, these days many ECM companies have started to purchase equipment with SECS/GEM standard in order to facilitate smoother future integration with Autonomous Maintenance or with other TPM pillars. This total integration of TPM (three pillars), SECS/GEM standard, labour and cost provides an avenue to monitor and address the operational losses in the production equipment in a timely manner. This system paves the way to improving Output (Manufacturing Performance)

Regionalized implementation strategy of smart automation within assembly systems in China

Produzierende Unternehmen in aufstrebenden Nationen wie China, sind bestrebt, die Produktivität der Produktion durch eine Verbesserung der Lean Produktion mit disruptiven Technologien zu erreichen. Smart Automation ist dabei eine vielversprechende Lösung, allerdings können Unternehmen aufgrund von mangelnden Ressourcen oft nicht alle Smart Automation Technologien gleichzeitig implementieren. Ebenso beeinflusst eine Vielzahl an Einflussfaktoren, wie z.B. Standortfaktoren. Dementsprechend herausfordernd ist die Auswahl und Priorisierung von Smart Automation Technologien in Form von Einführungsstrategien für produzierende Unternehmen. Der Stand der Forschung untersucht nur unzureichend die Analyse der Interdependenzen zwischen Standortfaktoren, Smart Automation Technologien und Key Performance Indikatoren (KPIs). Darüber hinaus mangelt es an einer Methode zur Ableitung der Einführungsstrategie von Smart Automation Technologien unter Berücksichtigung dieser Interdependenzen. Entsprechend trägt diese Arbeit dazu bei, eine regionalisierte Einführungsstrategie von Smart Automation Technologien in Montagesystemen zu ermöglichen. Zunächst werden die Standortfaktoren, Smart Automation Technologien und KPIs identifiziert. In einem zweiten Schritt werden, mit Hilfe von qualitativen und quantitativen Analysen, die Interdependenzen bestimmt. Anschließend werden diese Interdependenzen auf ein Montagesystem mittels hybrider Modellierung und Simulation übertragen. Im vierten Schritt wird eine regionalisierte Einführungsstrategie durch eine Optimierung und eine Monte-Carlo-Simulation abgeleitet. Die Methodik wurde im Rahmen des deutsch-chinesischen Forschungsprojekts I4TP entwickelt, das vom Bundesministerium für Bildung und Forschung (BMBF) unterstützt wird. Die Validierung wurde erfolgreich mit einem produzierenden Unternehmen in Beijing durchgeführt. Die entwickelte Methodik stellt einen neuartigen Ansatz zur Entscheidungsunterstützung bei der Entwicklung einer regionalisierten Einführungsstrategie für Smart Automation Technologien in Montagesystemen dar. Dadurch sind produzierende Unter-nehmen in der Lage, individuelle Einführungsstrategien für disruptive Technologien auf Basis wissenschaftlicher und rationaler Analysen effektiv abzuleiten