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In this paper we present the Interuniversity Research Center STEERING, formed in June 2017. The Research Center has been founded by three Italian Universities through five Departments. It represents the connection between Statistics and Engineering. The five Departments promoting it are the following: Department of Innovation and Information Engineering (Guglielmo Marconi University, Rome); Department of Statistics Computer Science Applications, Department of Information Engineering, Department of Industrial Engineering (University of Florence); Department of Mechanical and Civil Engineering (University of Cassino and Lazio Meridionale). The potentiality of the Research Center and some of its aims are explained through three empirical case studies.
G. Arcidiacono; R. Berni; N. Bonora; M. Catelani; M. Pierini. Interuniversity Research Center “STEERING” - STatistics for EnginERING: Design, Quality and Reliability. Procedia Structural Integrity 2018, 8, 168 -173.
AMA StyleG. Arcidiacono, R. Berni, N. Bonora, M. Catelani, M. Pierini. Interuniversity Research Center “STEERING” - STatistics for EnginERING: Design, Quality and Reliability. Procedia Structural Integrity. 2018; 8 ():168-173.
Chicago/Turabian StyleG. Arcidiacono; R. Berni; N. Bonora; M. Catelani; M. Pierini. 2018. "Interuniversity Research Center “STEERING” - STatistics for EnginERING: Design, Quality and Reliability." Procedia Structural Integrity 8, no. : 168-173.
This paper deals with Kriging modeling applied for optimizing the braking performances for freight trains. In particular, it focuses on mass distribution optimization to reduce the effects of in-train forces among vehicles, e.g. compression and tensile forces, in-train emergency braking. Kriging models are applied with covariance structure based on the Matérn function, and by introducing specific input parameters to better outline the payload distribution on the train, by also evaluating the shape of the payload distribution. Satisfactory results have been obtained considering compression forces, tensile forces and their sum, and by also evaluating residuals and diagnostic measures.
G. Arcidiacono; R. Berni; L. Cantone; N.D. Nikiforova; P. Placidoli. A Kriging modeling approach applied to the railways case. Procedia Structural Integrity 2018, 8, 163 -167.
AMA StyleG. Arcidiacono, R. Berni, L. Cantone, N.D. Nikiforova, P. Placidoli. A Kriging modeling approach applied to the railways case. Procedia Structural Integrity. 2018; 8 ():163-167.
Chicago/Turabian StyleG. Arcidiacono; R. Berni; L. Cantone; N.D. Nikiforova; P. Placidoli. 2018. "A Kriging modeling approach applied to the railways case." Procedia Structural Integrity 8, no. : 163-167.
Project-Based Learning is a method based on constructivist finding, its application is centred on project development as the learning tool catalysing knowledge discovery. Project-Based learning have been traditionally designed and implemented on a know-how and trial-and-error basis, but tasks and decisions taken during the design phases of the training modules have a substantial effect on its quality and outcomes. Axiomatic Design can contribute to improve the outcomes opportunities and the process efficiency by identifying where complexity exists within the requirements and design activities that underpin the model. In this study, the Axiomatic Design method is applied to link learning outcomes of Lean Six Sigma training with all the teaching processes and the availability of resources. As a conclusion some improvement suggestions are made to optimize the learning and teaching methodology in order to maximize the learner outcomes.
Gabriele Arcidiacono; Kai Yang; Jayant Trewn; Luca Bucciarelli. Application of Axiomatic Design for Project-based Learning Methodology. Procedia CIRP 2016, 53, 166 -172.
AMA StyleGabriele Arcidiacono, Kai Yang, Jayant Trewn, Luca Bucciarelli. Application of Axiomatic Design for Project-based Learning Methodology. Procedia CIRP. 2016; 53 ():166-172.
Chicago/Turabian StyleGabriele Arcidiacono; Kai Yang; Jayant Trewn; Luca Bucciarelli. 2016. "Application of Axiomatic Design for Project-based Learning Methodology." Procedia CIRP 53, no. : 166-172.
Companies should align production systems according to their overall strategy and consider the strategic goals of the organization as a whole. To be competitive and profitable, it is not sufficient to improvise, although it is necessary to consider all the variables and scenarios and accommodate the different contexts and situations as appropriate. To improve their competitive abilities and to enhance cost-reduction opportunities and process efficiency, organizations are bringing about improvements in their operations and processes, adding global operations optimization to a global manufacturing strategy. However companies’ ability to develop sustainable competitive advantage from these improvements is hampered by the lack of objective approaches for targeting their improvement efforts. There are significant limitations to the approaches used for project selection and prioritization, therefore the purpose of this work is to provide a structured approach, using Axiomatic Design (AD) principles, to identify and prioritize the best projects that are conducive to process excellence and performance improvement. Through the application of the Axiomatic Design method, we identify where complexity exists within the requirements and design activities that underpin the model. Using this analysis, this work identifies the critical points within the Project Identification and Prioritization model, and suggests necessary improvements to facilitate the implementation of Process Efficiency within a company.
Gabriele Arcidiacono; Christopher A. Brown; Luca Bucciarelli; Francesco Melosi. Axiomatic Design of Production Systems for Performance Improvement: A Project Identification and Prioritization Model. Axiomatic Design in Large Systems 2016, 251 -272.
AMA StyleGabriele Arcidiacono, Christopher A. Brown, Luca Bucciarelli, Francesco Melosi. Axiomatic Design of Production Systems for Performance Improvement: A Project Identification and Prioritization Model. Axiomatic Design in Large Systems. 2016; ():251-272.
Chicago/Turabian StyleGabriele Arcidiacono; Christopher A. Brown; Luca Bucciarelli; Francesco Melosi. 2016. "Axiomatic Design of Production Systems for Performance Improvement: A Project Identification and Prioritization Model." Axiomatic Design in Large Systems , no. : 251-272.
Research of solutions to problem in existing processes often deals with the tendency to follow mental schemes because of the psychological inertia. This study illustrates a knowledge-based systematic methodology of inventive problem solving for the effective development of new systems and solutions, a theory that consists of theoretical foundation, analytical and knowledge-based tools, applicable in conjunction with other creativity and engineering methodologies. The TRIZ (Theory of Inventive Problem Solution) applicability in process reliability can develop new and effective solutions thanks to the examination of contradiction, different prospective, and points of view. The systematic innovation process provides a platform to integrate heterogeneous resources and tools opening the problem-solving methodology to new and different interdisciplinary approaches. The deconstruction and identification of issues to analyze the problems in their context and in relation to other factors are adopted in many areas of industrial production as well as in the more general problem-solving matters. In particular, this study will show how TRIZ can be used in process optimization rather than research and development where this methodology is commonly adopted. This case represents a practical application of the TRIZ to increase quality and reliability in regard to a manufacturing process of an industry that designs and builds molds and equipment for the production of aluminum food containers. Copyright © 2016 John Wiley & Sons, Ltd.
G. Arcidiacono; L. Bucciarelli. TRIZ: Engineering Methodologies to Improve the Process Reliability. Quality and Reliability Engineering International 2016, 32, 2537 -2547.
AMA StyleG. Arcidiacono, L. Bucciarelli. TRIZ: Engineering Methodologies to Improve the Process Reliability. Quality and Reliability Engineering International. 2016; 32 (7):2537-2547.
Chicago/Turabian StyleG. Arcidiacono; L. Bucciarelli. 2016. "TRIZ: Engineering Methodologies to Improve the Process Reliability." Quality and Reliability Engineering International 32, no. 7: 2537-2547.