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This study presents a method of controlling robots based on fuzzy logic to eliminate the effect of uncertainties that are generated by the cutting forces in milling process. The common method to control industrial robots is based on the robot dynamic model and the differential equations of motion to compute the control values. The quantities in the differential equations of the motion of robots are complex and difficult to determine fully and accurately. The interaction forces between the cutting tool and the workpiece are the cutting forces, which are generated during the machining process. It is difficult to calculate the cutting force because it depends on many factors such as material of the machining part, depth of cut, feed rate, etc. This article presents the fuzzy rule system and the selection of the physical value domain of input and output variables of the fuzzy controller. The fuzzy rules are applied in this article to allow us to compute the driving forces based on the errors of input and output signals of the joint positions and velocities, thereby avoiding the calculation of cutting forces. This article shows the simulation results of the fuzzy controller and comparison with the results of the conventional controller when the dynamic model is assumed to be correctly determined. The achieved results are reliable and facilitate the research and application of a fuzzy controller to mechanical processing robots in general and milling machining in particular.

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...

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Nanolubricant mixing the normal lubricant with nanoparticles, gradually becomes a new trend study for metal cutting enhancement. An addition of the nanoparticles improves lubricating properties and convective heat transfer coefficient (cooling properties) of nanolubricants. In the present work, nanolubricant is formulated by using dispersions of 0.3% Al 2 O 3 nanoparticles in normal industrial oil VG46 for enhancement of gear machining performance of SCM420 steel. Comparative study of flank wear, crater wear and gear profile error in gear hobbing with normal oils in the existing production line as well as nanolubricant is studied. This study clearly reveals that tool wear, and gear profile error are reduced by the use of nanolubricant compared to that of normal oils. The paper results not only contribute the deeper understanding of the novel performance of nanoparticles in conventional cutting fluids, but also show a very promising solution to achieve the engineering economy effectiveness in gear machining.

In this paper, the adaptive filtering theory, recently proposed and developed the authors of present work [1-9] for stochastic, encountered in the field of data as simulation in meteorology and oceanography, is reviewed. Several important questions on numerical estimation og the gain matrix, model reduction, structural  choices for the gain, filter stability… are discussed. We show the connections of present approach with a standard Kalman filtering. Adaptive filter is implemented along with a Kalman filtering. Adaptive filter is implemented along with a Kalman filter and standard Newton relation method on the four-layer adiabatic Miami Isopycnical Co-ordinate Ocean Model (MICOM) to produce the estimate for the deep oceanic circulation using assimilate synthetic observations of surface height. Numerical results justify high efficiency of the adaptive filter whose performance is slightly better than that of a Kalman filter due to  impossibility to correctly specify the error statistics in a Kalman filter.

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