Jian Cao

Bio: Jian Cao is an academic researcher from Northwestern University. The author has contributed to research in topics: Sheet metal & Forming processes. The author has an hindex of 58, co-authored 486 publications receiving 11074 citations. Previous affiliations of Jian Cao include Pennsylvania State University & Shanghai Jiao Tong University.

Characterization of mechanical behavior of woven fabrics: Experimental methods and benchmark results

Abstract: Textile composites made of woven fabrics have demonstrated excellent mechanical properties for the production of high specific-strength products. Research efforts in the woven fabric sheet forming are currently at a point where benchmarking will lead to major advances in understanding both the strengths and the limitations of existing experimental and modeling approaches. Test results can provide valuable information for the material characterization and forming process design of woven composites if researchers know how to interpret the results obtained from varying test methods appropriately. An international group of academic and industry researchers has gathered to design and conduct benchmarking tests of interest to the composite sheet forming community. Shear deformation is the dominative deformation mode for woven fabrics in forming; therefore, trellis-frame (picture-frame) and bias-extension tests for both balanced and unbalanced fabrics have been conducted and compared through this collaborative effort. Tests were conducted by seven international research institutions on three identical woven fabrics. Both the variations in the setup of each research laboratory and the normalization methods used to compare the test results are presented and discussed. With an understanding of the effects of testing variations on the results and the normalization methods, numerical modeling efforts can commence and new testing methods can be developed to advance the field.

A continuum mechanics-based non-orthogonal constitutive model for woven composite fabrics

Abstract: A non-orthogonal constitutive model is developed to characterize the anisotropic material behavior of woven composite fabrics under large deformation. A convected coordinate system, whose in-plane axes are coincident with the weft and warp yarns of woven fabrics, are embedded in the shell elements. Contravariant stress components and covariant strain components in a constitutive relation are introduced into the convected coordinate system. The transformations between the contravariant/covariant components and the Cartesian components of the stress and strain tensors provide an approach for deriving the global non-orthogonal constitutive relations for woven composite fabrics. By taking advantage of the tensile–shear decoupling in the constitutive equation under the convected coordinate system, the material characterization of woven fabrics is simplified. As an essential part for these transformations, a fiber orientation model is developed, by using some fundamental continuum mechanics concepts, to trace the yarn reorientation of woven fabrics during deformation. The proposed material characterization approach is demonstrated on a balanced plain weave composite fabric. The equivalent material properties are obtained by matching with experimental data of tensile and bias extension tests on the woven composite fabric. Model validation is provided by comparing numerical results with experimental data of bias extension test and shear test. The development of this non-orthogonal model is critical to the ultimate goal, i.e. using numerical simulations to optimize the forming of woven composite fabric sheets.

Deep learning predicts path-dependent plasticity.

Abstract: Plasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most of its complexity arises from the nontrivial dependence of the yield loci on the complete strain history of a material and its microstructure. This motivated 3 ingenious simplifications that underpinned a century of developments in this field: 1) yield criteria describing yield loci location; 2) associative or nonassociative flow rules defining the direction of plastic flow; and 3) effective stress-strain laws consistent with the plastic work equivalence principle. However, 2 key complications arise from these simplifications. First, finding equations that describe these 3 assumptions for materials with complex microstructures is not trivial. Second, yield surface evolution needs to be traced iteratively, i.e., through a return mapping algorithm. Here, we show that these assumptions are not needed in the context of sequence learning when using recurrent neural networks, diverting the above-mentioned complications. This work offers an alternative to currently established plasticity formulations by providing the foundations for finding history- and microstructure-dependent constitutive models through deep learning.

Linking process, structure, property, and performance for metal-based additive manufacturing: computational approaches with experimental support

Abstract: Additive manufacturing (AM) methods for rapid prototyping of 3D materials (3D printing) have become increasingly popular with a particular recent emphasis on those methods used for metallic materials. These processes typically involve an accumulation of cyclic phase changes. The widespread interest in these methods is largely stimulated by their unique ability to create components of considerable complexity. However, modeling such processes is exceedingly difficult due to the highly localized and drastic material evolution that often occurs over the course of the manufacture time of each component. Final product characterization and validation are currently driven primarily by experimental means as a result of the lack of robust modeling procedures. In the present work, the authors discuss primary detrimental hurdles that have plagued effective modeling of AM methods for metallic materials while also providing logical speculation into preferable research directions for overcoming these hurdles. The primary focus of this work encompasses the specific areas of high-performance computing, multiscale modeling, materials characterization, process modeling, experimentation, and validation for final product performance of additively manufactured metallic components.

Testing and modelling of material behaviour and formability in sheet metal forming

Abstract: The paper deals with the testing and modelling of metals response when subjected to sheet forming operations. The focus is both on the modelling of hardening behaviour and yield criteria and on the description of the sheet metal formability limits. Within this scope, the paper provides a critical review of the models available today for predicting the material behaviour at both industrial and scientific level, and the tests needed to identify the models’ material parameters. The most recent advances in the field are also presented and discussed with particular emphasis on the challenges the sheet metal forming community is now facing.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.