Energy efficient cutting parameter optimization

Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented

A framework for energy monitoring of machining workshops based on IoT

Machining workshop is a widely distributed manufacturing system that consumes massive energy in low efficiency. Due to the complicated and dynamic energy flow of the machining workshop, machinery manufacturers still lack an effective method to monitor and manage the energy efficiency. Hence, this paper proposes an energy efficiency monitoring system for machining workshop with the support of the newly emerging Internet of Things (IoT) technology. With the application of the proposed system, potential opportunities for energy efficiency improvement can be identified. Machinery manufacturers can easily reduce energy consumption and energy cost by managing the machining process

High Edge-Quality Light-Field Salient Object Detection Using Convolutional Neural Network

The detection result of current light-field salient object detection methods suffers from loss of edge details, which significantly limits the performance of subsequent computer vision tasks. To solve this problem, we propose a novel convolutional neural network to accurately detect salient objects, by digging effective edge information from light-field data. In particular, our method is divided into four steps. Firstly, the network extracts multi-level saliency features from light-field data. Secondly, edge features are extracted from low-level saliency features and optimized by ground-truth guidance. Then, to sufficiently leverage high-level saliency features and edge features, the network hierarchically fuses them in a complementary manner. Finally, spatial correlations between different levels of fused features are considered to detect salient objects. Our method can accurately locate salient objects with exquisite edge details, by extracting clear edge information and accurate saliency information and fully fusing them. We conduct extensive evaluations on three widely used benchmark datasets. The experimental results demonstrate the effectiveness of our method, and it is superior to eight state-of-the-art methods

Comparative study of eggshell antibacterial effectivity in precocial and altricial birds using Escherichia coli.

In this study, we compared the antibacterial effectivity of the eggs of six precocial and four altricial bird species using Escherichia coli, based on their eggshell traits. The ultrastructure of eggshell was observed using a scanning electron microscope (SEM). According to SEM results, eggs from precocial birds (chicken, turkey, quail, duck, ostrich, and goose) had cuticle on the eggshells, while eggs from altricial birds (pigeon, budgerigar, munia, and canary) did not. The environment/selection pressure may induce the divergent evolution process in eggs of precocial and altricial birds. The E. coli experiment results showed that chicken, turkey, quail, duck, and goose eggs, with a high cuticle opacity, exhibited a much lower E. coli penetration rate. In contrast, the eggs with poor (ostrich) or without (pigeon, budgerigar, munia, and canary) cuticle exhibited a higher penetration rate. It is suggested that cuticle is a main barrier against bacterial penetration in precocial birds' eggs. Turkey and quail eggs showed the lowest E. coli contamination rate (3.33% and 2.22%, respectively), probably because of the tightly connected nanosphere structure on their cuticle. As for altricial birds' eggs, the eggs of budgerigar, munia, and canary with small pore diameter (0.57 to 1.22 μm) had a lower E. coli penetration rate than pigeon eggs (45.56%, 66.67%, 50%, and 97.78%, respectively, P < 0.05), indicating that pore diameter played a significant role in defending against bacterial trans-shell invasion. We found that eggshell thickness and pore area decreased with egg size. The cuticle quality had no relationship with egg size, but was closely related to the bird species. The E. coli penetration rate of altricial birds' eggs was significantly higher than that of precocial birds' eggs, mainly because the pores are exposed on the eggshell surface and cuticle protection is absent. This study provides detailed information on the eggshell cuticle, which gives insight into the cuticle evolution process that occurred in precocial and altricial bird species. Moreover, the results of E. coli penetration may help understanding the antibacterial behavior in birds

Research on Chinese consumers' shell egg consumption preferences and the egg quality of functional eggs

ABSTRACT: The purpose of this study is to investigate the characteristics of egg consumption in China and the production of functional eggs, and finally enrich the types of shell egg products. Trial 1 explored the influence of egg quality on Chinese consumers' willingness to purchase eggs through a questionnaire, which investigated 1,317 consumers' preferences for egg appearance, factors influencing egg purchase, and purchase of functional eggs. The results showed that about 65% of respondents ate more than 4 eggs per wk, pink eggs were the most popular in China, about 65% of consumers preferred eggs with an egg weight of 48 to 58 grams. For functional eggs, 75.32% of consumers have never heard of them. Preferences for eggshell color and yolk color varied by geographic region, with darker colors preferred in Northeast China. Based on the survey results of functional eggs consumption in Trial 1, the dwarf layers of China Agricultural University were used in Trial 2 to produce functional eggs. The eggs are small and pink in color, which is in line with the preferences of Chinese consumers. Three hundred dwarf layers were divided into 4 groups, using the linseed oil added, marigold extract added, and yeast selenium added diets to produce normal, n-3 fatty acid-enriched, lutein-enriched and selenium-enriched eggs by feeding for 28 d, determined the eggs’ nutrient content and egg quality. The results showed that the n-3 fatty acid, lutein and selenium contents of the eggs of dwarf layers were significantly increased by changing the diets and did not affect the egg weight, eggshell strength, Haugh units or the proportion of egg parts. The results of this study are helpful to understand the trend of egg consumption preferences in China, and on this basis to produce functional eggs that meet the consumers’ expectations