Processing Route Research Articles

The Effect ECAP Processing Routes on the Microstructure and Mechanical Properties of AISI 316 Austenitic Stainless Steel

The Effect ECAP Processing Routes on the Microstructure and Mechanical Properties of AISI 316 Austenitic Stainless Steel

Precipitation behavior of Al-Zn-Mg-Cu alloys with the coupled soft/hard micro-scale areas and its effect on properties

A novel thermomechanical processing route was developed to prepare Al-Zn-Mg-Cu alloys containing heterostructures with coupled soft/hard micro-scale areas. The heterostructure gives an effect on the subsequent precipitation behavior of alloys. Importantly, the tensile strengths, elongation and corrosion resistance of grain boundaries of the #3 alloy with an appropriate coupled distribution of soft/hard micro-scale areas are all greatly improved compared with the alloy with the homogeneous structure. Based on the detailed microstructure characterization, the corresponding strengthening and corrosion resistance mechanisms of alloys with heterostructure have been established in this paper.

Understanding the Role of Organic Hole Transport Layers on Pinhole Blocking and Performance Improvement in Sb2Se3 Solar Cells

AbstractSb2Se3 is an emerging semiconductor which has shown promise for low‐cost photovoltaic applications. After successive record‐efficiencies using a range of device structures, spiro‐OMeTAD has emerged as the default hole transport material (HTM), however, the function of HTM layers remains poorly understood. Here, thin‐film Sb2Se3 solar cells are fabricated with which three organic HTM layers ‐ namely P3HT, PCDTBT, and spiro‐OMeTAD are investigated. By comparing these against one another, and to a reference device, their role in the device stack are clarified. These organic HTM layers are found to serve a dual purpose, increasing both the average and peak efficiency by simultaneously blocking pinholes and improving the band alignment at the back contact, with marginal differences in performance between the different HTMs. This produced a champion device of 7.44% using P3HT, resulting from an improvement in all performance parameters. A more complex processing route, run‐to‐run variability, and lower overall device performance compared to the other organics challenge the assumption that spiro‐OMeTAD is the optimal HTM for Sb2Se3 devices. A Schottky barrier at the Au‐Sb2Se3 contact despite the deep work function of gold implies Fermi level pinning due to a defective interface, which each of the organic HTMs are equally capable of alleviating.

Exploring the Performance Impact of Neural Network Optimization on Energy Analysis of Biosensor

With the popularization of new energy vehicles, lithium battery systems, as the main components of new energy vehicles, have the characteristics of short life cycles and harmful substances inside. The green treatment of lithium battery systems has become a research hotspot. Disassembly and recycling are essential means of reusing waste in lithium battery systems. Due to the wide variety of lithium battery systems, the lack of unified design standards, and the high flexibility requirements for disassembly, manual disassembly is currently the primary method used. However, this method can cause health hazards to oneself when dismantling some harmful components. The optimization of the dismantling process route for lithium batteries is a crucial step before dismantling, which directly determines the economic benefits of dismantling. However, unlike general electromechanical products, lithium batteries have prominent safety issues during the dismantling process, so the safety requirements for their dismantling process route are relatively high. Given the substantial absence of parametric evaluation and modification in prior research, this work investigates the influence of the most significant factors on the power density of biosensors. A conduction-based framework was employed to ascertain these variables, and the calculations were performed utilizing a neural network. The neural network was developed with Particle Swarm Optimization (PSO). Based on this, this article considers studying the optimization method of the lithium battery safety disassembly process to maximize safety and economic benefits comprehensively. Based on the essential characteristics of lithium-ion battery systems, an analysis is conducted on the allocation method of difficulty level for human-machine cooperation tasks and the impact indicators of task allocation. Then, a product disassembly hybrid diagram is established, and on this basis, multiple sets of human-machine cooperation disassembly sequences are generated. Finally, a multi-objective optimization model for disassembly cost, difficulty, and time is established. Finally, taking the Tesla Model 1sPBS waste lithium battery as an example, the safety prediction model for dismantling the waste lithium battery and the optimization model for the safety dismantling process route were solved to verify the effectiveness of the above optimization method.

Structural design and test of superconducting magnet coil for the cooling storage ring external-target experiment

Abstract Heavy ion collision is an unique method for studying cold and dense nuclear matter in labs. The Cooling Storage Ring (CSR) External-target Experiment (CEE) at the Heavy Ion Research Facility in Lanzhou (HIRFL), China, is the first multi-purpose nuclear physics experimental device to operate in the GeV energy range. One of the key parts is a large acceptance dipole magnet, which is used to bend particles so that they can be detected by various detectors. A superconducting coil with the size of 3.1 m ×3.6 m × 2 m was designed to verify the rationality of the scheme. Although the coil-dominated superconducting magnet with NbTi has been used to reduce the weight and size of the magnet, large deformation and stress will occur in the cooldown and excitation stages owing to the large volume and weight. Therefore, it is crucial to design a reasonable structure to ensure the strength of the magnet and prevent the magnet from interfering with other components due to large deformation, and even the possibility of quenching. A truss supporter is proposed for the support of the superconducting coil. In this study, the structural design of the cold mass of a superconducting magnet is introduced, and its mechanical behaviors during cooldown and excitation are analyzed in detail. To verify the feasibility of the coil design and process route, a sub-size prototype was manufactured and tested at 4.2 K and reached the design current successfully.

Development and prospect of the acetylene production chain based on the process systems engineering: a focus on the polyvinyl chloride production

Abstract With the shortage of international oil resources and rising oil prices, the acetylene chemical industry has been revitalized. As an important basic raw material in the petrochemical industry, it can produce many chemical products and extend the acetylene production chain. Firstly, the production, conversion, and utilization of acetylene are outlined. Subsequently, this paper focuses on reviewing the core production process routes and the corresponding research progress in the acetylene production chain and points out the development trends of the domestic acetylene downstream conversion. Then, considering that acetylene is produced by calcium carbide technology in Ningdong and the extended downstream polyvinyl chloride industry chain is a large energy consumer and the focus of environmental safety monitoring, the entire process from coal-based calcium carbide acetylene production to polyvinyl chloride synthesis is elaborated from the perspective of process systems engineering. The resource utilization status and bottlenecks in the process are declared, and the direction of process improvement and optimization is proposed. Finally, the entire acetylene production chain is briefly analyzed and reviewed.

Tuning crosslinking of hybrid preceramic polymers in vat photopolymerization toward controlled ceramic yields

Control of preceramic polymer crosslinking for UV-curable processing is essential for fine 3D printing with high ceramic conversion for sustainable polymer-derived ceramics (PDC) engineering. While various factors influencing ceramic yield have been studied, the systematic exploration of the relationship between crosslinking and ceramic yield, especially when crosslinking increases volatile elements, remains open for further investigation. This study addresses this gap by utilizing vat photopolymerization (VP) additive manufacturing (AM) as a versatile platform for controlling preceramic crosslinking and ceramic yield. By rationally designing and tuning the photochemical crosslinking through digital light processing (DLP), we demonstrate that the ceramic yield can be enhanced from 64% to over 86%, even with added volatile elements. We reveal that the post-pyrolysis ceramic yield can be closely correlated with the pre-pyrolysis crosslinking of the preceramic network represented by its stiffness. This correlation thereby suggests a fast, energy-efficient, non-destructive methodology to predict and improve ceramic yield. Combined with these findings, our one-pot thiol-ene hybridization of polycarbosilane and polycarbosiloxane via DLP offers an exemplary method to generate hybrid preceramic polymers with tailored material properties toward target applications, and potentially even higher ceramic yields. This study thus contributes to achieving better resource- and energy-efficient preceramic polymer and PDC processing routes toward sustainable, advanced organic–inorganic materials manufacturing.

Unveiling the effect of strain path-dependent microstructural evolution on the electrochemical behavior of cold-rolled [FeNi]69Cr15Mn10Nb6 high-entropy alloy

In the present work, [FeNi]69Cr15Mn10Nb6 high-entropy alloys (HEAs) were subjected to a heavy cold-rolling process at ambient temperature through two distinct routes, namely unidirectional cold-rolling (UCR) and multistep cross cold-rolling (MSCCR). This was done to investigate the effect of strain path-dependent microstructural evolution on the electrochemical behavior of the alloys in a 0.5 M H2SO4 solution. The as-homogenized specimen displayed a two-phase microstructure with [FeNiNb]-rich dendrites distributed in a nearly homogenous face-centered cubic (FCC) high-entropy alloy matrix. It was found that the [FeNiNb]-rich dendrites elongated, broke down, and elongated along the rolling direction in both processing routes. In the MSCCR-processed specimen, the dendrites exhibited a smaller average size with a more uniform distribution compared to the UCR sample, which was mainly ascribed to the elongation mechanisms in the normal and transverse directions during the MSCCR route. Electrochemical studies demonstrated a decrease in corrosion current density due to high imposed strains in the UCR. Meanwhile, a more positive trend was observed in MSCCR processing. Consequently, achieving uniformly distributed small dendrites and uniform grain refinement through the MSCCR route provided ideal conditions for forming passive layer with superior protection properties.

MCMCF-Router: Multi-capacity Ordered Escape Routing Algorithms for Grid/Staggered Pin Array

Ordered escape routing (OER), which means that the pins need to be routed to the boundary of a pin array in a given order, is an important research topic in PCB design. Although OER has been widely investigated, most works assume that the routing capacity between two adjacent pins is just 1 and the structure of the pin array is a grid pin array. In this article, we focus on multi-capacity ordered escape routing (MC-OER) both in grid pin arrays and staggered pin arrays, which means that multiple wires are allowed to pass through between two adjacent pins. We first propose a multi-capacity multi-commodity flow (MC-MCF) model for the MC-OER problem. To accelerate the routing process, MCMCF-Router is proposed. In MCMCF-Router, a wiring resources driven partition strategy is proposed to reduce the problem size, followed by the approach based on routing conflicts. These approaches largely accelerate the MC-MCF model based method and increase the routability with minimal sacrifice on wire length. Experiments on various cases (with up to 525 pins) show that the proposed method achieves 100% routability within reasonable time (<810 seconds). Compared to the state-of-the-art works for single-capacity OER problems, MCMCF-Router performs similarly well or better.

Environmental impact assessment of alternative technologies for production of biofuels from spent coffee grounds

AbstractIn the strategy to combat climate change that has been caused by the world's overdependence on fossil fuels, current research is focusing on the decarbonisation of the energy sector through the production of renewable cleaner energy, such as biofuels. Spent coffee grounds (SCGs), the waste stream of the coffee brewing industry, are a potential feedstock for the production of valuable products, including biofuels. However, the environmental implications for the valorisation of this valuable waste need to be investigated. This study assesses the environmental impacts of six biomass‐to‐fuel processing technologies using SCGs as a feedstock, with the aim of identifying the most environmentally friendly technology. A cradle‐to‐gate life‐cycle assessment (LCA) was conducted on fast pyrolysis, fermentation, anaerobic digestion (AD), hydrothermal liquefaction (HTL), gasification, and biodiesel production. The mass and energy balances obtained from Aspen Plus simulations served as the life‐cycle inventory data. Using the ReCiPe 2016 midpoint (H) and Eco‐Indicator 99 as the assessment methods, potential environmental impacts were calculated in OpenLCA software. Electricity generation and carbon dioxide emissions were the biggest contributors of environmental impacts. For each category, the maximum result was set to 100% and the results of the other variants were displayed in relation to this result. AD, with the smallest total weighted score (160), was the most environmentally friendly biomass‐to‐fuel processing route, while HTL, with the biggest total weighted score (893), was the worst. A sensitivity analysis indicated that the environmental performance of biofuel production from SCGs was highly influenced by energy input flows and the source of energy generation.

An extended mesoscaled M-K model for predicting discrete ultimate strains and evaluating the effect of normal stress on forming limits

Due to the size effect, the formability of metal sheets diminishes and the ultimate strain points exhibit a more discrete distribution. This poses a huge challenge to the process design and manufacture of microparts. This paper presents a modified mesoscopic scale forming limit model based on the M-K model. The model extends the forming limit curve (FLC) into forming limit bands to predict the discrete nature of the ultimate strain. In addition, a method for applying normal stresses to increase the forming limit of thin metal sheets is proposed. The effects of normal stresses on surface roughening and flow stresses in thin metal sheets were evaluated and quantified by uniaxial tensile experiments under normal stresses. The results show that the normal stress suppresses the increase of surface roughness and improves the work hardening rate. The effect of normal stresses on forming limits was obtained using a constructed model. The results show that the predictions of the proposed model are able to include almost all the ultimate strain points. The forming limit bands are more inclusive and safer than the single forming limit curve. In addition, the forming limits of thin plates under normal stresses are significantly increased, which is attributed to the suppression of surface roughening and the increase of strain hardening index. The forming limit bands proposed in this paper provide a more reliable form of forming limit assessment for thin metal plates. The proposed method of applying normal stresses provides a robust and inexpensive process route to address the challenge of poor formability of thin metal sheets.

Liquid AlCoCrFeNi and AlCoCrFeNiX (X = Mo, Ta) high-entropy alloys on graphite: Wetting, reactivity and CALPHAD modelling

Wettability and interfacial reactivity with graphite of three equimolar High-Entropy Alloys (HEAs), namely AlCoCrFeNi (HEA-base), AlCoCrFeNiMo (HEA-Mo) and AlCoCrFeNiTa (HEA-Ta), were investigated for the first time, aiming to support industrial sectors involving liquid-phase processing routes. Isothermal high-temperature wettability tests were performed at 1400 °C for HEA-base and HEA-Mo, and at 1580 °C for HEA-Ta. The samples were then analysed by SEM-EDS. Based on the in-house-built GHEA thermodynamic database, CALPHAD calculations were used to simulate and discuss liquid-solid interactions occurring at high temperatures as well as transformations taking place in the samples during cooling. HEA-base wetted the graphite well after 5 min and the wetting behaviour significantly improved for HEA-Mo after the same contact time. HEA-Ta, after initial melting and wetting stage, solidified due to high-melting phases formation. All the graphite/HEA samples formed refractory carbides. For HEA-base and HEA-Mo, such carbides were found homogeneously dispersed in the whole drop. In HEA-Ta, TaC formed a compact layer separating two liquids of different compositions, with only one of these being in contact with the graphite substrate. The here proposed combination of experiments and thermodynamic calculations allowed to understand and discuss both high-temperature reactivity and evolution of the systems over cooling. Overall, a remarkable agreement between equilibrium calculations and experimental findings was observed for these complex dynamic systems.

Silicon nitride shadowed selective area growth of low defect density vertical GaN mesas via plasma-assisted molecular beam epitaxy

Ion bombardment during inductively coupled plasma reactive-ion etching and ion-implantation introduces irreparable crystalline damage to gallium nitride (GaN) power devices, leading to early breakdown and high leakage current. To circumvent this, a bi-layer selective area growth mask was engineered to grow up to 3.0 µm thick epitaxy of GaN using plasma-assisted molecular beam epitaxy as an ion-damage-free alternative to standard epitaxial processing routes. The masks and regrown architectures are characterized via SEM, conductive-atomic force microscopy (AFM), x-ray photo electron spectroscopy, Raman, and cathodoluminescence. Mask deposition conditions were varied to modulate and minimize the stress induced during thermal cycling. The resulting mesas exhibit low leakage, attributed to naturally terminated sidewalls as measured by an innovative perpendicular AFM measurement of the regrown sidewall. The regrown sidewall exhibited RMS (root mean square) roughness of 1.50 (±0.34) nm and defect density of 1.36 × 106 (±1.11 × 106) cm−2. This work provides a method to eliminate defect-inducing steps from GaN vertical epitaxial processing and stands to enhance GaN as a material platform for high-efficiency power devices.

Achieving high strength in Mg-Gd-Ag-Zr alloy through heterogeneous microstructure with multimodal grain structure and hierarchical precipitates

The development of high-strength Magnesium (Mg) alloys is a key research focus due to their relatively low strength compared to other structural metals. In this paper, a high-strength Mg-13.4Gd-2.2Ag-0.4Zr wt% alloy is achieved by carefully designing a multimodal grain structure combined with hierarchical precipitates, using a processing route of hot rolling followed by aging. The multimodal grain structure, characterized by three distinct grain size distributions and a strong basal texture, arises from partial dynamic recrystallization during hot rolling. The hierarchical precipitates include microscale rod-like and particle-like β phase at grain boundaries, twins, and deformation bands, as well as nanoscale β phase precipitates (~63nm) dispersed within the grain interiors. The fully β precipitates also demonstrate a modified precipitation pathway, differing from the β' and γ'' nanoprecipitates observed in conventional Mg-Gd-Ag alloys. This unique microstructure results in an ultimate tensile strength of 447MPa, primarily attributable to the strong basal texture, hierarchical precipitates, hetero-deformation-induced (HDI) strengthening, and high dislocation density.

دراسة تأثير نسبة حجم المارتنسيت على سلوك الفولاذ ثنائي الطور

Dual-phase (DP) steels are a subset of high-strength, low-alloy steels that exhibit a unique balance of strength and ductility due to their dual microstructure of ferrite and martensite. This study investigates the effects of varying martensite volume fractions on the mechanical properties of DP steel by preparing samples with martensite contents from 20% to 50%, achieved through intercritical annealing at temperatures between Ac1 and Ac3, followed by quenching in ice-brine. The mechanical properties of these samples were analyzed using optical microscopy, hardness tests, and impact toughness measurements. Results reveal that increasing the martensite volume fraction enhances both hardness and impact toughness, indicating that adjusting heat treatment parameters can optimize DP steels for strength-critical applications. This study provides insight into the microstructural factors influencing DP steel properties and highlights effective processing routes for tailoring material performance. Keywords: Dual-Phase Steel, Microstructure, Heat treatment, Mechanical properties.

Facile Synthesis of Colocasia esculenta Peels‐Derived Activated Carbon for High‐Performance Supercapacitor

ABSTRACTBiomass and biowaste resources can be used to create self‐doped carbon with a distinctive microstructure. Using an economical and environmentally friendly method to create heteroatom‐doped carbon electrode materials with excellent electrochemical performance has attracted much attention in the energy storage industry. A novel facile two‐step, low‐cost, and eco‐friendly synthesis method for Colocasia esculenta peels has been developed to manufacture activated carbon (CEPAC) and used as an electrode material for supercapacitor application. The CEPAC 1:1 displayed a high specific surface area of 910 m2/g with oxygen‐heteroatom polar sites in the carbon network. A specific capacitance of 525.3 F/g was recorded in the three‐electrode system using a 3 M KOH solution. The assembled symmetric cell delivered an impressive specific capacitance of 98.7 F/g at 1 A/g while maintaining 98.4% of the initially recorded capacitance after 10 000 charge–discharge cycles. These results present a promising low‐cost and simple processing route for synthesizing electrode materials with superior surface properties for high‐performance supercapacitors.

An automatic generation approach of process model based on feature knowledge and geometric modeling

Traditional process planning applies knowledge to convert design information within the part model into process information. This information is then displayed by manually drawing two-dimensional (2D) process cards from multiple views, illustrating the geometric design and the process details. However, this method has revealed several issues, including poor efficiency, information misinterpretation, and the potential for manual drawing errors. This traditional approach hampers the integration of manufacturing information across engineering software. It exacerbates the digital divide between computer-aided design (CAD), computer-aided process planning (CAPP), and computer-aided manufacturing (CAM). To address these issues, this study proposes an automated approach for creating three-dimensional (3D) process models based on feature knowledge and geometric modeling. The 3-axis milling features are recognized as the machining objects from the boundary representation (B-Rep) part model. Geometric and parameter knowledge for modeling is derived from the machining step. In the proposed approach, two geometric modeling methods are investigated to construct feature state volumes (FSVs). The first method utilizes FSV modeling to simulate the shape of unmachined features before rough machining, while the second method utilizes FSV modeling to construct the shape of machined features before finishing machining. The case studies illustrate that the proposed approach can construct FSVs for various machining states and autonomously generate process models. In digital manufacturing, this approach assists process planners in intuitively evaluating the reasonableness of process routes. Additionally, it provides the essential driving geometry required for the autonomous generation of tool paths.

Deactivation mechanism of catalyst in long-period experiment of 1,1,2-TCE dehydrochlorination

ABSTRACT VDC, as an important raw material for high barrier material PVDC and lithium-ion battery binder PVDF, had an increasing demand. The catalytic dehydrochlorination of 1,1,2-TCE to generate VDC was a green process route that was expected to replace the heavily polluting sodium hydroxide saponification process in industry. There was a certain research foundation for the catalytic dehydrochlorination of 1,1,2-TCE, among which the core problem was that the catalyst was prone to deactivation. Previous studies had calculated the average molecular structure of the spent catalyst surface species when coconut shell activated carbon was used as the support. In subsequent studies, alumina support had been proven to be a better carrier for dehydrochlorination of 1,1,2-TCE, and the selected catalyst had been used in long-period laboratory experiments. The results indicated that the catalyst life had been significantly improved. In the early stage of the reaction, the activity of the catalyst decreased rapidly. In the first 20 h, the conversion of 1,1,2-TCE decreased from 49.35% to 40.36%. From 21 h to 120 h, the conversion of 1,1,2-TCE decreased from 39.96% to 36.18%, tending to stabilize. After 120 h of reaction, the catalyst was extracted and elemental analysis, FTIR, and GPC characterization were performed on the spent catalyst surface species. The average molecular formula of the carbon deposits was obtained as C96.08H50.29Cl36.53. Compared with coconut shell activated carbon support, under the action of activated alumina support, the route of catalyst carbon deposition had changed, and the sediment was more composed of polyvinylidene chloride components. Under certain conditions, sediment formed a dynamic equilibrium, which explained why the activity of the catalyst gradually stabilized. This study revealed the catalytic mechanism of active alumina supported catalyst and the pathway of carbon deposits generation, which was of great significance for improving the lifespan of improved catalyst.

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.