Study on mechanical and energy characteristics of coal samples under different unloading states

There are many types of coal seams in China, and the mining of protective layers will cause different rates of stress reduction in protected coal seams at different intervals. Therefore, experiments were conducted at different unloading rates to explore the strength, deformation, and energy characteristics of coal. Research findings: the AE (acoustic emission) signal of the coal body before unloading has a small range of changes and similar characteristics. After unloading begins, because of the different development rates of internal crack in the coal body under different unloading states, the AE signal of the coal body varies at different unloading rates. The maximum stress increases exponentially with the increase of unloading rate. It was found that the higher the unloading rate, the easier and earlier the coal sample is to be damaged. And it was discovered that the dissipated energy of the coal sample in the elastic stage is extremely low, and a large amount of total energy is converted into elastic energy and stored inside the coal sample. The dissipation energy increases during the plastic stage, while the trend of increasing elastic energy slows down. After the peak stage, the dissipated energy rapidly increases and the elastic energy decreases

Fabrication of anti-adhesion surfaces on aluminium substrates of rubber plastic moulds using electrolysis plasma treatment

An anti-adhesion surface with a water contact angle of 167° was fabricated on aluminium samples of rubber plastic moulds by electrolysis plasma treatment using mixed electrolytes of C6H5O7(NH4)3 and Na2SO4, followed by fluorination. To optimise the fabrication conditions, several important processing parameters such as the discharge voltage, discharge time, concentrations of supporting electrolyte and stearic acid ethanol solution were examined systematically. Using scanning electron microscopy (SEM) to analyse surfaces morphology, micrometer scale pits, and protrusions were found on the surface, with numerous nanometer mastoids contained in the protrusions. These binary micro/nano-scale structures, which are similar to the micro-structures of soil-burrowing animals, play a critical role in achieving low adhesion properties. Otherwise, the anti-adhesion behaviours of the resulting samples were analysed by the atomic force microscope (AFM), Fourier-transform infrared spectrophotometer (FTIR), electrons probe micro-analyzer (EPMA), optical contact angle meter, digital Vickers microhardness (Hv) tester, and electronic universal testing. The results show that the electrolysis plasma treatment does not require complex processing parameters, using a simple device, and is an environment-friendly and effective method. Under the optimised conditions, the contact angle (CA) for the modified anti-adhesion surface is up to 167°, the sliding angle (SA) is less than 2°, roughness of the sample surface is only 0.409μm. Moreover, the adhesion force and Hv are 0. 9KN and 385, respectively

MULTI-OBJECTIVE OPTIMIZATION STRATEGY BASED ON ENTROPY WEIGHT, GREY CORRELATION THEORY, AND RESPONSE SURFACE METHOD IN TURNING

Machining with low energy consumption, high efficiency, and processing quality has become the preferred processing strategy for manufacturing enterprises. An effective multi-objective optimization method can help to formulate a good machining strategy. Gray correlation analysis is a powerful tool for formulating a multi-objective optimization strategy, and the parameters combination can be evaluated by the grey correlation degree. In multi-objective optimization problems, the weights assignment techniques are a key factor for making a decision. However, the conventional grey correlation analysis method ignores the weight of each optimization objective. Besides, the grey correlation analysis can only be carried out in the range of training samples, which will lead to inaccurate optimization results. Therefore, this paper develops a multi-objective optimization strategy based on entropy weight, grey correlation theory, and response surface method to improve the original defects and formulate the best machining strategy in turn. Firstly, in the orthogonal experiments of turning AISI 1045 steel with three factors and five levels, the machine tool-specific energy, surface roughness, and processing efficiency are taken as optimization objectives. Then the weight of optimization objectives is determined by entropy theory. Finally, the response surface method is introduced to establish the grey correlation degree prediction model according to the entropy results. Based on the particle swarm optimization algorithm, the optimum processing strategy is obtained without the limitation of training samples. The results illustrate that cutting depth is the most significant factor affecting grey correlation degree, and the grey correlation degree increases by 14.01% on the initial basis. The research establishes a mathematical model with grey correlation degree as the objective function, which provides a new idea for formulating a multi-objective optimization strategy

Multi-Objective Optimization of Cutting Parameters in Turning AISI 304 Austenitic Stainless Steel

Energy conservation and emission reduction is an essential consideration in sustainable manufacturing. However, the traditional optimization of cutting parameters mostly focuses on machining cost, surface quality, and cutting force, ignoring the influence of cutting parameters on energy consumption in cutting process. This paper presents a multi-objective optimization method of cutting parameters based on grey relational analysis and response surface methodology (RSM), which is applied to turn AISI 304 austenitic stainless steel in order to improve cutting quality and production rate while reducing energy consumption. Firstly, Taguchi method was used to design the turning experiments. Secondly, the multi-objective optimization problem was converted into a simple objective optimization problem through grey relational analysis. Finally, the regression model based on RSM for grey relational grade was developed and the optimal combination of turning parameters (ap = 2.2 mm, f = 0.15 mm/rev, and v = 90 m/s) was determined. Compared with the initial turning parameters, surface roughness (Ra) decreases 66.90%, material removal rate (MRR) increases 8.82%, and specific energy consumption (SEC) simultaneously decreases 81.46%. As such, the proposed optimization method realizes the trade-offs between cutting quality, production rate and energy consumption, and may provide useful guides on turning parameters formulation

Preliminary Investigations of an Opposed Rotary Piston Compressor for the Air Feeding of a Polymer Electrolyte Membrane Fuel Cell System

Automotive polymer electrolyte membrane fuel cell systems are attracting much attention, driven by the requirements of low automotive exhaust emissions and energy consumption. A polymer electrolyte membrane fuel cell system provides opportunities for the developments in different types of air compressors. This paper proposed an opposed rotary piston compressor, which had the merits of more compact structures, less movement components, and a high pressure ratio, meeting the requirements of polymer electrolyte membrane fuel cell systems. Preliminary performance evaluations of the opposed rotary piston compressor were conducted under various scenarios. This will make a foundation for optimizations of outlet pipe layouts of the compressor. A three-dimensional numerical simulation approach was used; further, in-cylinder pressure evolutions, fluid mass flow rates, and P–V diagrams were analyzed. It indicated that the cyclic period of the opposed rotary piston compressor was half of reciprocating piston compressors. The specific mass flow rate of the compressor is in the range of 0.094–0.113 kg·(s·L)−1 for the given scenarios. Outlet ports 1 and 2 dominated the mass flow in the discharge process under scenarios 1, 3, and 4. In-cylinder pressure profiles show multipeaks for all of these scenarios. In-cylinder pressure increased rapidly in the compression process and part of the discharge process, which led to high energy consumption and low adiabatic efficiency. The maximum adiabatic efficiency is approximately 43.96% among the given scenarios

Investigation of Adhesive Resistance of Aluminum Alloy by Sandblasting and Electrochemical Machining

A novel method for fabricating an adhesive resistance surface is presented. Sandblasting and electrochemical machining were introduced to prepare micro-nano structures on the sample surface. Then, the prepared sample was immersed in a tridecafluoroctyltriethoxysilane ethanol solvent. The surface of the aluminum alloy sample roughened and covered with low-surface-energy chemical groups was examined by scanning electron microscope (SEM) and atomic force microscope (AFM). Surface wettability and adhesive resistance of the treated sample were characterized by water contact angles, area fraction, sliding angle and solid surface energy. Furthermore, the effects of some process parameters, such as sand size, current density, electrochemical machining time, and electrolyte concentration, on the contact angle, area fraction, sliding angle and the solid surface-energy of the modified sample surfaces were provided. The results show that the combination of binary micro-structures and surface modification of tridecafluoroctyltriethoxysilane plays a role to improve adhesive resistance of the aluminum alloy surface