This doctoral thesis is the result of the supervision and collaboration of the University of Basilicata, the Polytechnic of Bari, and the enterprise Master Italy s.r.l. The main research lines explored and discussed in the thesis are: sustainability in general and, more specifically, manufacturing sustainability, the Industry 4.0 paradigm linked to smart (green) manufacturing, model-based assessment techniques of manufacturing processes, and data-driven analysis methodologies. These seemingly unrelated topics are handled throughout the thesis in such a way that it reveal how strongly interwoven and characterised by transversality they are. The goal of the PhD programme was to design and validate innovative assessment models in order to investigate the nature of manufacturing processes and rationalize the relationships and correlations between the different stages of the process. This composite model may be utilized as a tool in political decision-making about the long-term development of industrial processes and the continuous improvement of manufacturing processes. The overarching goal of this research is to provide strategies for real-time monitoring of manufacturing performance and sustainability based on hybrid thermodynamic models of the first and second order, as well as those based on data and machine learning. The proposed model is tested on a real industrial case study using a systemic approach: the phases of identifying the requirements, data inventory (materials, energetic, geometric, physical, economic, social, qualitative, quantitative), modelling, analysis, ad hoc algorithm adjustment (tuning), implementation, and validation are developed for the aluminium alloy die-casting processes of Master Italy s.r.l., a southern Italian SME which designs and produces the accessories and metal components for windows since 1986. The thesis digs in the topic of the sustainability of smart industrial processes from each and every perspective, including both the quantity and quality of resources used throughout the manufacturing process's life cycle. Traditional sustainability analysis models (such as life cycle analysis, LCA) are combined with approaches based on the second law of thermodynamics (exergetic analysis); they are then complemented by models based on information technology (big-data analysis). A full analysis of the potential of each strategy, whether executed alone or in combination, is provided. Following a summary of the metrics relevant for determining the degree of sustainability of industrial processes, the case study is demonstrated using modelling and extensive analysis of the processes, namely aluminium alloy die casting. After assessing the sustainability of production processes using a model-based approach, we move on to the real-time application of machine learning analyses with the goal of identifying downtime and failures during the production cycle and predicting their occurrence well in advance using real-time process thermodynamic parameter values and automatic learning. Finally, the thesis suggests the use of integrated models on various case studies, such as laser deposition processes and the renovation of existing buildings, to demonstrate the multidisciplinarity and transversality of these issues. The thesis reveals fascinating findings derived from the use of a hybrid method to assessing the sustainability of manufacturing processes, combining exergetic analysis with life cycle assessment. The proposed theme is completely current and relevant to the most recent developments in the field of industrial sustainability, combining traditional model-based approaches with innovative approaches based on the collection of big data and its analysis using the most appropriate machine learning methodologies. Furthermore, the thesis demonstrates a highly promising application of machine learning approaches to real-time data collected in order to identify any fault source in the manufacturing line beginning with sustainability measures generated from exergetic analysis and life cycle analysis. As such, it unquestionably represents an advancement above earlier information depicted in the initial state of the art. In actuality, manufacturing companies that implement business strategies based on smart models and key enabling technologies today have a higher market value in terms of quality, customisation, flexibility, and sustainability.

Innovative thermodynamic hybrid model-based and data-driven techniques for real time manufacturing sustainability assessment / Selicati, Valeria. - (2022 Jul 19).

Innovative thermodynamic hybrid model-based and data-driven techniques for real time manufacturing sustainability assessment

SELICATI, VALERIA
2022-07-19

Abstract

This doctoral thesis is the result of the supervision and collaboration of the University of Basilicata, the Polytechnic of Bari, and the enterprise Master Italy s.r.l. The main research lines explored and discussed in the thesis are: sustainability in general and, more specifically, manufacturing sustainability, the Industry 4.0 paradigm linked to smart (green) manufacturing, model-based assessment techniques of manufacturing processes, and data-driven analysis methodologies. These seemingly unrelated topics are handled throughout the thesis in such a way that it reveal how strongly interwoven and characterised by transversality they are. The goal of the PhD programme was to design and validate innovative assessment models in order to investigate the nature of manufacturing processes and rationalize the relationships and correlations between the different stages of the process. This composite model may be utilized as a tool in political decision-making about the long-term development of industrial processes and the continuous improvement of manufacturing processes. The overarching goal of this research is to provide strategies for real-time monitoring of manufacturing performance and sustainability based on hybrid thermodynamic models of the first and second order, as well as those based on data and machine learning. The proposed model is tested on a real industrial case study using a systemic approach: the phases of identifying the requirements, data inventory (materials, energetic, geometric, physical, economic, social, qualitative, quantitative), modelling, analysis, ad hoc algorithm adjustment (tuning), implementation, and validation are developed for the aluminium alloy die-casting processes of Master Italy s.r.l., a southern Italian SME which designs and produces the accessories and metal components for windows since 1986. The thesis digs in the topic of the sustainability of smart industrial processes from each and every perspective, including both the quantity and quality of resources used throughout the manufacturing process's life cycle. Traditional sustainability analysis models (such as life cycle analysis, LCA) are combined with approaches based on the second law of thermodynamics (exergetic analysis); they are then complemented by models based on information technology (big-data analysis). A full analysis of the potential of each strategy, whether executed alone or in combination, is provided. Following a summary of the metrics relevant for determining the degree of sustainability of industrial processes, the case study is demonstrated using modelling and extensive analysis of the processes, namely aluminium alloy die casting. After assessing the sustainability of production processes using a model-based approach, we move on to the real-time application of machine learning analyses with the goal of identifying downtime and failures during the production cycle and predicting their occurrence well in advance using real-time process thermodynamic parameter values and automatic learning. Finally, the thesis suggests the use of integrated models on various case studies, such as laser deposition processes and the renovation of existing buildings, to demonstrate the multidisciplinarity and transversality of these issues. The thesis reveals fascinating findings derived from the use of a hybrid method to assessing the sustainability of manufacturing processes, combining exergetic analysis with life cycle assessment. The proposed theme is completely current and relevant to the most recent developments in the field of industrial sustainability, combining traditional model-based approaches with innovative approaches based on the collection of big data and its analysis using the most appropriate machine learning methodologies. Furthermore, the thesis demonstrates a highly promising application of machine learning approaches to real-time data collected in order to identify any fault source in the manufacturing line beginning with sustainability measures generated from exergetic analysis and life cycle analysis. As such, it unquestionably represents an advancement above earlier information depicted in the initial state of the art. In actuality, manufacturing companies that implement business strategies based on smart models and key enabling technologies today have a higher market value in terms of quality, customisation, flexibility, and sustainability.
data-driven approach; exergy; I4.0; integration modelling; life cycle assessment; model-based approach; smart manufacturing; sustainability assessment.
Innovative thermodynamic hybrid model-based and data-driven techniques for real time manufacturing sustainability assessment / Selicati, Valeria. - (2022 Jul 19).
File in questo prodotto:
File Dimensione Formato  
SELICATI V. - Innovative thermodynamic hybrid model-based and data-driven techniques for real time manufacturing sustainability assessment.pdf

embargo fino al 19/07/2023

Descrizione: Tesi di Dottorato
Tipologia: Tesi di dottorato
Licenza: Dominio pubblico
Dimensione 14.04 MB
Formato Adobe PDF
14.04 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/157566
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact