Johnson-Cook parameter identification from machining simulations using an inverse method

The Johnson-Cook model is a material model which has been widely used for simulating the chip formation processes. It is a simple 5 parameter material model which predicts the flow stress at large strains, strain-rates and at high temperatures. These parameters are usually identified by determining the flow stress curves experimentally, and then using curve fitting techniques to find the optimal parameters to describe the material behaviour. However the state-of-the-art experimental methods can only rely on data obtained from strains of up to 50% and strain-rates of the order of 103 per second, whereas in machining processes strains of more than 200% are reached at strain-rates of the order of 106 or more. Therefore, the parameters obtained at much milder conditions have limited applicability when simulating machining. In this paper an inverse method of material parameter identification from machining simulations is described. It is shown that by using the observables of a machining process such as the chip shape and cutting forces, the underlying material parameters can be identified. In order to achieve this, a finite element model of the machining process is created and simulation is carried out using a known standard parameter set from literature. The objective of the inverse method is to reidentify this set by using the chip shape and cutting forces. An error function is created using the non-overlap area of the chip shapes and the difference in the cutting forces. The Levenberg-Marquardt algorithm is used to minimise the error function. It has been shown before that multiple sets of Johnson-Cook parameter sets exist which might give rise to indistinguishable chip shapes and cutting forces. In order to identify the parameter set uniquely, simulations are performed at widely varying cutting conditions such as differing rake angles, cutting speeds and non-adiabatic conditions. Thus, material parameters which represent the material behaviour over a wide range can be identified

Constructive Use of Errors in Teaching the UML Class Diagram in an IS Engineering Course

A class diagram is one of the most important diagrams of Unified Modeling Language (UML) and can be used for modeling the static structure of a software system. Learning from errors is a teaching approach based on the assumption that errors can promote learning. We applied a constructive approach of using errors in designing a UML class diagram in order to (a) categorize the students’ errors when they design a class diagram from a text scenario that describes a specific organization and (b) determine whether the learning-from-errors approach enables students to produce more accurate and correct diagrams. The research was conducted with college students (N = 45) studying for their bachelor’s degree in engineering. The approach is presented, and the learning-from-errors activity is illustrated. We present the students’ errors in designing the class diagram before and after the activity, together with the students’ opinions about applying the new approach in their course. Twenty errors in fundamental components of the class diagram design were observed. The students erred less after the activity of learning from errors. The displayed results show the relevance and potential of embedding our approach in teaching. Furthermore, the students viewed the learning-from-errors activity favorably. Thus, one of the benefits of our developed activity is increased student motivation. In light of the improved performance of the task, and the students’ responses to the learning-from-errors approach, we recommend that information systems teachers use similar activities in different fields and on various topics

Operation Strategy for a Low-Cost Easy-Operation Cassino Hexapod

This paper presents operation strategies for a hexapod walking machine that has been designed and built at the Laboratory of Robotics and Mechatronics (LARM) at the University of Cassino. Special care has been addressed in proposing and describing a suitable mechanical design and architecture that can be easily operated by a PLC with on–off logic. Experimental tests are reported in order to show feasibility and operational capability of the proposed design

Reconstruction of Self-Sparse 2D NMR Spectra from Undersampled Data in the Indirect Dimension†

Reducing the acquisition time for two-dimensional nuclear magnetic resonance (2D NMR) spectra is important. One way to achieve this goal is reducing the acquired data. In this paper, within the framework of compressed sensing, we proposed to undersample the data in the indirect dimension for a type of self-sparse 2D NMR spectra, that is, only a few meaningful spectral peaks occupy partial locations, while the rest of locations have very small or even no peaks. The spectrum is reconstructed by enforcing its sparsity in an identity matrix domain with ℓp (p = 0.5) norm optimization algorithm. Both theoretical analysis and simulation results show that the proposed method can reduce the reconstruction errors compared with the wavelet-based ℓ1 norm optimization

A multidisciplinary consensus on the morphological and functional responses to immunotherapy treatment

The implementation of immunotherapy has radically changed the treatment of oncological patients. Currently, immunotherapy is indicated in the treatment of patients with head and neck tumors, melanoma, lung cancer, bladder tumors, colon cancer, cervical cancer, breast cancer, Merkel cell carcinoma, liver cancer, leukemia and lymphomas. However, its efficacy is restricted to a limited number of cases. The challenge is, therefore, to identify which subset of patients would benefit from immunotherapy. To this end, the establishment of immunotherapy response criteria and predictive and prognostic biomarkers is of paramount interest. In this report, a group of experts of the Spanish Society of Medical Oncology (SEOM), the Spanish Society of Medical Radiology (SERAM), and Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM) provide an up-to-date review and a consensus guide on these issues