A rehabilitation robot is a device that has been proving its positive effectiveness in the process of helping patients recover quickly after a stroke. Researching, designing, and manufacturing robot models in general and upper limb rehabilitation robots in particular are very practical. In this study, we proposed to combine the use of an algorithm and a physical modeling method to shorten the calculation process and design an upper limb rehabilitation robot. First, an exoskeleton upper limb rehabilitation robot model (UExosVN) was briefly described. Next, in turn, all the important problems including inverse kinematics, inverse dynamics for this robot model were proposed and solved by using optimization algorithms and physical modeling methods. The model was evaluated in the critical movement of daily operations. The results after the testing process have proven the accuracy and effectiveness of the proposed methods.
Computer aided process planning (CAPP) is an important bridge between computer aided design (CAD) and computer aided manufacturing (CAM) in computer integrated manufacturing environment. Operation sequence generation is one of the most difficult tasks in CAPP. The aim of operation sequencing in CAPP is to determine the best order of machining operations with minimal manufacturing cost while satisfying all the precedence constraints. This paper presents a proposed method for optimizing operation sequence using modified clustering algorithm. The key concept of method is that the precedence constraints are firstly checked for selecting all possible next operations of the last operation in the sequence and their traveling costs are compared to choose the optimal feasible operation which has the minimum traveling cost in the sequence. Then, all operation sequences are calculated the total traveling cost for obtaining the optimal sequence result. Because of removing all unfeasible sequences at the beginning of procedure and selecting the optimal operation into sequence in each step, the time can be significantly reduced. The capability and performance of the proposed method are demonstrated in three specific case studies. The comparisons show that the proposed method can solve the problem in much lesser computational time while generating more alternate optimal feasible sequences than previous algorithms. Phung, Tran, Hoang and Truong, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol.11, No.1 (2017) cutting tools, and set-up allows reducing the machining cost while the machine changes, set-up changes, and cutting tool changes related to traveling cost. The selection of manufacturing resources is based on the machine, setup or cutting tool cost. The optimal selection should have the minimum manufacturing resource cost while ensuring machining technology. Many researchers have approached the problem of minimizing the traveling cost to obtain the optimal operation sequences based on all selected manufacturing resources. To solve this issue, several researchers proposed various methods using artificial intelligence algorithms (Roman Stryczek 2007). Bhaskara Reddy SV et al. (1999) applied genetic algorithm to obtain the optimal operation sequence. It is based on consideration of traveling cost and precedence constraints. Jaber Abu Qudeiri et al. (2007) found the efficient sequence of operations located in asymmetrical locations and different levels to achieve the shortest cutting tool travel path based on genetic algorithm. It is effectively demonstrated by the application of finding operation sequence in hole making series in different levels. JinFeng Wang et al. (2011) developed a modified genetic algorithm for process planning optimization. The natural number composing of five decimal codes was adopted in coding strategy. Krishna AG and Rao KM (2006) presented an operation sequence optimization method based on ant colony algorithm. G. Nallakumarasamy et al. (2011) developed an algori...
The experiments of the surface grinding of Ti-6Al-4V grade 5 alloy (Ti-64) with a resin-bonded cubic Boron Nitride (cBN) grinding wheel are performed in this research to estimate the influence of cutting parameters named workpiece infeed speed, Depth of Cut (DOC), cooling condition on the grinding force, force ratio, and specific energy. A finite element simulation model of single-grain grinding of Ti-64 is also implemented in order to predict the values of grinding forces and temperature. The experimental results show that an increase of workpiece infeed speed creates higher intensified cutting forces than the DOC. The grinding experiments under wet conditions present slightly lower tangential forces, force ratio, and specific energy than those in dry grinding. The simulation outcomes exhibit that the relative deviation of simulated and experimental forces is in the range of 1-15%. The increase in feed rate considerably reduces grinding temperature, while enhancement of DOC elevates the heat generation in the cutting zone.
This investigation deals with the rate of heat transferred to the workpiece and the heat removed from the workpiece in cool air grinding with a porous metal bonded diamond wheel (PMBDW). The following conclusions are obtained from the grinding tests: Blowing cool air onto the workpiece causes its temperature to decrease before grinding, leading to a low grinding surface temperature. Heat is removed from the grinding zone with the presence of cool air, so that the rate of heat transfer to the workpiece decreases. The amount of heat removed by cool air increases with the increase in the wheel pore rate, the increase of the wheel speed and the decrease of the table speed.
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