Plastic injection molding is a widespread industrial process in manufacturing. This article investigates the energy consumption in the injection molding process of fruit containers, proposing a new use strategy for the application of artificial intelligence algorithms. The aim is to optimize the process parameters, such as the mold temperatures, the injector temperatures, and the cycle time, to minimize energy consumption. This new use strategy, a hybrid use strategy, combines an unsupervised autoencoder with the K-Means algorithm to analyze production data and identify factors influencing energy consumption. The results show the capability of discovering different operating modes at different levels of energy requirements. An analysis of the process parameters reveals that the number of parts left to complete production, the current cycle counter, the number of shots left to complete the production, the material needed to complete the production, and the total time dedicated to production, so far, are the most relevant features for the optimization of the energy consumption per single piece. The study demonstrates the potential of common artificial intelligence algorithms if appropriately used to improve the sustainability of the plastic injection molding process.

Predictive Maintenance (PdM) in grinding processes is often constrained by the high cost of sensor infrastructures and the limited robustness of data-driven models in noisy industrial environments. This gap limits the effective adoption of advanced diagnostic techniques on the shop floor. This work presents a fault diagnosis framework based on a supervised autoencoder (SAE), designed for accurate and cost-effective industrial deployment using a reduced set of standard sensors. The approach integrates temporal segmentation, feature extraction, and a two-stage classification strategy for both fault detection and fault classification. The paper mages a comparison of SAE technique with other AI methods, making a detailed analysis of accuracy metrics. The main contributions are threefold: (i) the development of an SAE architecture capable of learning a highly discriminative latent representation of machine health; (ii) the demonstration that reliable diagnosis can be achieved using only two standard industrial sensors (force and acoustic emission), significantly reducing instrumentation requirements; and (iii) the introduction of an early fault detection strategy based on pre-contact data. Experimental validation on a bearing grinding machine dataset shows that the proposed method achieves accuracy above 92% with two sensors and up to 99.44% using only pre-contact signals. These results enable a zero-scrap strategy by allowing early process interruption before defective parts are produced.

The increasing generation of agro-industrial by-products from fruit processing represents both an environmental challenge and a valuable opportunity for resource recovery. In this study, tomato, apple, and orange by-products were selected as sustainable substrates for valorization through lactic acid bacteria (LAB) fermentation. The objective was to enhance their functional potential while contributing to waste reduction within a circular bio-economy framework. Selected Lactiplantibacillus plantarum strains were applied to each matrix, and microbial growth dynamics were monitored throughout fermentation. In parallel, experimental data were used to build and validate machine learning-assisted models to predict growth kinetics and identify optimal fermentation conditions. To validate the model, the growth of LAB at 32 °C with a bacterial inoculum of approximately 108 CFU/mL was predicted and subsequently compared with experimental results. The developed models demonstrated reliable predictive capability, with low-to-moderate Root Mean Squared Error (RMSE) across different substrates and growth parameters, ranging from 0.143 (vMaxAvg in apple) to 7.155 (LagAvg in orange). Following model validation, selected matrix–strain combinations were applied to develop food products, in which fermented by-products were incorporated as functional ingredients. The resulting formulations exhibited enhanced antioxidant activity and increased total phenolic content, as determined by DPPH (2,2-diphenyl-1-picrylhydrazyl) assay and phenolic analysis, reaching 56.56 mg GAE/100 g (p < 0.05) and 33.76 mg Trolox eq./100 g (p < 0.05), respectively.