Search Results (129)

The significant increase in the importance of silumin recycling in the context of sustainable development is driven by tangible ecological and economic benefits. However, the primary technological challenge associated with using scrap is the accumulation of iron, which promotes the formation of undesirable sludge particles, degrading the alloy’s mechanical properties. This paper presents a description of the phase transformations in an AlSi7Mg alloy with increased iron and manganese content. Analysis of data from Differential Scanning Calorimetry (DSC) revealed the primary crystallisation of sludge particles (SP) and the pre-eutectic precipitation of the α-Al₁₅(Fe,Mn)₃Si₂ phase, which replaced the β-Al₅FeSi phase. The remaining constituents of the AlSi7Mg alloy structure—α(Al) solid solution dendrites, the α(Al)+β(Si) eutectic, and the Mg₂Si phase—crystallise regardless of the iron, manganese, and chromium content. It was established that the increase in the crystallisation temperature of SP, rich mainly in the elements mentioned above, is directly proportional to the increase in the value of the sludge factor (SF) and ranges from 620 °C (for SF~1.3%) to approx. 645 °C (for SF~3.1%). SEM studies revealed that the combined increase in iron and manganese content not only raises the precipitation temperature of SP but also alters its morphology from single polyhedra to compact, “cluster-like” structures. To avoid the presence of sludge particles in the AlSi7Mg alloy, which have an unfavourable morphology and reduce the yield of the melting process, the SF for high-pressure die-casting should not exceed 2.0%. Full article

Industrial machine tools are both performance assets and environmental hotspots over their long service lives. Maintenance is traditionally optimized to safeguard availability, quality and cost. However, maintenance choices also determine the energy consumption, footprints, component duration and end-of-life pathways. In this study, we present a decision framework to compare performance-only maintenance (POM) with sustainability-aware maintenance (SAM) for machine tools. The framework integrates degradation and Remaining Useful Life (RUL) estimation, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Outcomes are summarized with a Sustainable Maintenance Balance (SMB) index. We test the proposed approach on a horizontal machining center for aluminum, validated by running a Monte Carlo simulation over a 1000 h functional unit. Across empirical data and simulation, SAM—compared to POM—demonstrated an ability to improve availability, reduces downtime and scrap, and lower total LCC while cutting carbon emissions. The proposed method is proposed as readily deployable in real plants, supporting robust sustainable-production decisions. Full article

The demand for submersible pumps for lifting the liquids from under the surface is growing day by day. The growing market of submersible pumps creates difficulties for managing their end of life. Recycling, the process of converting scraps material into new virgin material and fine products while the expenditures are used for carrying the scrap material from different sources to destinations, is the main focus area of this research. The aim of this study is to investigate and create strategies for minimizing the transportation cost in the reverse logistics of electrical appliances (submersible pumps) in order to increase profitability and economically sustainability. Data was gathered through interviews with two distinct individuals that are extremely knowledgeable and proficient in their fields. A case study of reverse logistics of submersible pumps is used to optimize the transit cost in a supply chain. To find an appropriate solution, the simplex linear programming approach was used. Microsoft Excel Solver was utilized to conduct data analysis. The findings revealed that optimizing the transportation cost not only reduces the operational cost, but also increases the profit margins. The study concludes that by integrating reverse logistics strategically, both environmental and financial benefits can be achieved by industries. Full article

Accurate scrap forecasting is essential for advancing green manufacturing, as reducing defective output not only lowers production costs but also prevents unnecessary resource consumption and environmental impact. Effective scrap prediction enables manufacturers to take proactive measures to minimize waste generation, thereby supporting sustainability goals and improving production efficiency. This study proposes a hybrid ensemble framework that integrates CatBoost and XGBoost, combined with Particle Swarm Optimization (PSO), to enhance prediction accuracy in industrial applications. The model exploits the complementary strengths of both algorithms by applying weighted averaging and stacked generalization, allowing it to process heterogeneous datasets containing both categorical and numerical variables. A case study in the aerospace manufacturing sector demonstrates the effectiveness of the proposed approach. Compared to standalone models, the PSO-enhanced hybrid ensemble achieved more than a 30% reduction in Root Mean Squared Error (RMSE), confirming its ability to capture complex interactions among diverse process parameters. Feature importance analysis further showed that categorical attributes, such as machine type and operator, are as influential as numerical parameters, underscoring the need for hybrid modeling. Although the model requires higher computational effort, the integration of PSO significantly improves robustness and scalability. By reducing scrap and optimizing resource utilization, the proposed framework provides a data-driven pathway toward greener, more resource-efficient, and resilient manufacturing systems. Full article

In the production of stainless steel, chromium losses, particularly in the electric arc furnace (EAF) phase, pose a challenge. This study addresses these issues by reviewing and analyzing the thermodynamics of the Fe-Cr-C-O-(Si) system, highlighting discrepancies in existing literature regarding Gibbs free energies, interaction parameters, and other thermodynamic data. We developed a simple to use thermodynamic model to simulate the oxidation process using established data from scientific literature. The model calculates the equilibrium solubilities of chromium and carbon, showing how process variables like temperature, partial pressure of carbon monoxide, and silicon concentration influence chromium oxidation. The findings confirm that higher temperatures and the presence of silicon significantly reduce chromium loss by favoring carbon oxidation over chromium. The research concludes by providing practical guidelines for minimizing chromium losses in EAFs, such as protecting scrap with carbon, silicon, and aluminum; controlling oxygen intake; and ensuring a high melt temperature during decarburization. These guidelines aim to improve the economic efficiency and sustainability of stainless steel production. The paper is an expanded version of a prior conference paper. Full article

Copper is a critically important metal for economic security, and its supply chain is influenced by various factors, particularly market prices. This paper aims to uncover the impact of high uncertainty in copper prices on the copper supply chain (CSC) configuration and propose strategies for CSC construction. To achieve this goal, this study presents a closed-loop supply chain (CLSC) network, simulates copper market volatility using the geometric Brownian motion (GBM) model, and establishes a two-stage stochastic programming (TSSP) model. An empirical study was conducted using geographical and economic data of the CSC in the Chinese province of Hunan. The research results indicate that there is a threshold in copper prices that can lead to the construction of a reverse supply chain (RSC). However, significant fluctuations in copper prices introduce uncertainty into the supply chain network configuration. Therefore, policy measures to encourage copper scrap recycling should be implemented to maintain the safety of the CLSC during market instability. The proposed modelling framework for addressing fluctuation factors in supply chain design has been validated and can be promoted to other similar industries affected by markets. Full article

Tool wear detection is very important in CNC machine tool cutting. Once the tool is excessively worn, it is not only easy to cause the workpiece to be scrapped, but even to damage the machine. Therefore, common external turning tools of CNC machines are studied. The effect of tool nose wear on machining accuracy was analyzed by a building mathematical model. According to different wear conditions, a linear detection method based on edge images and input features was proposed to detect the main and secondary cutting edges, which helped determine the theoretical center of the tool nose and build a morphological visual model. For different error cases, the axial and radial error compensation strategies were proposed, respectively. By comparing the experimental data of four kinds of workpieces before and after compensation machining, the average errors of them were reduced separately, and the maximum value reached 79.2%, which verified the effectiveness of the compensation strategy. The intelligent compensation strategies will significantly improve the micro-machining accuracy and efficiency of the external turning tools in CNC machines. Full article

Driven by environmental concerns and aligned with the principles of the circular economy, urban plastic waste—including packaging materials, disposable items, non-functional objects, and industrial scrap—is increasingly being collected, recycled, and marketed as a potential substitute for virgin polymers. However, the use of recycled polymers introduces uncertainties that can significantly affect both the durability and the further recyclability of the resulting products. This paper demonstrates how spectroscopic analysis in the mid-infrared (MIR) and near-infrared (NIR) regions can be applied well beyond the basic identification of the main polymeric component, typically performed during the sorting stage of recycling processes. A detailed interpretation of spectral data, based on well-established correlations between spectroscopic response and material structure, enables the classification of recycled polymers according to specific physicochemical properties, such as chemical composition, molecular architecture, and morphology. In this context, infrared spectroscopy not only provides a reliable comparison with the corresponding virgin polymer references but also proves particularly effective in assessing the homogeneity of recycled materials and the reproducibility of their properties—factors not inherently guaranteed due to the variability of input sources. As a case study, we present a robust protocol for determining the polypropylene content in recycled polyethylene samples. Full article

This research investigates the influence of waste mask fabric scraps (WMFSs) and nano-carbon-modified filler (NCMF) on the mechanical characteristics and durability of hot mix asphalt, aiming to improve pavement performance concerning tensile stress, fatigue, and moisture damage using recycled materials. Asphalt mixtures were created with aggregate and WMFS/NCMF at 0.3% and 0.5% weight percentages (relative to aggregate), with fiber lengths of 8, 12, and 18 mm, utilizing a ‘wet mixing’ method where fibers were incrementally added to aggregates during mixing. The samples underwent indirect tensile strength, moisture susceptibility, and Marshall stability testing. The results demonstrated that incorporating WMFSs and NCMF initially enhanced tensile strength, moisture susceptibility resistance, and Marshall stability, reaching an optimal point; beyond this, further fiber addition diminished these properties. Data analysis identified the sample containing 0.3% fibers at a 12 mm length as the superior performer, showcasing the highest ITS and Marshall stability values. Statistical t-tests revealed significant differences between fiber-containing samples and control groups, verifying the beneficial impact of WMFSs and NCMF. Design-Expert software (Design-Expert 12.0.3) was used to develop functional models predicting asphalt properties based on fiber percentage and length. The optimal combination—12 mm fiber length and 0.3% WMFS/NCMF—demonstrated a 33% increase in tensile strength, a 17% improvement in moisture resistance, and a 70% reduction in fatigue deformation. Safety protocols, including thermal decontamination of WMFSs, were implemented to mitigate potential health risks. Full article

Bridge cranes generally have a significant disparity between their actual service life and design life. If they are scrapped according to the design life, it is likely to result in resource wastage or pose potential safety hazards due to extended service. Existing studies have not thoroughly examined the coupling relationship among actual working conditions, structural damage, and load-matching strategies. It is difficult to achieve real-time and accurate adaptation between loads and the carrying capacity of equipment, and thus cannot effectively narrow this life gap. To this end, this paper defines a digital twin system framework for crane load adaptive matching, constructs a load adaptive matching optimization model, proposes a method for developing a digital twin system for bridge crane load adaptive matching, and builds a digital twin system platform centered on virtual-real mapping, IoT connectivity, and data interaction. Detailed experimental verification was conducted using the DQ40 kg-1.8 m-1.3 m light-duty bridge crane. The results demonstrate that this method and system can effectively achieve dynamic matching between the load and real-time carrying capacity. While ensuring the service life exceeds the design life, the difference between the two is controlled at around 3467 cycles, accounting for approximately 0.000462% of the design life. This significantly improves the equipment’s operational safety and resource utilization efficiency, breaks through the limitations of load reduction schemes formulated based on human experience under the traditional regular inspection mode, and provides a scientific load-matching decision-making basis and technical support for special equipment inspection institutions and users. Full article

In this study, we adopt a geometallurgical analytical approach common in mineral processing in the characterization of samples of shredded waste printed circuit board (PCB) E-waste, originating from Europe. Conventionally, bulk chemical analysis provides a value for E-waste; however, chemical analysis alone does not provide information on the textural variability, phase complexity, grain size, particle morphology, phase liberation and associations. To address this, we have integrated analysis using binocular microscopy, manual scanning electron microscopy, phase, textural and compositional analyses by automated (SEM-EDS), phase analysis based on (Automated Material Identification and Classification System (AMICS) software, and elemental analysis using micro-XRF. All methods used have strengths and limitations, but an integration of these analytical tools allows the detailed characterization of the texture and composition of the E-waste feeds, ahead of waste reprocessing. These data can then be used to aid the design of optimized processing circuits for the recovery of the key payable components, and assist in the commercial trading of e-scrap. Full article

This study investigates the effects of repeated mechanical recycling on the structural, thermal, mechanical, and aesthetic properties of poly(butylene succinate) (PBS), a commercially available bio-based and biodegradable aliphatic polyester. PBS production scraps were subjected to five consecutive recycling cycles through semi-industrial extrusion compounding followed by injection molding to simulate realistic mechanical reprocessing conditions. Melt mass-flow rate (MFR) analysis revealed a progressive increase in melt fluidity. Initially, the trend of viscosity followed the melt flow rate; however, increasing the reprocessing number (up to 5) resulted in a partial recovery of viscosity, which was caused by chain branching mechanisms. The phenomenon was also confirmed by data of molecular weight evaluation. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) confirmed the thermal stability of the polymer, with minimal shifts in glass transition, crystallization, and degradation temperatures during the reprocessing cycles. Tensile tests revealed a slight reduction in strength and stiffness, but an increase in elongation at break, indicating improved ductility. Impact resistance declined moderately from 8.7 to 7.3 kJ/m² upon reprocessing; however, it exhibited a pronounced reduction to 1.8 kJ/m² at −50 °C, reflecting brittle behavior under sub-ambient conditions. Despite these variations, PBS maintained excellent color stability (ΔE < 1), ensuring aesthetic consistency while retaining good mechanical and thermal properties. Full article

The electric arc furnace (EAF) process includes key stages: charging scrap metal, melting using electric arcs, refining through oxygen injection and slag formation, and tapping molten steel. Recently, EAF steelmaking has become increasingly important due to its flexibility with recycled materials, lower environmental impact, and reduced investment costs. This study focuses specifically on select aspects of the refining stage, analysing decarburization and the associated exothermic oxidation reactions following the removal of carbon with oxygen injection. Particular attention is given to FeO generation during refining, as it strongly affects slag chemistry, yield losses, and overall efficiency. Using a Computational Fluid Dynamics (CFD)-based refining simulator validated with industrial data from EVRAZ North America (showing an 8.57% deviation), this study investigated the impact of oxygen injection rate and burner configuration. The results in a three-burner EAF operation showed that increasing oxygen injection by 10% improved carbon removal by 5%, but with an associated increase of FeO generation of 22%. Conversely, reducing oxygen injection by 15% raised the residual carbon content by 43% but lowered FeO by 23%. Moreover, the impact of the number of burners was analysed by simulating a second scenario with 6 burners. The results show that by increasing the number of burners from three to six, the target carbon is reached 33% faster while increasing FeO by 42.5%. Moreover, by reducing the oxygen injection in the six-burner case, it is possible to reduce FeO generation from 42.5 to 28.5% without significantly impacting carbon removal. This set of results provides guidance for burner optimization and understanding the impact of oxygen injection on refining efficiency. Full article

Optimizing manufacturing processes to reduce scrap and enhance process stability presents significant challenges, particularly when multiple conflicting objectives must be addressed concurrently. As the number of objectives increases, the complexity of the optimization task escalates. This difficulty is further intensified in online optimization scenarios, where optimal parameter settings must be delivered in real time within active production environments. In this work, we propose a reinforcement learning-based framework for the multi-objective optimization of manufacturing parameters, demonstrated through a case study on pinion gear manufacturing. The framework utilizes the Multi-Objective Maximum a Posteriori Optimization (MO-MPO) algorithm to train a reinforcement learning agent. A high-fidelity simulation of the pinion manufacturing process is constructed in Simufact, serving both data generation and validation purposes. The agent’s performance is assessed using a hold-out test set along with additional simulations of the physical process. To ensure the generalizability of the approach, further validation is performed using open-source manufacturing datasets and synthetically generated data. The results demonstrate the feasibility of the proposed method for real-time industrial deployment. Moreover, Pareto-optimality is verified via half-space analysis, emphasizing the framework’s effectiveness in managing trade-offs among competing objectives. Full article

Additive manufacturing (AM) presents significant opportunities for advancing sustainability through optimized process control and material utilization. This research investigates the application of machine learning (ML) models to directly associate AM process parameters with sustainability metrics, which is often a challenge by experimental methods alone. Initially, experimental data are generated by systematically varying key AM parameters, layer height, infill density, infill pattern, build orientation, and number of shells. Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. Among the models, Random Forest yields the highest predictive accuracy and lowest mean squared error across all target sustainability indicators: energy consumption, part weight, scrap weight, and production time. The results confirm the efficacy of ML in predicting sustainability outcomes when supported by robust experimental data. This research offers a scalable and computationally efficient approach to enhancing sustainability in AM processes and contributes to data-driven decision-making in sustainable manufacturing. Full article