Due to great effect of mining operation on environment and dependent sides, paying attention to the aspects of sustainable development (SD) is important. A conjugation of Grey theory and Decision-Making Trial and Evaluation Laboratory (DEMATEL) is able to find out cause and effect relations among the triple mining SD components and their effective factors. Grey-DEMATEL approach has been used in order to effectively quantify indicators of sustainable development of a copper mine located in south east Iran. This systematic approach transformed the quantitative and qualitative information into a analogous scale and measured the interrelationships among the SD components and their factors. Hierarchical Grey-based DEMATEL compensated the incomplete and uncertain environmental and socio-economic information. The obtained results indicated that social component with an R value of 10 is relatively strong direct influencer on the other components and the economic component is the least direct influencer with R= 8.49. Among the social impacting factors, employment of local work forces is the most important factor that needs to be consider with sustainable development objectives.

In this paper, the development of controlling a six Degree of Freedom (DOF) Lower Limb Exoskeleton (LLE) model using the Robot Operating System (ROS) is presented. Moreover, this work proposes a method to analyze kinematic properties and control of the LLE before the prototype. The model of the LLE is described using Extensible Markup Language (XML) programming in the Unified Robot Description Format (URDF). The dynamic equation of the six-DoF LLE is determined by using Newton-Euler. In addition, a Proposition-Integral-Derivative (PID) controller is established in a feedback closed-loop control system. The PID controller is tuned via Ziegler-Nichols (Z-N). The tuned PID controller is tested in the Gazebo environment to confirm the performance of the proposed method. The nodes and topics flow chart of the programmed 3-D model of the LLE is described. Furthermore, a desired angular trajectory based on the phase on walking is defined for each joint of the LLE. The result shows that the actual pursue the desired angular trajectory for each joint. The average and maximum error of the angular trajectories for all the joints are less than 0.05 radian. It can be ascertained that our developed LLE model in the Gazebo simulator can be used for giving an overview of the walking pattern.

Computer numerical control (CNC) machines have been widely used in automotive manufacturing industries especially of machining operation in automotive part such as engine body and cylinder. One of the key features that improve efficiency of CNC machining is through the optimization of tool path. Previous researcher to optimize tool path has premeditated several approaches. This paper aims to provide a critical review of those approaches that have been developed in tool path. The developed tool path approaches covered different types of machining process under various constraints condition. This paper focuses on tool path generation in CNC machining such as milling and cutting process. Based on our finding, this review paper collects information on tool path optimization and recommends future research direction.

Today, in most of metal machining process, Computer Numerical Control (CNC) machine tools have been very popular due to their efficiencies and repeatability to achieve high accuracy positioning. One of the factors that govern the productivity is the tool path travel during cutting a work piece. It has been proved that determination of optimal cutting parameters can enhance the machining results to reach high efficiency and minimum the machining cost. In various publication and articles, scientist and researchers adapted several Artificial Intelligence (AI) methods or hybrid method for tool path optimization such as Genetic Algorithms (GA), Artificial Neural Network (ANN), Artificial Immune Systems (AIS), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). This study presents a review of researches in tool path optimization with different types of AI methods that show the capability of using different types of optimization methods in CNC machining process.

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