Processing Route Research Articles

Impact of Ultra-Flash Tempering Treatment on the Microstructure and Mechanical Properties of High-Strength Carbon Steel

A novel high-frequency induction technology was successfully developed to carry out ultra-flash tempering treatment (UFTT) of high-strength carbon steel (HS-CS) with a heating rate of 1000 °C/s. The as-received HS-CS is a fully martensitic structure with low toughness. UFTT strategy at various temperatures is proposed to produce a tempered martensitic lath structure with promoting carbide precipitates in this structure. Microstructure evolution during UFTT was characterized using secondary electron imaging and electron backscatter diffraction technique in a scanning electron microscope. Micro-indentation hardness tests were measured through the cross-section of the steel to analyze the impact extent of UFTT. The mechanical properties were measured by uniaxial tensile tests. The results revealed that UFTT at various temperatures (550-650 °C) significantly affected the microstructure and the mechanical strength of the steel. A fully tempered martensitic microstructure with various types of carbide precipitates was promoted. Although, the microhardness and tensile strength of flash-tempered steel decreased owing to the breakdown of lath and dislocation structure in the achieved microstructure by UFTT. Hence, it is expected that the promoted microstructure during UFTT in the tested steel will result in a superior strength-toughness synergy. Based on the achieved results, the UFTT technique provides an alternative route for the conventional processing to tailor the microstructure of microalloyed HS-CS, consequently, optimizing the mechanical performance. Meanwhile, Economically, it is a cost-effective route to manufacture advanced high-strength steel.

Double Soaking of Medium Manganese Steels

Double soaking (DS) has been proposed as an alternate processing route for medium manganese steels. DS consists of soaking in the intercritical annealing region to stimulate manganese enrichment of austenite by depletion of ferrite followed by secondary soaking at a higher temperature and cooling to room temperature to obtain a martensite/austenite microstructure. DS is different from more traditional medium manganese heat treating which usually involves a single soaking step in the intercritical region to generate a ferrite/austenite microstructure. DS has been shown effective at generating attractive tensile properties notably tensile strength levels in excess of typically observed levels in medium manganese steels. A review of properties and microstructural evolution obtained by DS of medium manganese steels is presented here.

Development of polyamide 6‐graphene oxide nanocomposite and direct‐joining with aluminum alloy for lightweight engineering applications

AbstractLightweight polyamide composites and structures have been widely studied and employed in several industrial sectors. In this study, the development of a nanocomposite of polyamide 6 and graphene oxide (PA6‐GO) by melt‐compounding and the joining with aluminum alloy AA6061 through direct‐adhesion injection overmolding are addressed. PA6‐GO nanocomposite exhibited well‐dispersed thin (300 nm) GO platelets distributed within the PA6 matrix, which resulted in an increase of 21% in the tensile modulus and 16% in the yield stress for a filler loading of 0.5 wt%. PA6‐GO/AA6061 hybrid joints showed good mechanical anchorage through the complete filling of the polymer into micro‐scale grooves designed onto the metal surface, which ensured outstanding shear strength of 7.1 ± 1.0 MPa, comparable to other polyamide‐metal hybrid joints reported in the literature. This indicates that the processing routes and materials developed are promising for lightweight engineering applications.Highlights A PA6‐GO nanocomposite with enhanced tensile properties was developed. A joint of PA6‐GO and AA6061 with outstanding shear strength was obtained. Fast and efficient melt‐processing routes were employed. These materials are promising for lightweight engineering applications.

The Effect of Ti/Ta Ratio and Processing Routes on the Hardness and Elastic Modulus of Porous TiNbZrTa Alloys

TiNbZrTa alloys are promising for multidisciplinary applications, such as refractory and biomedical purposes, due to their high thermal stability and non-toxicity. Hardness and elastic modulus are among the key features for their adequate industrial applications. The influence of porosity and Ti/Ta ratio were investigated on TiNbZrTa alloys produced by three different processing routes, i.e., (i) blend element and posterior press and sintering (BE + P&S); (ii) mechanical alloying with press and sintering (MA + P&S); and (iii) arc melting and casting. Porosity decreased in the following order: casting < MA + P&S < BE + P&S. The total porosity of alloys increased with increasing Ta contents, i.e., by lowering the Ti/Ta ratio. However, the Ti/Ta ratio did not considerably affect the bonding energy or the elastic modulus. Hardness was increased significantly in dense alloys compared to porous ones. However, porosity and Ti/Ta ratio did not show a clear trend in hardness among the porous alloys.

Superior strength-ductility balance of an extremely low stacking fault energy (SFE) high-entropy alloy (HEA) processed by novel hybrid-rolling

An extremely low SFE Co20Cr26Fe20Mn20Ni14 FCC HEA was processed by warm-rolling (WR) and a novel hybrid-rolling (HR) (cryo-rolling followed by warm-rolling). The HR material showed finer nanostructure and a larger fraction of Cr-rich precipitates due to the finer microstructure and greater driving force resulting from the prior cryo-rolling step. Annealing resulted in finer recrystallized FCC grain size (GS) and a significantly higher fraction of in-grain precipitate in the HR material. The finer GS and higher in-grain precipitate fraction in the annealed HR material resulted in remarkably high strength (∼1125 MPa) and elongation (∼20%). The results highlighted the significant potential of this novel processing route for tailoring microstructure and properties of HEAs.

Automated model generation framework for material flow simulations of production systems

Owing to increasingly important drivers for change, such as automation, digitisation, and dynamic demand patterns, simulation models of production systems become outdated rapidly. At the same time, building simulation models often requires much time, cost, and expertise, especially when dealing with complex job shop production systems. To address these challenges, an automated simulation model generation (ASMG) framework for material flow simulation of production systems is presented. This framework contains multiple approaches to infer routeing, control and temporal aspects from event-based data. To achieve this, methods from process mining (PM) and machine learning (ML) are applied. Additionally, the suitability of Coloured Petri Nets (CPNs) to serve as conceptual and operational simulation models is examined. The inferred simulation models have high validity when compared to the real system concerning the KPIs machine utilisation, throughput, and work in process. It is shown, that most model elements can be inferred very well, in particular process routes, processing times, and resource selection rules. This proof of concept serves as a foundation for research on detection approaches inferring further model elements such as setup times accurately.

Powder bed fusion additive manufacturing of ultra-high molecular weight polyethylene using a novel laser scanning strategy

Current processing routes (ram extrusion, compression molding, and sheet molding) for fabricating ultra-high molecular weight polyethylene (UHMWPE) greatly limit the geometries that can be processed. Due to these limitations, additive manufacturing (AM) of UHMWPE has long been a goal, as the ability to fabricate complex geometries from this polymer could open new applications not possible using these traditional processing routes. UHMWPE’s especially high wear properties also fill a gap in current AM materials. Powder bed fusion (PBF) processing of UHMWPE is challenging due to the high melt viscosity of UHMWPE and melt explosion, which occurs during melting of UHMWPE and causes powder particle expansion during scanning and leads to powder recoating failure. This work demonstrates a novel scan strategy for PBF of UHMWPE, in which layers are scanned using large (1.2 mm) hatch spacings. Scanning with a large hatch spacing reduces conduction between scanlines and distributes energy so as to encourage coalescence without melt explosion. UHMWPE powder is only partially melted during this scanning approach, which causes light coalescence between particles and avoids z-direction expansion that disrupts powder recoating. The lightly coalesced “green” parts are brittle and are densified by thermally post-processing them in the melt. Parts retain their shape during post-processing due to the high melt viscosity of UHMWPE. Using this combination of a novel scan strategy with large hatch spacing and a post-processing thermal treatment, a 3000 kDa UHMWPE was successfully printed, demonstrating the first reported multi-layer PBF of UHMWPE with complex geometries.

Structural and magnetic behaviors of Fe-based glassy alloys prepared by industrial raw materials and different processing routes

Structural and magnetic behaviors of Fe-based glassy alloys prepared by industrial raw materials and different processing routes

Plasma powder transferred arc additive manufacturing of ((Fe, Ni)-Al) intermetallic alloy and resulting properties

Intermetallic alloys such as iron aluminides are of increasing interest for high-temperature applications due to their properties. However, their application potential is restricted by their limited machinability with conventional manufacturing methods such as milling. Arc-based additive manufacturing offers an approach to produce these materials to final contour or with very little post-processing. However, the properties of many intermetallic alloys, such as low toughness, require a special manufacturing process. Using a selected iron-nickel-aluminum intermetallic compound as an example, a possible process, including a subsidiary heat treatment, for the arc-based additive manufacturing of materials based on brittle intermetallic materials is presented. This process route could enable the production of structural components. In addition, some basic mechanical properties that can be achieved in a component are shown proportionally. These properties include initial investigations into the wear resistance of this kind of compounds. It is shown that these intermetallic compounds have a superior wear resistance compared to commonly used co-base alloys but have a lower density compared to cobalt-basis alloys.

Minimal processed infant formula vs. conventional shows comparable protein quality and increased postprandial plasma amino acid kinetics in rats.

During industrial processing, heat treatments applied to infant formulas may affect protein digestion. Recently, innovative processing routes have been developed to produce minimally heat-processed infant formula. Our objective was to compare the in vivo protein digestion kinetics and protein quality of a minimally processed (T−) and a heat-treated (T+++) infant formula. Sixty-eight male Wistar rats (21 d) were fed with either a diet containing 40 % T− (n 30) or T+++ (n 30), or a milk protein control diet (n 8) during 2 weeks. T− and T+++ rats were then sequentially euthanised 0, 1, 2, 3 or 6 h (n 6/time point) after ingestion of a meal containing their experimental diet. Control rats were euthanised 6 h after ingestion of a protein-free meal to determine nitrogen and amino acid endogenous losses. Nitrogen and amino acid true caecal digestibility was high for both T− and T+++ diets (> 90 %), but a tendency towards higher nitrogen digestibility was observed for the T− diet (96·6 ± 3·1 %) compared with the T+++ diet (91·9 ± 5·4 %, P = 0·0891). This slightly increased digestibility led to a greater increase in total amino acid concentration in plasma after ingestion of the T− diet (P = 0·0010). Comparable protein quality between the two infant formulas was found with a digestible indispensable amino acid score of 0·8. In conclusion, this study showed that minimal processing routes to produce native infant formula do not modify protein quality but tend to enhance its true nitrogen digestibility and increase postprandial plasma amino acid kinetics in rats.

Global routing between capsules

Current convolutional neural networks (CNNs) lack viewpoint equivariance. Hence, they perform poorly when dealing with viewpoints unseen during training procedures. CNN achieves invariance via pooling operations in image classification tasks. However, the pooling operation does not necessarily improve viewpoint generalization, rather relying on more data to achieve viewpoint equivariance. Capsule network (CapsNet) is proposed to tackle this issue, but it is inefficient and inaccurate when applied to complex datasets. We propose a novel CapsNet architecture called Global Routing CapsNet (GR-CapsNet) to solve this problem. Specifically, colored background in the input image can generate invalid background voting capsules to reduce the performance of CapsNet. Therefore, we first construct a dynamic linear unit (DLU), which avoids the generation of invalid background voting capsules. Then we present two extra learnable units: frequency domain unit (FDU) and spatial unit (SPU). The former is used to capture finer features in the frequency domain and aims to improve classification performance on complex datasets. The latter is applied to construct the spatial relationship between the voting capsules and component capsules and aims to enhance robustness to affine transformation. Finally, we propose a global routing mechanism to simplify the routing process for CapsNet, which obtains more feature information to improve the performance of CapsNet. Extensive experiments on nine datasets show that our method obtains better robustness and generalization and achieves SOTA performance compared to other related methods. And it has fewer the number of parameters and GPU memory consumption than these related methods. The source code is available on https://github.com/cwpl/GR-CapsNet.

Untangling the impacts of land cover representation and resampling in distributed hydrological model predictions

Accurate land cover information is essential for hydrometeorological modeling, as it defines parameters governing land-atmosphere-vegetation interactions, water partitioning, and routing. This study presents a controlled sensitivity analysis that evaluated the impact of land cover resampling methods on hydrologic simulations within the WRF-Hydro/NWM framework. The choice of resampling algorithms affected simulations but was generally limited to arid environments, catchments featuring minor land cover classes with high hydrologic impact, or low-flow predictions. We tested two distinct spatial aspects: areal proportions of land cover classes and spatial patterns in land cover. Areal proportions influenced vertical hydrologic fluxes at the catchment scale and subsequently affected streamflow characteristics. In contrast, spatial arrangement alone had a marginal impact on vertical fluxes but could still induce limited alterations in streamflow characteristics through routing processes. These results suggest that spatially distributed land cover, as used in physics-based model structures, has a limited impact on watershed-scale hydrologic simulations.

Phenoxythiazoline (FTz)‐Cobalt(II) Precatalysts Enable C(sp2)–C(sp3) Bond‐Formation for Key Intermediates in the Synthesis of Toll‐like Receptor 7/8 Antagonists**

AbstractEvaluation of the relative rates of the cobalt‐catalyzed C(sp2)–C(sp3) Suzuki–Miyaura cross‐coupling between the neopentylglycol ester of 4‐fluorophenylboronic acid and N‐Boc‐4‐bromopiperidine established that smaller N‐alkyl substituents on the phenoxyimine (FI) supporting ligand accelerated the overall rate of the reaction. This trend inspired the design of optimal cobalt catalysts with phenoxyoxazoline (FOx) and phenoxythiazoline (FTz) ligands. An air‐stable cobalt(II) precatalyst, (FTz)CoBr(py)3 was synthesized and applied to the cross‐coupling of an indole‐5‐boronic ester nucleophile with a piperidine‐4‐bromide electrophile that is relevant to the synthesis of reported toll‐like receptor (TLR) 7/8 antagonist molecules including afimetoran. Addition of excess KOMe⋅B(OiPr)3 improved catalyst lifetime due to attenuation of alkoxide basicity that otherwise resulted in demetallation of the FI chelate. A first‐order dependence on the cobalt precatalyst and a saturation regime in nucleophile were observed, supporting turnover‐limiting transmetalation and the origin of the observed trends in N‐imine substitution.

Spatially Tuned Properties in a Bulk Fe–Co Soft Magnetic Alloy via Transverse Induction Annealing and Feasibility Study for Motor Applications

Electric vehicle adoption has seen a dramatic upward trend in the last decade. Maximal efficiency of electric motor technology for maximum range, achieved by minimizing losses experienced by motor stator and rotor, is limited by a trade‐off with high strength, as necessary to withstand torque experienced during operation. Spatial tailoring of magnetic and mechanical properties for optimized magnetic performance while retaining sufficient mechanical strength is a desirable pathway toward improved performance. Here, radiofrequency transverse induction annealing is demonstrated as a novel pathway to continuous spatial variation in microstructure of bulk crystalline soft magnetic alloys, providing a novel processing route to spatially tuned properties. Via local control of grain size, magnetic and mechanical properties may be separately prioritized as functions of position, enabling optimized stator laminations with reduced losses while retaining requisite mechanical strength. Experiment and finite element modeling demonstrate that a cylindrical induction coil can impose significant microstructure and property variation in a Fe–Co bulk crystalline soft magnetic alloy. A basic feasibility study follows, examining improved performance of a motor containing a stator with radial variation in coercivity. Results indicate future potential for co‐optimization of spatial thermal processing of magnetic laminations with motor designs for unprecedented performance.

Processing-microstructure correlations in material extrusion additive manufacturing of carbon fiber reinforced ceramic matrix composites

Liquid silicon infiltration is a cost-effective manufacturing route for carbon fiber reinforced silicon carbide (C/C-SiC)11C/C-SiC: carbon fiber reinforced silicon carbide, which is a ceramic matrix composite with high specific stiffness associated with damage tolerance. To reduce the cost-intensive machining while improving the geometric freedom of design, a processing route for C/C-SiC based on material extrusion additive manufacturing was investigated. Filament-based material extrusion was applied to manufacture green bodies made of short carbon fiber reinforced polyetheretherketone (PEEK)22PEEK: polyetheretherketone. Additionally, a thermo-oxidative crosslinking step, which is assumed to be a function of the specific surface area, was introduced prior to pyrolysis to prevent the re-melting of PEEK. Via computer-aided image analysis, the influence of process parameters during material extrusion on the green bodies’ specific surface area were investigated using statistical design of experiments. It was shown that it is possible to selectively adjust the specific surface area by modifying the microstructure of the carbon fiber reinforced green bodies and hence the shrinkage during pyrolysis, the liquid silicon infiltration kinetics as well as the phase composition of the obtained C/C-SiC. The understanding of the processing-microstructure correlations enabled an improvement of the mechanical properties. With a layer height of 0.1 mm, an infill density of 100% and a printing direction of ± 45°, a flexural strength of 80.3 ± 6.7 MPa was measured.

A novel environmental friendly and sustainable process for textile dyeing with sulphur dyes for cleaner production

This investigation herein primarily focuses on developing a new textile dyeing process with sulphur dye class at room temperature using bacterial Lysate. Sulphur dyes are applied on cotton to produce dark shades at a lower cost, primarily providing excellent colour fastness, except against chlorinating agents. The insoluble sulphur dyes, like vat dyes, are first reduced and then applied as soluble leuco compounds that need to be kept under alkaline conditions, along with a reducing agent. The conventionally followed method of dying process involves the process of reduction of dyes using hazardous chemicals as reducing agent and alkali at higher temperature as well as subsequent dyeing at higher temperatures (60–80 °C) varying with the nature of dyes and processes. It eventually eliminates traditional heating arrangements and achieving easier control of the dyeing process at room temperature than at high temperatures. This investigation compares the newly designed processing route with the followed traditional process in the context of exhaust dyeing and rope dyeing methods with padding mangle. In addition, it involves in substituting the conventional reducing agent i. e. sodium sulphide partially with sodium hydrosulphite for sulphur dyeing. In nut-shell, this process comprises of reduction of dyes in the presence of an alkali agent, a reducing agent and a catalytic agent, wherein the reducing catalytic agent is a bacterial Lysate, establishing it to be environmentally viable, energy and cost effective, and sustainable dying process along with promising improvement quality in terms of dye uptake and comparable fastness properties.

A novel method based on deep reinforcement learning for machining process route planning

Efficient and high-quality machining process route planning is crucial in the realm of manufacturing. Traditional methods heavily rely on human-computer interaction, which can be inefficient. To enhance the efficiency of machining process route planning, this paper introduces a novel framework based on deep reinforcement learning (DRL), designed to automatically generate machining process routes for designated parts. The framework treats machining process route planning as a Markov decision process, making it amenable to DRL techniques. For effective representations of parts, the framework utilizes directed attributed adjacency graphs, wherein nodes represent machining features and edges represent their relationships. To effectively process the graphs, convolutional graph neural networks are employed as the underlying neural networks in the framework. After training, the framework is able to generate efficient machining process routes while adhering to machining process rules. Experimental studies conducted on a number of aircraft structural parts serve as examples to show the feasibility and effectiveness of the proposed approach. The experimental results underscore the effectiveness of the proposed method in machining processes planning for parts and its ability to overcome limitations in traditional methods. As a result, this study contributes to the enhancement of process planning efficiency within the manufacturing domain, carrying practical implications.

Influence of processing techniques on mechanical and electrical performance of PLA-graphene composite feedstock filaments

The 3D printable PLA-graphene composite feedstock filaments can be used to print components that exhibit multi-functionality. The present work analyzes different processing routes and proposes a standard procedure for making PLA-graphene composite feedstock filaments. We used two routes to process graphene-based fillers: the modified Hummers process (HG) and electrochemical exfoliation (EG). After that, PLA-graphene composites with variable filler loadings were processed by mixing different solvents, DMF and CHCl3. Then, printable feedstock filaments were processed from these composites by using a desktop single-screw extruder. It was observed that solvent type influences mechanical properties, and filler type influences electrical properties. The DMF-based composites showed high ductility compared to CHCl3-based composites. The EG-based filaments showed higher electrical conductivity than HG-based filaments. The printability of processed filaments was investigated by 3D printing test samples using fused filament fabrication (FFF). It was observed that CHCl3 improved printability at low filler loading, and DMF improved printability at high filler loading. The 3D-printed samples showed higher mechanical strength at 0.3 wt% for all filament combinations. The electrical properties of 3D printed samples are highest for EG. It is suggested to use 5 wt% of EG with DMF for enhanced electrical properties of printed samples. Also, 0.3 wt% HG with CHCl3 is suggested for enhanced mechanical properties of printed samples.

Uncertainty analysis on flood routing of embankment dam breach due to overtopping failure

The breach risk of reservoir dam exists objectively while exerting benefits. There is great uncertainty in the overtopping breach process of embankment dam and the dam break flood routing which affects the accuracy of dam risk analysis and may cause unnecessary waste in emergency scheduling. However, the uncertainty analysis of the breach process and its consequence is currently inadequate. Therefore, a stratified sampling Monte Carlo method is proposed to simulate uncertain overtopping breach flood of embankment dams. The main sources of uncertainty are analyzed and determined as uncertain dam breach and flood routing processes. The uncertain breach process of dam is studied by presenting a sensitive study between 3 mechanism breach models and 5 parametric breach models. The random dam breach process is restored using HEC-RAS semi-empirical breach model by estimating breach characteristics through multiple common breach models. The random flood routing is carried out through 1D–2D coupled unsteady flow analysis in which the random breach process is adopted as an upper inflow boundary condition. According to the case study results, though parameters have been controlled in a limited range, the flood routing results in the early stage of dam overtopping failure present greater uncertainty. As the flood progresses further downstream, the uncertainty will gradually decrease. This study could serve as a reference for dam breach risk map making.

Machining feature process route planning based on a graph convolutional neural network

The machining processes of machining features, as the crucial components of the machining process for the overall part, significantly impact machining quality and production efficiency. However, the existing methods for machining feature process planning primarily focus on the information of individual machining features and lack sufficient consideration of the overall part information. This deficiency results in reduced effectiveness of process design outcomes and limited direct applicability, necessitating significant manual adjustments. To address these limitations, we propose a novel approach for machining feature process planning using graph convolutional neural networks. The proposed method utilizes an attribute graph to efficiently represent the information of a part. In this representation, nodes symbolize machining features, while edges describe their interaction relationships. Subsequently, a graph convolutional neural network is constructed for learning the machining feature process planning model. After training, the proposed model achieved 93.31% accuracy in predicting process routes for machining features. In addition, the experimental results demonstrate the successful resolution of some current limitations in learning-based machining feature process planning. These findings underscore the potential of intelligent automation in this domain. Overall, this research contributes to the progress of intelligent process planning in manufacturing.