PMMasse

PMMasse is a framework in industrial process control designed to provide real-time mass estimation for products and batches on manufacturing lines. The acronym stands for Predictive Mass Monitoring and Estimation. The aim is to combine sensor data, physical models, and statistical inference to improve measurement accuracy, traceability, and process efficiency.

The concept originated from collaborations among researchers and industry partners in the late 2010s, with pilots conducted in packaging and automotive assembly facilities. A reference implementation appeared in open literature in 2021–2022, outlining modular components and standard interfaces.

Architecture and components include a data acquisition layer that collects measurements from load cells, flow meters, and volume sensors; calibration and pre-processing modules that correct drift and temperature effects; a core estimation engine that fuses data with physics-based constraints and machine-learned priors to produce mass estimates for items or batches; a reporting layer that provides dashboards and batch records; and an interoperability layer that supports integration with manufacturing execution systems.

Algorithms and methods commonly used with PMMasse include Kalman-based state estimation, Bayesian regression, and physics-informed neural networks. Uncertainty quantification is central, with confidence intervals reported alongside estimates. The design emphasizes traceability and auditability of mass estimates.

Applications and impact include monitoring product weight in real time, verifying packaging accuracy, supporting material usage optimization, and reducing the need for direct weighing at multiple stages. It is particularly relevant where direct weighing is impractical or slow.

Limitations and future work involve dependence on sensor coverage and calibration quality, potential sensitivity to environmental conditions, and ongoing efforts to improve calibration robustness, standard data models, and cross-domain applicability.