Wrinkling of corrugated skin sandwich panels

Compression wrinkling of composite sandwich panels with corrugated skins was investigated numerically, analytically and experimentally. Semi-circular and sine-wave shaped corrugations were studied. The corrugations significantly increased wrinkling strength when compared with equal mass flat panels. Semi-circular corrugations proved to be highly preferable to sine-wave shaped corrugations due to localized buckling in the latter. Over 40 fiberglass and foam core sandwich specimens were manufactured with semi-circular skin corrugations. These specimens were tested to failure, providing confirmation of the numerical and analytical results.     

Correlating microstructural features with wear resistance of dual phase steel

In order to explore the tribological potential of the dual phase (DP) steel as a wear resistant material, the wear characteristics of the dual phase (DP) steel have been investigated with varying amounts of martensite from 43 to 81 vol pct, developed by varying holding time at the intercritical annealing temperature of 780 °C. Dry sliding wear tests have been conducted on DP steels using a pin-on-disk machine under different normal loads of 61.3, 68.5, 75.7 and 82.6 N and at a constant sliding speed of 1.20 m/s. At these loads, the mechanism of wear is mainly delamination, which has been confirmed by SEM micrographs of the subsurface and wear debris of the samples. Wear and friction properties have been found to be improved with increasing martensite volume fraction in dual phase steels.     

Effects of alloying elements on fracture toughness in the transition temperature region of base metals and simulated heat-affected zones of Mn-Mo-Ni low-alloy steels

This study is concerned with the effects of alloying elements on fracture toughness in the transition temperature region of base metals and heat-affected zones (HAZs) of Mn-Mo-Ni low-alloy steels. Three kinds of steels whose compositions were varied from the composition specification of SA 508 steel (grade 3) were fabricated by vacuum-induction melting and heat treatment, and their fracture toughness was examined using an ASTM E1921 standard test method. In the steels that have decreased C and increased Mo and Ni content, the number of fine M₂C carbides was greatly increased and the number of coarse M₃C carbides was decreased, thereby leading to the simultaneous improvement of tensile properties and fracture toughness. Brittle martensite-austenite (M-A) constituents were also formed in these steels during cooling, but did not deteriorate fracture toughness because they were decomposed to ferrite and fine carbides after tempering. Their simulated HAZs also had sufficient impact toughness after postweld heat treatment. These findings indicated that the reduction in C content to inhibit the formation of coarse cementite and to improve toughness and the increase in Mo and Ni to prevent the reduction in hardenability and to precipitate fine M₂C carbides were useful ways to improve simultaneously the tensile and fracture properties of the HAZs as well as the base metals.     

THE LINEAR NORMALIZATION TECHNIQUE—AN ALTERNATIVE PROCEDURE FOR DETERMINING J-R CURVES FROM A SINGLE SPECIMEN TEST RECORD BASED ON LANDES' NORMALIZATION METHOD

An alternative approach for determining J-R curves from a single specimen test based on the normalization technique is proposed. The procedure requires only the load versus load-line displacement record and the final crack length in order to derive the J-R curve. The method has been applied to metal and polymers of various geometries and was verified by comparison with J-R curves obtained from potential drop and multiple specimen techniques.     

Making costly manufacturing smart with transfer learning under limited data: A case study on composites autoclave processing

The integration of advanced manufacturing processes with ground-breaking Artificial Intelligence methods continue to provide unprecedented opportunities towards modern cyber-physical manufacturing processes, known as smart manufacturing or Industry 4.0. However, the “smartness” level of such approaches closely depends on the degree to which the implemented predictive models can handle uncertainties and production data shifts in the factory over time. In the case of change in a manufacturing process configuration with no sufficient new data, conventional Machine Learning (ML) models often tend to perform poorly. In this article, a transfer learning (TL) framework is proposed to tackle the aforementioned issue in modeling smart manufacturing. Namely, the proposed TL framework is able to adapt to probable shifts in the production process design and deliver accurate predictions without the need to re-train the model. Armed with sequential unfreezing and early stopping methods, the model demonstrated the ability to avoid catastrophic forgetting in the presence of severely limited data. Through the exemplified industry-focused case study on autoclave composite processing, the model yielded a drastic (88%) improvement in the generalization accuracy compared to the conventional learning, while reducing the computational and temporal cost by 56%.     

An integrated fuzzy regression-data envelopment analysis algorithm for optimum oil consumption estimation with ambiguous data

This study introduces an integrated fuzzy regression (FR) data envelopment analysis (DEA) algorithm for oil consumption estimation and optimization with uncertain and ambiguous data. This is quite important as oil consumption estimations deals with several uncertainties due to social, economic factors. Furthermore, DEA is integrated with FR because there is no clear cut as to which FR approach is superior for oil consumption estimation. The standard indicators used in this paper are population, cost of crude oil, gross domestic production (GDP) and annual oil production. Fifteen popular and most cited FR models are considered in the algorithm. Each FR model has different approach and advantages. The input data is divided into train and test data. The FR models have been tuned for all their parameters according to the train data, and the best coefficients are identified. Center of Average Method for defuzzification output process is applied. For determining the rate of error of FR models estimations, the rate of defuzzified output of each model is compared with its actual rate consumption in test data. The efficiency of 15 FR models is examined by the output-oriented Data Envelopment Analysis (DEA) model without inputs by considering three types of relative error: RMSE, MAE and MAPE. The applicability and superiority of the proposed algorithm is shown for monthly oil consumption of Canada, United States, Japan and Australia from 1990 to 2005.     

Preparation and mechanical characteristics of fine-woven cloth and punctured felt preform Cf/C-SiC-ZrC composite

A 3D architecture carbon fiber preform, specifically fine-woven cloth and punctured felt preform, is used to manufacture a novel advanced C_f/C-SiC-ZrC composite. The composite matrix is produced by chemical vapor infiltration (CVI) plus precursor infiltration and pyrolysis (PIP) process and finalized by using a chemical vapor deposition (CVD) of SiC coating to make the final density of the material reach 1.95 g/cm³. The organic precursors of SiC and ZrC have a weight ratio of 4:1 in a xylene solute. The composite mechanical properties, such as tensile, compression, bending, shear, and Z-direction load bearing, are introduced under analysis to find possible applications for the composite. What is more, scanning electron microscope (SEM) images are employed to illustrate the failure behavior of the ceramic composite. The results showed that the punctured filament tows will be beneficial, not only for the composite to withstand compression force up to 308.6 MPa and shear strength to 18.14 MPa but also for the alternatively stacked weave piles and short fiber layers to support the punctured bundles, as well as to hold the composite structure under mechanical forces from different orientations, which is believed to reinforce the ceramic matrix for some high pressure and severe ablation applications.     

Effect of lamellar spacing on microstructural instability and creep behavior of a lamellar TiAl alloy

Finer lamellar spacing in the lamellar structure of a Ti–45Al–2Nb–2Mn + 0.8 vol.%TiB₂ (45XD) alloy does improve the primary creep resistance. However, the unstable nature of the fine plate contributes largely to the degradation of the lamellar structure and a rapid increase in the tertiary creep rate, indicating that a fine lamellar structure has a detrimental effect on the long-term creep.