Work

Physically Interpretable Soft Sensor for Deformation Diagnostics in Extrusion-Based Shaping: A Case Study on Ceramic Roof Tiles

Year: 2026

Type: article

Abstract: This study examines the longitudinal shortening of clay blanks during extrusion and introduces a hybrid soft sensor framework for early prediction of ceramic roof tile performance. Targeted properties include shrinkage, water absorption, and saturation. The models integrate real-time process data collected after vacuum extrusion and pressing with clay-specific descriptors such as carbonate content and granulometry, alongside additional variables including moisture, firing temperature, and length reduction. Partial Least Squares (PLS) regression was adopted as the core method due to robustness against multicollinearity and ease of industrial integration. In contrast to complex machine learning pipelines, PLS-based soft sensors enable lightweight edge deployment without reliance on IoT infrastructure. Complementary regression and machine learning models were used to benchmark predictive accuracy and explore nonlinear effects. The results confirm reliable prediction of key performance indicators and reveal mechanistic links between extrusion-induced deformation and downstream behavior. Although developed for clay systems, the framework is generalizable and can be adapted to other traditional ceramic processes or industries seeking interpretable, locally deployable solutions for process control.

Source: Processes

DOI: 10.3390/pr14020279

Authors: Milica Vidak Vasić, Zoran Bačkalić, Pedro Muñoz

Institutions: Institut za ispitivanje materijala IMS a.d. Beograd, Metropolitan University, Universidad Autónoma de Chile, Universidad Internacional de La Rioja

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896