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Tesi etd-11032025-021022


Tipo di tesi
Tesi di laurea magistrale
Autore
AYOUSH, ALI
URN
etd-11032025-021022
Titolo
Diagnostic Investigation and Performance Optimization of Tissue Machines
Dipartimento
INGEGNERIA CIVILE E INDUSTRIALE
Corso di studi
TECNOLOGIA E PRODUZIONE DELLA CARTA E DEL CARTONE
Relatori
relatore Prof. Frosolini, Marco
tutor Prof. Gabbrielli, Roberto
Parole chiave
  • Industrial Diagnostics
  • Paper Production
  • Process Optimization
  • Tissue Machine
Data inizio appello
27/11/2025
Consultabilità
Non consultabile
Data di rilascio
27/11/2095
Riassunto
This thesis originates from the candidate’s internship at Toscotec S.p.A., an Italian
company of international standing in the design and manufacture of advanced tissue
production lines. The research addresses two major operational challenges encountered
during the start-up and optimization phases of tissue machines: the influence of headbox
lip geometry and pressure distribution on sheet formation, and the emergence of chatter
marks on the Yankee dryer surface.
A comprehensive methodology combining field inspections, diagnostic analyses, and
simulation tools has been developed within the framework of Root Cause Analysis (RCA)
to identify and mitigate both mechanical and thermal deviations. The results demonstrate
that millimetric inaccuracies in headbox calibration critically affect cross-direction basis-
weight uniformity, while chatter phenomena are predominantly linked to subtle thermal
imbalances, along with disturbances in surface roughness and coating behaviour, and were
not always attributable just to the cases of mechanical misalignment in the creping system.
Corrective actions—such as headbox lip realignment, micrometer recalibration, and Soda
Straw optimization—proved effective in restoring process stability and confirmed the high
sensitivity of modern tissue machines to geometric precision and thermal equilibrium.
Methodologically, the thesis underscores the importance of integrating theoretical
modelling, OEM standards, and observation-based procedures within a coherent and
traceable analytical framework. This integration ensured reliable, reproducible results,
bridging scientific knowledge with industrial application. Furthermore, preventive
maintenance and continuous trend monitoring emerged as strategic practices that enhance
product quality and equipment integrity while generating measurable economic savings.
In a broader perspective, the study highlights that sustainable advancement in the tissue
industry depends on the symmetry between mechanical precision, digital process
governance, and environmental responsibility. The proposed directions for future
development—automation of headbox calibration, digital-twin implementation, and
energy-efficiency optimization—delineate a forward-looking framework for resource
efficient and environmentally sustainable production, where technological innovation
converges with ecological accountability and responsible corporate management.
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