1,721,170 research outputs found
A value-driven method for the design of performance-based services for manufacturing equipment
Full text linkIndustrial services are increasingly becoming more relational and customer-oriented, due to manufacturers' adoption of servitisation approaches and product service system offerings. Challenges remain regarding the effective design and delivery of these new offerings, and the understanding of their actual value for both providers and customers. This work focuses on one specific type of product service systems in the context of manufacturing equipment: result-oriented or performance-based services, which aim at delivering an outcome rather than selling the equipment to the customer. A proposal of a value-driven method for their design that engages the customer in the process is presented. This new method has been applied to a real industrial life setting through an application case, involving the service provider and its customer, and targeting manufacturing equipment within customers' plant. Results indicate the effectiveness of this prescriptive approach. Reported benefits from participants refer to its flexibility, adaptability and applicability for different types of equipment, as well as its potential to help providing a modular service portfolio adequate to equipment specific context and requirements
Maintenance concepts evolution: a comparative review towards Advanced Maintenance conceptualization
Full text linkThe implementation of Industry 4.0-like solutions for the maintenance of production assets is a relevant topic in the mainstream for researchers and industries around the world. As a matter of facts, the technology-based transformation of maintenance has been germinated since several years. In fact, the evolution of maintenance along with the development of the information and communication technologies has been studied in the literature since early 2000, and concepts like e-maintenance and intelligent maintenance have been largely addressed. Nowadays, smart maintenance and maintenance 4.0 concepts are getting popular in the Industry 4.0-based literature. While e-maintenance, intelligent maintenance, smart maintenance and maintenance 4.0 are similar concepts, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, connotation, and technical development of the concepts are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of the scientific literature to clarify the relationship among the different maintenance concepts. A bibliometric analysis of publication sources, annual publication numbers, keywords frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include the evolutionary path of the different concepts. Moreover, the evidence collected through a case study involving eight production companies are discussed to report the perspective of industry about advanced maintenance, may it be defined 4.0, smart, intelligent or e-maintenance. Finally, a definition of the advanced maintenance concept is given, proposed as an integral approach inheriting the knowledge from past developments of e-maintenance and intelligent maintenance concepts and more recent developments including smart maintenance and maintenance 4.0
A Digital Twin Proof of Concept to Support Machine Prognostics with Low Availability of Run-To-Failure Data
Full text linkThe present research illustrates a Digital Twin Proof of Concept to support machine prognostics with Low Availability of Run-to-Failure Data. Developed in the scope of the Industry 4.0 Lab of the Manufacturing Group of the School of Management of Politecnico di Milano, the Digital Twin is capable to run in parallel to the drilling machine operations and, as such, it enables to predict the evolution of the most critical failure mode, that is the imbalance in the drilling axis. The real-time monitoring of the drilling machine is realized with a low-cost and retrofit solution, which provides the installation of a Raspberry-Pi accelerometer, able to enhance the extant automation. Relying on a joint use of real-time monitoring and simulation, the Digital Twin implements a random coefficient statistical method through the so-called Exponential Degradation Model, eventually demonstrating to increase the prediction precision as monitoring data arrives. The Digital Twin Proof of Concept is described according to the entire process from data acquisition to Remaining Useful Life prediction, following the MIMOSA OSA-CBM standards
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A framework to integrate novelty detection and remaining useful life prediction in Industry 4.0-based manufacturing systems
Full text linkThe capability to predict the behaviour of machines is nowadays experiencing a tremendous growth of interest within Industry 4.0-based manufacturing systems. The route to this end is not straightforward when Run-To-Failure (RTF) data are poorly available or not available at all, thus a strategy must be properly defined. In this proposal, assuming no RTF data, a novelty detection is combined with random coefficient statistical modelling for Remaining Useful Life (RUL) prediction. This approach is formalized by means of a reference framework extending the ISO 13374–OSA-CBM standards. The framework guides the integration of novelty detection and RUL prediction finally implemented in the scope of a Flexible Manufacturing Line part of the Industry 4.0 Lab of the School of Management of Politecnico di Milano
Analysing the support of sustainability within the manufacturing strategy through multiple perspectives of different business functions
Full text linkThis paper proposes an empirical study aimed at characterizing the evolution of a company towards a sustainable manufacturing strategy, with a special emphasis on the role played by the business functions within the industrial organization. In the study, a methodology to evaluate and rank the sustainable manufacturing strategy in different production contexts is developed, stemming from the fact that there is a lack of objective methods for sustainable manufacturing strategy ranking in the literature. The analysis method consists of an Analytic Hierarchy Process applied to competitive priorities and manufacturing performances. It enables a structured reflection that considers the multiple perspectives of different decision-makers in business functions relevant to the implementation of sustainability in the manufacturing strategy. The main objective of the approach proposed in this study is to provide a methodology able to give an integrated view of the economic, environmental and social dimensions of the manufacturing plants. The proposed methodology is applied in two application case studies referring to two different production contexts. The application cases show the usefulness of the methodology to assess how sustainability is supported in the manufacturing strategy, with specific concern to the evolution of a manufacturing plant towards a sustainable manufacturing strategy
Towards a Maturity Model for Intelligent Digital Twins in Manufacturing
Full text linkThe adoption of Digital Twins (DTs) in manufacturing is promising as they hold the potential to bring key changes in the way industrial systems are managed and optimized. As enabling technologies continue to emerge, their integration into these complex systems enhances the granularity of intelligence and functionalities available within the Cyber-physical Systems. Such granularity accentuates the need to clarify the distinctive features of this emerging class of DTs, where the transition from Conventional DTs to Intelligent DTs marks a shift towards increasingly proactive, autonomous, and cognitive systems. This paper particularly explores the evolution of Intelligent DTs, focusing on formalizing the enhanced intelligence in correspondent maturity levels that express the progression on DT systems. The maturity levels of Intelligent DTs are categorized into predictive, prescriptive, and autonomous stages, to then identify both the associated capabilities and technologies across different application domains related to manufacturing. On its whole, the outcome of this paper can be seen both as a current map on the maturity of Intelligent DTs implementation, and as a research agenda to explore capabilities and technologies from different domains in manufacturing
A framework for fault detection and diagnostics of articulated collaborative robots based on hybrid series modelling of Artificial Intelligence algorithms
Full text linkSmart factories build on cyber-physical systems as one of the most promising technological concepts. Within smart factories, condition-based and predictive maintenance are key solutions to improve competitiveness by reducing downtimes and increasing the overall equipment effectiveness. Besides, the growing interest towards operation flexibility has pushed companies to introduce novel solutions on the shop floor, leading to install cobots for advanced human-machine collaboration. Despite their reliability, also cobots are subjected to degradation and functional failures may influence their operation, leading to anomalous trajectories. In this context, the literature shows gaps in what concerns a systematic adoption of condition-based and predictive maintenance to monitor and predict the health state of cobots to finally assure their expected performance. This work proposes an approach that leverages on a framework for fault detection and diagnostics of cobots inspired by the Prognostics and Health Management process as a guideline. The goal is to habilitate first-level maintenance, which aims at informing the operator about anomalous trajectories. The framework is enabled by a modular structure consisting of hybrid series modelling of unsupervised Artificial Intelligence algorithms, and it is assessed by inducing three functional failures in a 7-axis collaborative robot used for pick and place operations. The framework demonstrates the capability to accommodate and handle different trajectories while notifying the unhealthy state of cobots. Thanks to its structure, the framework is open to testing and comparing more algorithms in future research to identify the best-in-class in each of the proposed steps given the operational context on the shop floor
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