Fully-Automated Liquid Penetrant Inspection Line Simulation Model for Increasing Productivity

Abstract

By proposing an optimization model for a new automated liquid penetrant inspection (LPI) system in order to increase its productivity, the paper tries to identify the best algorithm to solve this case study. The architecture of the system is dictated by the successive stages of the inspection process and the available conditions in the work shop. As a novelty in the field, the authors developed such a fully automated LPI system for inspecting different parts, which eliminates the need of the visual inspection made by operator, using instead dedicated software solution for processing the digital images of the inspected parts and for giving the pass/fail verdict. In the present case study, the attention was focused on optimizing the new LPI system architecture. Simulations in different working scenarios are run with the purpose to increase productivity by optimizing the critical waiting times within the system and by establishing the best order for inspecting parts belonging to three families subjected to LPI method. Moreover, the results of the simulation are used for programming the system by setting the optimal values of the functional parameters of system's equipment in order to avoid running a large number of tests which are expensive and time consuming.

The Role of Visual Inspection in the 21st Century

“…Semi-automated processes for liquid penetrant testing have been patented for some high-throughput operations (Mendoza, 1973;Vetterlein, Wagener, Rongen, & Sampson, 2006); however, the human still has a role in visual inspection for this NDT method. Some fully automated methods for liquid penetrant inspection have been proposed but have not yet been implemented (Popescu, Anania, Cotet, & Amza, 2013). Thus, the human continues to have a vital role in visual inspection for low-throughput highconsequence operations such as those at the Pantex Plant.…”

Using IoT to Dynamically Test Smart Connected Devices

“…As most of the IoT platforms comes with analytics and machine learning, it is easier than ever to implement fast, different solutions that replace human interaction with the products and lower the processes time, eliminates any human error and that is scalable for any future products. Various types of data are available in manufacturing systems or waste management systems, and should be properly collected, encoded and transmitted to the cloud application [1,2,3,4].…”

Modeling Processing Cell Architecture by Material Flow Simulation

“…On the other hand, the productivity represents a performance indicator expressed as a function to be maximized. One of the most used optimization options in Material Flow Management (MFM) is based on the identification of the location where the material flow is slowed or blocked (bottlenecks) and on finding solutions to eliminate them with or without modifying the system architecture [2].…”