Hybrid modeling to support the smart manufacturing: concepts, theoretic contributions and real-case applications about Hybrid and Wisdom-based Systems

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Prediction and estimation model of energy demand of the AMR with cobot for the designed path in automated logistics systems

Abstract The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots' physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and preliminary results of the linear regression model is presented

An open source framework for the storage and reuse of industrial knowledge through the integration of PLM and MES

Today, the changes in market requirements and the technological advancements are influencing the product development process. Customers demand a product of high quality and fast delivery at a low price, while simultaneously expecting that the product meet their individual needs and requirements. For companies characterized by a highly customized production, it is essential to reduce the trial-and-errors cycles to design new products and process. In such situation most of the company’s knowledge relies on the lessons learnt by operators in years of work experience, and their ability to reuse this knowledge to face new problems. In order to develop unique product and complex processes in short time, it is mandatory to reuse the acquired information in the most efficient way. Several commercial software applications are already available for product lifecycle management (PLM) and manufacturing execution system (MES). However, these two applications are scarcely integrated, thus preventing an efficient and pervasive collection of data and the consequent creation of useful information. The aim of this paper is to develop a framework able to structure and relate information from design and execution of processes, especially the ones related to anomalies and critical situations occurring at the shop floor, in order to reduce the time for finalizing a new product. The framework has been developed by exploiting open source systems, such as ARAS PLM and PostgreSQL. A case study has been developed for a car prototyping company to illustrate the potentiality of the proposed solution

Development of a key performance indicator framework for automated warehouse systems

Designing effective braking controllers for aircrafts is a challenging task, due to the highly time-varying dynamical features and the very different working conditions of interest. Traditional controllers rely on threshold-based acceleration approaches, but it has been recently proved that slip-based solutions can enhance performance and allow direct tire monitoring. However, also slip-based solutions need adaptation. This paper shows how this can be achieved via LPV data-driven design, with an approach that has its major strength in easing the controller design phase, as it learns both controller parameters and scheduling functions from data. The proposed slip controller ensures both safety and performance, and it is tested within a very realistic simulation setting, endowed with measured noises due to slip estimation errors

Machine learning framework for predictive maintenance in milling

In the Industry 4.0 era, artificial intelligence is transforming the manufacturing industry. With the advent of Internet of Things (IoT) and machine learning methods, manufacturing systems are able to monitor physical processes and make smart decisions through realtime communication and cooperation with humans, machines, sensors, and so forth. Artificial intelligence enables manufacturers to reduce equipment downtime, spot production defects, improve the supply chain, and shorten design times by using machine learning technologies which learn from experiences. One of the last application of these technologies is the development of Predictive Maintenance systems. Predictive maintenance combines Industrial IoT technologies with machine learning to forecast the exact time in which manufacturing equipment will need maintenance, allowing problems to be solved and adaptive decisions to be made in a timely fashion. This study will discuss the implementation of a milling Cutting-tool Predictive Maintenance solution (including Wear Monitoring), applied to a real milling data set as validation of the framework. More generally, this work provides a basic framework for creating a tool to monitor the wear level, preventing the breakdown, of a generic manufacturing tool, in order to improve human-machine interaction and optimize the production process

Materiali e tecnologie odontostomatologiche

Questa terza edizione nasce per rispondere alle crescenti esigenze, dopo l’ottima ricezione, da parte degli studenti e cultori della materia. È dedicata alla Prof.ssa Elettra Dorigo De Stefano, primo docente di Materiali Dentari, Past President del Collegio dei Docenti di Odontoiatria, sotto il cui patrocinio è iniziato il nostro progetto; i suoi consigli sono stati fondamentali per la sua produzione collegiale. Il carisma e la dedizione della prof.ssa Dorigo nei confronti della disciplina sono di esempio per tutte le generazioni future che intendono intraprendere la carriera accademica. Ventuno sedi universitarie, oggi ventisette, hanno aderito a questo progetto; tutte hanno collaborato attivamente fornendo il proprio materiale didattico. Il risultato è questo volume, che rimane sotto il Patrocinio del presidente del Collegio dei Docenti, oggi il prof. Roberto di Lenarda, a cui va il mio ringraziamento. La nuova edizione è stata implementata con nuovi argomenti quali: materiali per l’ingegneria cranio-facciale, per l’igiene orale, per lo sbiancamento, per lo studio e il trattamento dell’alitosi e dispositivi LED in odontoiatria. Inoltre è stato inserito un capitolo apposito per quanto riguarda la ricerca ed analisi al microscopio. Includendo questi nuovi argomenti si creano i fondamenti e si fornisce il know-how agli studenti sia di Odontoiatria che di Igiene Dentale per mettere in dialogo i professionisti del “dentale”. L’indice di questo testo riflette i programmi condivisi dai docenti di disciplina, senza entrare nelle tematiche specialistiche in modo approfondito, per lasciare così al singolo docente spazio per gli approfondimenti che riterrà necessari. Inoltre, per facilitare la comprensione del testo, i diversi argomenti sono stati raggruppati per aree tematiche, caratterizzate da diversi colori. Sono personalmente convinto che molto è ancora da fare e da correggere e pertanto spero, con l’arrivo di suggerimenti, di poter migliorare e aggiornare il testo. Giuseppe Spoto Coordinatore dei Docenti di questo Lavoro Collegiale già Referente della Disciplina “Materiali Dentari e Tecnologie Protesiche di Laboratorio” già Professore ordinario di Materiali Dentari, Prof. inc. di Chimica Medica, Referente Nazionale della sede di Chieti per il Collegio dei Docenti di Odontoiatria, Università “G. d’Annunzio” di Chieti, Dipartimento di Scienze Mediche Orali e Biotecnologiche (via dei Vestini 31, Chieti)