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Thin-Walled Parts (TWP) are widely used in aerospace, whose service performance is significantly affected by the machining accuracy. Considering the poor stiffness of TWP, multi-pass machining is used to machine TWP for better surface accuracy. However, it is difficult to accurately predict the final machining accuracy due to the surface topography error’s propagation and accumulation in multi-pass machining. Therefore, this paper proposes a multi-pass machining accuracy prediction method for TWP based on dynamic factors (cutting force and stiffness). Firstly, a flexible cutting force prediction model, which considers the axial errors determined by the initial surface topography and part deflection, is proposed. Secondly, a Position-Pass-Dependent Stiffness (PPDS) model is established considering position-dependent of stiffness and multi-pass machining material removal. Finally, combining the two models above, a multi-pass machining accuracy prediction method based on Genetic Algorithm - Back Propagation (GA-BP) neural network is proposed. The experiments under various conditions have been carried out to validate the proposed method. The machining accuracy (flatness as an example) is as high as 90.8% using the method in this paper, while it is only 73.9% when the accumulative error is neglected. The proposed method can significantly improve the performance of machining accuracy prediction by analyzing the error propagation mechanism and the effect of dynamic factors between multi-pass machining. Furthermore, this also provides a theoretical basis for process parameters optimization and machining accuracy improvement in TWP machining.

Multi-part Dedicated Flow-line (MDF) is widely used to produce discrete products for high volumes with low varieties. Since frequent changes occur in customers’ demand and rapid upgrading of machines, the line needs to reconfigure timely to remain competitive. A lot of researchers investigate how to reconfigure the line when facing these changes, and most of them adopts small adjustments such as machine resettlement or buffer size rearrangements to react. However, when great changes occur in product’ machining process or machine’s performance, it’s time to reconfigure the line in a higher level by reselecting machining process of products and/or reselecting machines composition of the line. To fill this research gap, the paper proposes an effective Multi-part Dedicated Flow-line Reconfiguration Model (MDFRM) which can effectively react to great changes occur in products and machines. By optimal selection of Machining Process Path (MPP) for every product and machines composition for the new line, MDFRM can reconfigure MDF with objective of minimizing the products’ cost and capital investment. To solve this problem, an Enhanced Equilibrium Optimizer (EEO) with three layers of code and three steps to handling boundaries and constraints based on Equilibrium Optimizer (EO) is presented. The proposed new model and the designed method are validated by numerical experiments and real case study. This paper will significantly help decision-makers make a scientific decision to optimally reconfigure MDF.

Background Deafness, autosomal recessive 16 (DFNB16) is caused by compound heterozygous or homozygous variants in STRC and is the second most common form of genetic hearing loss. Due to the nearly identical sequences of STRC and the pseudogene STRCP1, analysis of this region is challenging in clinical testing. Methods We developed a method that accurately identifies the copy number of STRC and STRCP1 using standard short-read genome sequencing. Then, we used whole genome sequencing (WGS) data to investigate the population distribution of STRC copy number in 6813 neonates and the correlation between STRC and STRCP1 copy number. Results The comparison of WGS results with multiplex ligation-dependent probe amplification demonstrated high sensitivity (100%; 95% CI, 97.5%–100%) and specificity (98.8%; 95% CI, 97.7%–99.5%) in detecting heterozygous deletion of STRC from short-read genome sequencing data. The population analysis revealed that 5.22% of the general population has STRC copy number changes, almost half of which (2.33%; 95% CI, 1.99%–2.72%) were clinically significant, including heterozygous and homozygous STRC deletions. There was a strong inverse correlation between STRC and STRCP1 copy number. Conclusions We developed a novel and reliable method to determine STRC copy number based on standard short-read based WGS data. Incorporating this method into analytic pipelines would improve the clinical utility of WGS in the screening and diagnosis of hearing loss. Finally, we provide population-based evidence of pseudogene-mediated gene conversions between STRC and STRCP1.

Structure elucidation of novel compounds with high flexibility and trace amount is among the hardest tasks in natural product research. In the present study, trace metabolites-targeted LSC strategy was applied for Penicillium sp. sh18 to provide two types of novel scaffolds, heterotetrameric FcA, pentetrafuranone A (1) and heterotrimeric FcA, pentrifuranones A and B (2 and 3). The flexibility and sample shortage obstruct the accessibility to crystallographic and chemical modification methods to determine their structures. Furthermore, the complexity of the structure and the presence of an uncommon "conditional chiral center", coupled with the permutation of NMR data, made conventional techniques ineffective. To overcome these challenges, a combinational method with extensive NMR analysis, including pure-shift 1H NMR, LR-HSQMBC, and multi-step conformational sampling method coupled with spatial sensitive parameters RDC and QID, and fc-rDG/DDD approaches was employed to establish the planar structures and cross-validate their configurations. In addition, antimalaria activity was evaluated and 2 exhibits significant inhibitory effect against Plasmodium falciparum 3D7 at asexual blood-stage with IC50 of 0.72 µM. The present study offers new scaffolds that exhibit significant antimalaria activity. Moreover, it showcases how spatially sensitive-based analytical strategies can serve as a crystallography-independent approach for assigning structure and configuration of intricate natural products.

Entomopathogenic fungi infect insects by penetrating through the cuticle into the host body. To breach the host cuticle, some fungal pathogens produce specialized infection cells called appressoria, which develop enormous turgor pressure to allow cuticle penetration. However, regulatory mechanisms underlying appressorium turgor generation are poorly understood. Here, we show that the histone lysine methyltransferase ASH1 in the insecticidal fungus Metarhizium robertsii , which is strongly induced during infection of the mosquito cuticle, regulates appressorium turgor generation and cuticle penetration by activating the peroxin gene Mrpex16 via H3K36 dimethylation. MrPEX16 is required for the biogenesis of peroxisomes that participate in lipid catabolism and further promotes the hydrolysis of triacylglycerols stored in lipid droplets to produce glycerol for turgor generation, facilitating appressorium-mediated insect infection. Together, the ASH1–PEX16 pathway plays a pivotal role in regulating peroxisome biogenesis to promote lipolysis for appressorium turgor generation, providing insights into the molecular mechanisms underlying fungal pathogenesis.

The surface residual stress significantly influences the performance of gears. The residual stresses of different grinding positions on the gear surface are different. Focusing on the gear profile grinding process, the residual stress distribution of gear tooth flank is investigated. To measure the residual stress of gear tooth accurately, a specific measurement method, including adjustment angle calculation model and alignment fixture, is carried out. The measured results with the proposed method are much better, and differences of residual stresses among measured positions are discussed. Then, considering the variation of chip geometry, a gear grinding model is proposed to analyze the residual stress distribution of gear tooth flank. The changing trend of the section area of the cutting chip calculated by the proposed grinding model matches the changing trend of measured results of the residual stress distribution. Finally, experiments are conducted, and the grinding model is verified from the perspective of cutting parameters.

Xylomyrocins, a unique group of nonribosomal peptide secondary metabolites, were discovered in Paramyrothecium and Colletotrichum spp. fungi by employing a combination of high-resolution tandem mass spectrometry (HRMS/MS)–based chemometrics, comparative genome mining, gene disruption, stable isotope feeding, and chemical complementation techniques. These polyol cyclodepsipeptides all feature an unprecedented d -xylonic acid moiety as part of their macrocyclic scaffold. This biosynthon is derived from d -xylose supplied by xylooligosaccharide catabolic enzymes encoded in the xylomyrocin biosynthetic gene cluster, revealing a novel link between carbohydrate catabolism and nonribosomal peptide biosynthesis. Xylomyrocins from different fungal isolates differ in the number and nature of their amino acid building blocks that are nevertheless incorporated by orthologous nonribosomal peptide synthetase (NRPS) enzymes. Another source of structural diversity is the variable choice of the nucleophile for intramolecular macrocyclic ester formation during xylomyrocin chain termination. This nucleophile is selected from the multiple available alcohol functionalities of the polyol moiety, revealing a surprising polyspecificity for the NRPS terminal condensation domain. Some xylomyrocin congeners also feature N- methylated amino acid residues in positions where the corresponding NRPS modules lack N- methyltransferase (M) domains, providing a rare example of promiscuous methylation in the context of an NRPS with an otherwise canonical, collinear biosynthetic program.

The growing threat of insecticide resistance prompts the urgent need to develop additional tools for mosquito control. Entomopathogenic fungi provide an eco-friendly alternative to chemical insecticides. One limitation to the use of mycoinsecticides is their relatively low virulence. Here, we report an approach for suppressing mosquito immunity and increasing fungal virulence. We engineered Beauveria bassiana to express Aedes immunosuppressive microRNAs (miRNAs) to induce host RNA interference (RNAi) immune responses. We show that engineered strains can produce and deliver the miRNAs into host cells to activate cross-kingdom RNAi during infection and suppress mosquito immunity by targeting multiple host genes, thereby dramatically increasing fungal virulence against Aedes aegypti and Galleria mellonella larvae. Importantly, expressing host miRNAs also significantly increases fungal virulence against insecticide-resistant mosquitoes, creating potential for insecticide-resistance management. This pathogen-mediated RNAi (pmRNAi)-based approach provides an innovative strategy to enhance the efficacy of fungal insecticides and eliminate the likelihood of resistance development.

Surface roughness, which has a significant influence on fatigue strength and wear resistance, is an important technical parameter. In practical machining, it is unstable and may be larger than the acceptable surface roughness due to unstable machining process. This will seriously deteriorate the surface performance of the workpieces. Therefore, an effective surface roughness stabilization method is of great significance to improve machining efficiency and reduce machining cost. In this paper, a surface roughness stabilization method is proposed and illustrated by taking five-axis machining as an example. A self-learning surface roughness prediction model based on Pigeon-Inspired Optimization and Support Vector Machine is firstly constructed and its prediction error is only 8.69% in the initial stage. This model has the self-learning ability that the prediction accuracy can be improved with the increase of training data. Furthermore, a machining parameters self-adaption adjustment method based on digital twin is proposed to make the machined surface quality stable. In this method, considering the feasibility of practical machining operation, the cutter posture (i.e. lead angle and tilt angle in five-axis machining) and spindle speed are selected as the adjustable parameters. When the predicted surface roughness doesn’t meet the requirements, the Gradient Descent algorithm is applied to recalculate the new parameters for adjustment. According to the experimental results, the proposed method can stabilize surface roughness and improve the surface quality, which is vital for the precision manufacturing of complex workpiece. Meanwhile, it also greatly improves the intelligence level of manufacturing and production.

With the increasing market features of globalization, service and customization, the way manufacturers conduct manufacturing business is changing. Under this background, Cloud Manufacturing (CMfg) emerges as a new networked manufacturing model. However, CMfg is immature in many aspects, especially in exception handling of service composition execution. Due to the complexity of the enterprise manufacturing process, there are a large number of unpredictable abnormal events in the dynamic open cloud manufacturing environment (such as user demand change, machine failure, etc.), so in order to ensure the smooth implementation of the service combination, it is indispensable to establish an effective service exception handling mechanism in CMfg. Moreover, when an exception occurs, in order to ensure the smooth execution of the downstream services after the exception point, the exception handling must satisfy the strict time constraints. To realize the exception-handing of service-composition with the strict deadline or strict time constraints, this paper proposes a service-composition exception adaptive adjustment model, considering the influences of the logistics transferring time and cost. And the occupied time of the cloud services and the valid replacement time range of the exception service are considered as the constraints in this model. In addition, the processing quality, the cost, and the quality of service are set as the optimal objectives. On the above basis, a service-composition exception handling adaptive adjustment (SCEHAA) algorithm based on the improved ant colony optimization algorithm (ACO) is proposed and applied to address the above model. Finally, to validate the performance of SCEHAA, a case study and the comparison experiment between SCEHAA and other algorithms (Particle Swarm Optimization and Artificial Bee Colony) are performed. The results show that the SCEHAA algorithm can perform the adaptive adjustment of the service-composition with strict time limit effectively, through the adaptive service execution path reconfiguration and has fast convergence effects.

In flexible manufacturing systems, disordered parallel machine scheduling may cause interruptions in the production line; thus, a reasonable scheduling plan benefits profits of manufacturing. In the literature, the operation sequence, the operation assignment, the tool scheduling, and the tool switching problems have been studied with unlimited resources. However, in practical manufacturing process, a scheduling problem along with these problems with restrained resources should be studied to make scheduling solutions more performable. There are a number of operations processed on a group of parallel machines, and each operation requires a set of tools where the tool copy number and tool service life are limited. The objective of this problem is to minimize the make-span with restrained resources. This article studies a parallel machine scheduling problem combining operation scheduling, tool scheduling and restrained resources, which benefit the real industry. A Tabu-Genetic Algorithm is proposed to find the optimal solution. In small size problem, the proposed method can achieve similar results whereas the mixed integer programming approach can obtain the exact solution. However, in large size problem, the latter cannot obtain the solutions within acceptable times, whereas the proposed method can find solutions within reasonable times. This algorithm performs better when compared to Tabu search algorithm and genetic algorithm in selecting local and global optima.

As a kind of cylindrical spring structured with a helically wound strand, the stranded wire helical springs are widely employed as reset components in engineering fields. The accurate description of the spring geometry is critical for its design, manufacture, and further theoretical and finite element method (FEM) analysis. However, the outside wires of the spring were assumed to be double-helixes in commonly used geometric model without considering the interwire contact interactions, which introduced non-negligible geometric error. To cope with this problem, a deformation-based accurate geometric model considering the interwire contact interactions is presented in this paper. By combining the geometry constraints between wires with the thin rod theory and an elastic–plastic contact model, a novel algorithm is established to determine the interwire contact behaviors and the coordinates of the nodes on the wire centerlines iteratively. The FEM simulations and the experiments are performed to verify the effectiveness of the proposed model. The results show that the accuracy of the proposed model is similar to that of FEM but much higher than that of the commonly used geometric model. The effects of the strand curvature and the outside wire pitch on the spring geometry are analyzed with the presented model as well.

Modern manufacturing enterprises are consuming a considerable amount of energy every year. Improving energy efficiency will not only benefit the enterprises economically, but also help the world to overcome various problems such as energy crisis and air pollution. To achieve this, an accurate energy consumption model is essential. The main objective of this paper is to develop an improved cutting power-based energy consumption model for general end milling process. The proposed model consists of an idle part due to auxiliary components and spindle rotation, and an additional part due to cutting workpiece materials. The first part is modelled as a function of spindle rotation speed, and the other part is considered proportional to the cutting power. Experiments under various milling conditions have demonstrated the effectiveness and efficacy of the proposed model. Comparative studies show that the proposed model is more accurate than other models. Although calibrated from slotting experiments when cutting aluminium alloy, the proposed model is applicable for general milling process. Partial-immersion milling experiments show that the prediction error of the proposed model is as low as 1.74%. When workpiece material changes to titanium alloy, its performance remains decent, with low prediction error of 2.81%. This reveals its capability to provide reliable estimation for different workpiece materials. As such, it could help avoid tedious model calibration, thus saving time, material, and energy. Finally, the energy efficiency of general end milling process is investigated through numerical experiments with the proposed model. By revealing the relationship between energy consumption and various cutting parameters, the proposed model could serve as an excellent platform towards energy-efficient manufacturing/cleaner production.

Tool path generation is an important task in end milling. In the existing literature, the tool path pattern/style is determined based on geometric analysis. However, there is little analysis from the viewpoint of machining dynamics/cutting force, which is more related to the final manufacturing performance. Although there are many types of tool paths (e.g., zigzag, spiral, and ISO-scallop tool path), they can be decomposed into a number of circular tool paths with various path radii and cutting angles. Firstly, by investigating chip formation mechanism, the effect of tool path radius on uncut chip thickness is obtained. Analysis shows that tool path radius has a significant influence on uncut chip thickness, thus affecting the cutting force, especially when the tool path radius is small. Then, the effect of cutting angle on cutting force distribution is analyzed. It is found that the feed direction could vary the distribution of cutting force in X and Y direction in 3-axis end milling. Based on such analysis, the cutting force prediction model considering tool path effect is established. To validate the proposed model, various experiments have been carried out based on well-designed tool paths. The comparative results show that (1) tool path has great effect on cutting force through the tool path radius, rotation direction, and start/end point, and (2) the proposed model is capable of providing accurate cutting force prediction considering tool path effect. From a physical viewpoint, the proposed method provides huge potential in automated/intelligent tool path optimization towards better surface quality via minimizing cutting force, which significantly contributes to the state of the art in tool path generation.

Recently, energy-efficient process planning has aroused numerous concerns from both industry and academia. However, as one of the most important tasks of process planning in discrete parts manufacturing system, tool selection has seldom been investigated in terms of energy efficiency. Without a good understanding of its effects on energy efficiency of machining process, energy-efficient manufacturing cannot be achieved to a satisfactory level. To bridge this research gap, this paper studies tool selection effects by analysing tools with different geometrical parameters, i.e. cutter radius, flute number and helix angle. Their effects on energy efficiency of slot milling process are analysed using experimental approach and theoretical approach, respectively. In experimental approach, Taguchi method is firstly applied to study the effects of each tool geometrical parameter. Then, analysis of variance (ANOVA) is performed to study the significance of each factor. In theoretical approach, the influence of tool selection on cutting power is analytically revealed. Subsequently, the energy efficiency of different cutters is quantitatively analysed through numerical experiments. The results are quite promising with both approaches leading to the same conclusion. The rank of influence is cutter radius > flute number > helix angle. Both methods suggest that a milling cutter with a larger cutter radius and fewer flutes will improve the energy efficiency in milling process. Although there is a small discrepancy in helix angle, it does not affect the good agreement of these two methods since the influence of the helix angle is nearly negligible. Compared to experimental approach, theoretical approach may have more application potential since it may help reduce time and waste of materials. To the best knowledge of the authors, the effects of tool selection on energy efficiency in milling process are systematically analysed for the first time, which significantly advances the state of the art in energy-efficient process planning.

Voids formed during liquid composite molding significantly degenerates mechanical performances of the final products, accurate prediction of the formation and size of void has significance for the parameter design of LCM. However, the 3D simulation method of void formation, in which the complex interconnecting of pores can be fully considered, has not been developed. In order to analyze the meso-scale-void formation process in full dimensionality, the mechanisms of dual-scale flow and void formation are analyzed firstly in this paper, then the mathematical models for the two-phase inter-tow and intra-tow flow are established based on the VOF theory. During numerical solving, the 3D geometry model is used and the momentum source of capillary force is updated in real-time to guarantee the simulation accuracy. The simulated formation process and size of meso-scale-void are compared with experimental results to verify the effectiveness and correctness of the developed method.