OPTIMIZATION OF EDM SMALL HOLE DRILLING PROCESS USING TAGUCHI APPROACH

ABSTRACT Electrical Discharge Machining (EDM) is a process used to remove or cut a material into desired shape through the action of spark discharge between the tool and work piece. The objective of this paper is to optimize the independent variables to achieve better accuracy in EDM small hole drilling by using Taguchi method. The L9 orthogonal array is employed to study the performance characteristics in drilling operations of mild steel (AS3679) as workpiece by using 1 mm copper (Cu) pipe electrode. Three drilling parameters namely, pulse off time, peak current and servo standard voltage are considered to optimize drilling hole diameter. The result concluded that use of greater pulse off time, greater peak current and medium servo standard voltage give the better hole diameter for the specific test range. Further study in this topic could consider different factor such as pulse on time, material removal rate (MRR) and coolants to investigate how these factors would affect hole diameter

Parameters of effects in decision making of automotive assembly line using the Analytical Hierarchy Process method

The automotive industry contributes high income to most of the countries. The assembly line is an essential part of the automotive industry because it combines all the components into a complete body unit. Assembly lines often experience delays in meeting production targets, requiring overtime to complete. Musculoskeletal Disorder (MSD) complaints among assembly workers predominantly lie in trimming, chassis, and finishing processes. Improvements are needed to reduce complaints according to the priority process. This study aims to prioritize the process on the assembly line with the parameters of work position, workload, work layout and equipment. This study implements the Analytical Hierarchy Process (AHP) method to achieve the objectives of the decision-making process. Preliminary weighting priorities for chassis, finishing and trimming are 0.6153, 0.2313 and 0.1533; respectively highest weight is in the chassis process and will be a priority for this study in optimizing solutions

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms