Giuseppe Puglisi is a full professor of Mathematical Physics at the Politecnico di Bari. After graduating as a Structural Engineer at Politecnico di Bari, he took his PhD in Materials and Structural Engineering at the Trento University, working on smart materials at the AME Department of the University of Minnesota. After several periods as visiting researcher in prestigious European universities, he became researcher and then associate professor of Continuum Mechanics and Structural Engineering at Politecnico di Bari. He is a member of the Society of Natural Philosophy (SNP), International Society for the Interaction of Mechanics and Mathematics (ISIMM), and Italian Group of Mathematical Physics (GNFM). His research topics are in Continuum and Materials Mechanics with particular interests in the soft rubberlike and biological materials, active and smart materials, and metamaterials and smart structures.
Damage, healing and residual strains in biological materials: from molecular to macroscopic scale
Dipartimento di Scienze dell'Ingegneria Civile e Ambientale Politecnico di Bari, Bari, Italy
The comprehension of the role of mechanical forces at the molecular level represents nowadays the focus of incredible efforts in many different research fields of Biology, Biomechanics, Material Engineering, Biomedical Sciences. Fundamental phenomena such as DNA and RNA hairpins unfolding and refolding in enzymatic activity, sensing of metabolites, transcription termination and attenuation, morphogenic phenomena, cell motility, focal adhesion and so on cannot be described without a comprehension of mechanical fields effect and macromolecular and cells force-response. Single-molecule stretching experiments opened up the possibility of an impressive progress in in this direction.
We describe in the framework of Statistical Mechanics the experimentally observed mechanical behavior of different protein macromolecules under cyclic stretching experiments. The proposed models reproduce quantitatively known observed phenomena such as stretch induced domains unfolding and refolding, softening and variation of reference configuration. Based on such microscale description, we then propose multiscale approaches to deduce the macroscopic material response depending on few constitutive parameters with a clear physical interpretation.
To analyze the effectiveness of the proposed models we tested the macroscopic cyclic behavior of spider silks and of a keratin proteins such as human, cow and rabbit hairs. By adopting experimental values of the macromolecular material parameters, we show that the model reproduces with surprising accuracy the obtained experimental behavior with a strongly non linear damage, dissipation and large permanent stretches. These results represent in our opinion also a significant step forward in the perspective of the design of new bioinspired history dependent materials.
Dr. Han is a Professor in Mechanical Engineering and the President of Hebei University of Technology, China. His current research interests are mainly in numerical simulation-based engineering, optimization design and reliability analysis. Professor Han has published more than 200 journal articles and 3 monographs. He is the Associate Editor of ASME Journal of Mechanical Design, International Journal of Computational Methods, and Inverse Problems in Science and Engineering. He has received a number of honors and recognitions including continuously being the 2014 to 2018 Most Cited Chinese Researchers in Mechanical Engineering by Elsevier; second prize award of National Science and Technology Progress, first prize award of Natural Science of the Ministry of Education of China, and first prize award of Science and Technology Progress of Hunan Province.
Reliability modeling and error predicting for industrial robots
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, P. R. China
The industrial robot is composed of mechanical structure, drive system, control system and perception system. The analysis of industrial robots involves reliability modeling, reliability design, reliability evaluation and reliability experiment, making the reliability analysis process more complex and difficult than the traditional mechanical products. In order to ensure the high performance of reliability of the robots, the modeling, simulating, predicting as well as the testing of the robots should be systematically investigated. This talk will present our recent research works on reliability modeling and error prediction methods for industrial robots, including: kinematic reliability modeling and analysis, dynamics reliability modeling and analysis, error field modeling and precision predicting. A computational framework of kinematic accuracy reliability analysis is proposed to evaluate comprehensively the positional accuracy reliability for single coordinate, single point, multipoint and trajectory accuracy for industrial robots. The computational accuracy and efficiency are significantly improved compared to the traditional competitive approaches. In terms of dynamics reliability modeling and analysis, a rotational sparse grid method for statistical moment evaluation and dynamics reliability analysis with enhanced accuracy and efficiency is presented. Then, an error predicting method is proposed to evaluate the error values at any position in three-dimensional space stimulated by neural network technique. In this way, off-line precision compensation for industrial robots can be easily realized. Finally, a series of reliability experiments and reliability analysis tools are presented to verify the effectiveness of the proposed methods.