Bulging in incremental sheet forming of cold bonded multi-layered Cu clad sheet: Influence of forming conditions and bending

Bulge is a defect that causes geometrical inaccuracy and premature failure in the innovative incremental sheet forming (ISF) process. This study has two-fold objectives: (1) knowing the bulging behavior of a Cu clad tri-layered steel sheet as a function of forming conditions, and (2) analyzing the bending effect on bulging in an attempt to identify the associated mechanism. A series of ISF tests and bending analysis are performed to realize these objectives. From the cause-effect analysis, it is found that bulge formation in the layered sheet is sensitive to forming conditions in a way that bulging can be minimized utilizing annealed material and performing ISF with larger tool diameter and step size. The bending under tension analysis reveals that the formation of bulge is an outgrowth of bending moment that the forming tool applies on the sheet during ISF. Furthermore, the magnitude of bending moment depending upon the forming conditions varies from 0.046 to 10.24 N·m/m and causes a corresponding change in the mean bulge height from 0.07 to 0.91 mm. The bending moment governs bulging in layered sheet through a linear law. These findings lead to a conclusion that the bulge defect can be overcome by controlling the bending moment and the formula proposed can be helpful in this regards.     

A Constitutive Equation of Thermoelasticity for Solid Materials

A new constitutive equation of thermoelasticity for crystals is presented based on the interatomic potential and solid mechanics at finite temperature. Using the new constitutive equation, the calculations for crystal copper and graphene are carried out under different loading paths at different temperatures. The calculated results are in good agreement with those of the previous thermoelasticity constitutive equation based on quantum mechanics, which clearly indicates that our new constitutive equation of thermoelasticity is correct. A lot of comparisons also show that the present theory is more concise and efficient than the previous thermal stress theory in the practical application.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.