To address the low-frequency noise challenges in lithium-ion battery systems, such as thin-walled structures, auxiliary units, and power electronics, this study develops a novel embedded-tube Helmholtz resonator (ET-HR) with sinusoidal walls instead of the traditional straight-wave wall. Finite element modelling is performed on the sound absorption properties of the proposed ET-HR. The effect of structural configurations, including diameters, tube angles, and wave numbers, on sound absorption properties is revealed. The sound absorption peak shifts to a lower frequency with the decrease in the equivalent diameter and the increase in the length of the embedded tube. As the depth of the cavity increases, the sound absorption performance in the low-frequency range is improved. Finally, the ET-HR with multitube is designed, with experiments showing that it can achieve a sound absorption coefficient greater than 0.7 from 307 Hz to 733 Hz and 0.9 from 445 Hz to 681 Hz. This study provides a basis for the design of structures with efficient and adjustable sound absorption performance under finite thickness.
Incremental Forming with a single-point tool is an efficient sheet-metal forming operation for producing various net shapes. The quality of the surface manufactured by the process is crucial for final applications and depends on the input values selected for the forming operation. In the present study, Incremental Sheet Forming assisted by a Static Pressure Support was used to form AISI 316L stainless steel sheets of uniform thickness. A Taguchi design of experiments was used, and a mixed L18 orthogonal array was constructed to carry out the experiments. The experiments were performed by changing the forming parameters such as tool nose diameter, forming step depth, bottom wall angle, rate of feed and tool rotational speed with various levels and their effect on the responses was studied. The results confirm that the forming step depth and the feed rate have the greatest influence on surface roughness and forming time, respectively. The developed optimisation method will be very helpful in the estimation of surface roughness and forming time of AISI 316L stainless steel formed during incremental sheet forming using static pressure support with various forming parameter conditions. (Received in May 2026, accepted in July 2026. This paper was with the authors 2 weeks for 1 revision.)
Industry 5.0 prioritizes sustainable, human-centred production, but practical frameworks integrating digital twins, artificial intelligence, and virtual reality (VR) remain scarce. This study develops an operational toolbox acting as an advanced digital shadow, featuring a real-time pipeline via MQTT, Telegraf, InfluxDB, FlexSim, an InceptionTime model for energy forecasting, and VR. Lacking automated physical interventions, it relies on a decision-mediated feedback loop: simulated sensor data is cloud-streamed, AI-analysed, and 3D-visualized, empowering human decision-makers to manually implement changes. The proof-of-concept achieved similar to 2 seconds end-to-end latency, validated short-horizon energy forecasts (MAE/RMSE reported), and demonstrated VR's feasibility for spatial bottleneck comprehension. This integrated architecture provides a replicable blueprint for near-real-time immersive systems, supporting industrial awareness and sustainability-oriented decision-making (SDG 12).
The growing complexity of production systems, combined with the licencing costs of commercial simulation solutions and the need for greater interoperability, has driven increasing interest in open-source discrete event simulation (DES) solutions. However, their stochastic capabilities remain largely unvalidated. This study evaluates the ability of open-source DES solutions to reproduce predefined statistical distributions. A conceptual model was implemented across five open-source solutions and one commercial solution benchmark, with a specific statistical distribution assigned to each process building block. Simulation outputs were analysed using SciPy, Fitdistrplus, and Arena's Input Analyzer. Results reveal significant variation in adherence between predefined and proposed distributions. SimPy demonstrated the greatest consistency, highlighting the importance of statistical validation in simulation modelling and suggesting that open-source solutions represent a viable alternative to commercial simulation solutions. (Received in March 2026, accepted in June 2026. This paper was with the authors 2 weeks for 3 revisions.)
Family based dispatching (FBD) rules are a prominent approach for implementing virtual cellular manufacturing, designed to enhance system efficiency by lowering setup times. However, FBD rules can trigger bulk arrivals and cause downstream delays-consequences that remain largely unexplored in existing literature. Additionally, contemporary FBD research has predominantly focused on minimizing average flow time. To address these gaps, this study investigates a novel shop floor control challenge that integrates FBD rules and pre-emption alongside newly identified key factors. Utilizing a two-stage flow shop model to isolate the direct downstream impacts of FBD rules under a due date-related objective, this paper aims to identify ideal operational conditions for different pre-emption types. Ultimately, this research evaluates how different pre-emption types perform with the key factors, offering valuable insights into pre-emption, FBD rules, and the novel key factors.
