Rhythm Suren Wadhwa

Framework for Implementing Internal Part Traceability in Iron Foundry

Abstract Appropriate description and implementation of internal part traceability in manufacturing is a complex task. Accurate and real- time traceability from a part, or a part feature, to a manufacture, storage, or transport issue is essential to efficient and high- quality operations. With the increasing amount of machine status and product quality information coming from the manufacturing lines, certain questions arise. When there is a problem with the process or product quality what information can be utilized to enable effective traceability to the foundry batch lot? Also, what aggregate information values are needed to enable real-time problem solutions? In this paper a systems-based approach is used to propose a method to define and implement internal part traceability in two participating foundries.

Implementing Continuous Improvement in Metal Casting: Case Study of an Aluminum Foundry

The paper intends to identify and suggest improvements in two areas crucial for implementing lean principles in an aluminum semi-permanent casting small to medium enterprise (SME). This paper builds an analytical model to measure production performance and presents a case study of an aluminum foundry. The case study reveals the use of two lean principles of bottleneck identification and lean buffering could allow for timely response to customer requirements. This paper helps in identifying areas for lean manufacturing tailored to the needs to foundry SMEs, in which the number of published studies is very limited.

Bee Colony Algorithm application for manipulating metalcastings in SME

The paper presents an application of Bee Colony Algorithm (BCA) for template matching that is used for manufacturing automation in a metalcasting foundry. The paper presents a novel application of BCA for robot pick-and-place operation. To enable robust manipulation by a six axis industrial robot manipulator, it is important that the correct orientation of the parts is input to the manipulator. This orientation is input by a vision system which is then used to orient the robot gripper to correctly handle the part. BCA is used for the purpose of robust template matching by the vision system. The BCA algorithm is inspired by the collective behavior of honey bees to find food sources around the hive, which is one of the Swarm Intelligence (SI) techniques. The Normalized cross-correlation (NCC) function is used as an objection function in the BCA optimization procedure. The results of the BCA computer simulations are shown for different number of food sources.

Neural Network Approach to Robot Motion Control

A neural network approach has been proposed & analyzed to control the motion of robot. This involves study of dynamics of a manipulator, which is highly non linear, study & design of neural network to solve the problem. The designed neural network has given the correct output joint angles, joint velocities according to given input torque initial joint angles & joint velocities. Feasibility of the approach is demonstrated through simulation using matlab using the physical parameters of SCARA robot for open loop as well as close loop.