Conveyor equipment is commonly used in packaging systems, and operators must stand for long periods and perform a similar, monotonous task. This might increase the operator’s physical and mental fatigue. A hypothesis is, that the speed, noise, and direction of material flow influence the operator’s workload. A work system optimization could be proposed using response surface methodology. The Central Composite Design was proposed as an experimental design. Both physical and mental workload are assessed as the response. The results showed that the optimal combination of physical workload response parameters was a speed of 25.8579 cm/s, a noise of 89.3569 dB, and a flow direction from right to left. Meanwhile, the optimal parameter combination for mental workload response was a speed of 25.8579 cm/s and a noise level of 77.9289 dB, with the most optimal flow direction from right to left.
The paper compares the results of protective measures used to maintain business continuity of Polish production enterprises during a crisis situation. It examines how these results differ depending on the type and form of production organization. A mix-model CATI/CAWI survey was performed on a representative group of 600 enterprises. Over 40 individual protection measures were identified and assigned to one of five predefined groups. The research specified three production types (unit/job, batch, mass) and two production forms (line production, job-shop production). A chi-square test was used for the statistical analysis. The results indicate that production volume, defined as the number of manufactured, repetitive products, affects the selection of protection measures. The production form, reflecting the way a product flows through the production process, is also significant. The findings provide a basis for future preparations aimed at ensuring business continuity in the event of similar disruptions to production processes.
Quality assessment of manufactured products is vital to ensure performance, safety, and customer satisfaction across industries. Defects in items such as bottle caps, cables, capsules, leather, and metal components can affect functionality and durability. Traditional inspection methods relying on manual visual checks are time-consuming and error-prone. This study proposes an AI-driven framework using the Probabilistic U-Net integrated with a Conditional Variational Autoenco der (CVAE) for automated defect detection. The model introduces stochastic latent variables to generate multiple plausible segmentation maps, enhancing accuracy under ambiguous or noisy conditions. Using the MVTec Anomaly Detection dataset, which includes defects such as scratches and discoloration, the system applies preprocessing steps including resizing, normalization, and data augmentation to enhance the robustness and consistency of the input data. A hybrid loss combining cross-entropy and Kullback-Leibler divergence improves segmentation precision and latent space alignment. Experimental results confirm robust and reliable defect detection across diverse product categories, demonstrating the model's potential for automated manufacturing quality assurance.
The article addresses the challenges associated with the Product-as-a-Service (PaaS) concept in the context of the circular economy. The use of simulation technologies within this concept can accelerate the adoption of circular economy principles in companies. It enables faster modelling and optimisation of value-retention processes such as remanufacturing and refurbishment. The aim of the paper is to propose a simulation approach that allows engineers to develop simulation models (digital twins) in the same way they design and redesign closed-loop manufacturing systems. A further objective is to highlight the specific requirements for testing such simulation models and for sizing planned digital-twin projects. The article presents the development of a hybrid model that combines discrete piece-flow for remanufacturing with mass flow (in kilograms) for recycling, using household appliances as an example and identifying sources of uncertainty that affect the organisation of PaaS processes, particularly remanufacturing and recycling. The main contribution is the development of a hybrid simulation approach based on operations in the PaaS domain, i.e. closed-loop manufacturing with the 6R framework, and the application of the COSMIC methodology to size simulation projects. The article also discusses methods for simulation modelling of individual sources of uncertainty and uses an operation-based simulation method instead of conventional discrete-event simulation.
Three-dimensional (3D) printing is an attractive method for creating gel geometries for specific applications. In this research, the 3D printing parameters for direct and indirect extrusion of alginate-based hydrogels are optimized using the Taguchi and Analysis of Variance methods for output responses, including printed shape retention and the height of the 3-printed layers. The results show that for an indirect extrusion, the optimal values of extrusion speed, printing speed, and nozzle distance for the shape retention ability are 13 steps/mm, 50 mm/s, and 0.5 mm, respectively, while those values for the printed height are 13 steps/mm, 60 mm/s, and 0.5 mm, respectively. For direct extrusion, the optimal parameter set for former response is 35 steps/mm, 60 mm/s, and 0.6 mm, respectively, while the set for the latter is 35 steps/mm, 40 mm/s, and 0.6 mm, respectively. The findings in this report can be used as data for related research.
