Development Of Automatic Quality Control System Using Vision System.

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UNIVERSITY TEKNIKAL MALAYSIA MELAKA

DEVELOPMENT OF AUTOMATIC QUALITY CONTROL

SYSTEM USING VISION SYSTEM

This report submitted in accordance with requirement of the Universiti Teknikal Malaysia Melaka (UTeM) for the Bachelor Degree of Manufacturing Engineering

(Robotics and Automation) with honours

by

CHIN GEE SING

B050710006

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DECLARATION

I hereby, declared this report entitled “Development of Automatic Quality Control System Using Vision System” is the results of my own research except as cited in

references.

Signature :

Author’s Name : CHIN GEE SING

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APPROVAL

This report is submitted to the Faculty of Manufacturing Engineering of UTeM as a partial fulfillment of the requirements for the degree of Bachelor of Manufacturing Engineering (Robotics and Automation) with honours. The member of the supervisor committee is as follow:

……….

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ABSTRAK

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ABSTRACT

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ACKNOWLEDGEMENT

I would like to thank for peoples who guided and contributed in various ways to this PSM I report. This report would not have been possible without the expert advice and suggestion of my PSM supervisor, Dr. Zamberi bin Jamaludin. Thank you Dr. Zamberi for the invaluable information and suggestions that you provided and your willingness for contribute your precious time to this effort. Besides that, I also thanks to laboratory technician, En. Faizul and Pn. Norhafisah for giving me technical support.

Thanks also to the Universiti Teknikal Malaysia Melaka (UTeM) for offering PSM as a compulsory subject for students to gain skills on organizing a project.

Beyond the professional involved in this report, my coursemate and family were also quite influential in this work. Thanks to my coursemate for sharing their precious opinion, experience, comments and resources. Thank you for being there to listen to my gripe about my work. Thanks for my beloved family unending support and motivation.

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DEDICATION

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TABLE OF CONTENT

Abstract Abstrak Dedication

Acknowledgement Table of Content List of Tables List of Figures List Abbreviations

1. INTRODUCTION

1.1 Background 1.2 Problem Statement 1.3 Objectives

1.4 Scope

1.5 Expected Result 1.6 Content

2. LITERATURE REVIEW

2.1 Introduction 2.2 Vision System

2.3 Machine Vision Component 2.3.1 Vision Sensor Types

2.3.1.1 CCD 2.3.1.2 CMOS

2.3.1.3 Comparison between CCD and CMOS 2.3.2 Image Processing Technique

2.3.2.1 Smoothing Technique 2.3.2.1.1 Lowpass Filter

2.3.2.1.2 Local Average 2.3.2.1.3 Gaussian Filter

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2.3.2.1.4 Median Filter

2.3.2.2 Edge Detection Technique 2.3.2.2.1 Prewitt Operator

2.3.2.2.2 Sobel Operator 2.3.2.2.3 Roberts Operator 2.4 Sorting System 2.4.1 Robot

2.4.2 Types of Robot Manipulator 2.5 Conveyor

2.5.1 Traditional Conveyor 2.5.2 Evolution of Conveyor 2.6 Measurement Devices

2.6.1 Analogue-to-Digital Converter 2.6.2 Photoelectric Sensor

2.7 Programmable Logic Controller (PLC) 2.8 Summary

3. METHODOLOGY

3.1 Introduction

3.2 Overall Research Strategies 3.3 Project Structure

3.3.1 Image Processing Procedures

3.4 Characterization of System Components 3.4.1 Conveyor

3.4.2 Rhino Robot Arm 3.4.3 Webcam

3.4.4 Personal Computer 3.4.5 Photoelectric Sensor

3.4.6 Programmable Logic Controller (PLC) 3.5 Summary

4. EXPERIMENTAL SETUP AND PROCEDURES

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4.2 Experimental Setup 4.2.1 Hardware Component 4.2.2 Software Component

4.2.2.1 Logic of Electrical Control System 4.2.2.2 PC Access

4.2.2.3 Graphical User Interface (GUI) 4.2.2.4 Image Processing Configuration 4.2.2.5 Pick and Place Configuration 4.3 Experimental Setup Procedures 4.4 Summary

5. RESULT AND DISCUSSION

5.1 Introduction 5.2 Results

5.2.1 Ladder Diagram

5.2.2 Development of I/O Server

5.2.3 Graphical User Interface Design and Data Binding 5.2.4 Vision Inspection

5.2.5 Sorting 5.3 Discussion

5.3.1 Circuit Board for Relay

5.3.2 Matched Timing and Sequence between Robot Arm and Image Acquisition

5.3.3 Numeric Indicators 5.4 Suggestion

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APPENDICES

A Ladder Diagram

B Rhino Robot Programming C Block Diagram of GUI

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LIST OF TABLES

2.1 Feature and Performance Comparison of CCD and CMOS

2.2 Correction Factor for Direct Reflective Photoelectric Sensor

3.1 Gantt Chart for FYP I 3.2 Gantt Chart for FYP II

4.1 Step for PLC Configuration 4.2 Configuration of PC Access 4.3 GUI Configuration of Labview

4.4 Image Processing Configuration on VBAI

4.5 Rhino Robot Configuration using RoboTalkWindow

5.1 Condition for Combination of Colour and Shape

18

35

53 54

59 61 62 64 66

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LIST OF FIGURES

2.1 Basic Functions of Machine Vision System

2.2 The Coordinate Convention of Pixels That Represent Digital Images

2.3 The Basic Components of a Vision System 2.4 The Example of CCD Image Sensor 2.5(a) CCD Pixel

2.5(b) Architecture

2.6 Basic Principle of The Charge-Coupled Device 2.7 CMOS Sensor is Implemented in Low-End Web-Cam 2.8 CMOS Vision Sensor Floorplan

2.9 Schematic of The RGB Colour Cube 2.10 The Level of Histogram of Gray Scales

2.11 Calculating The Median Value of a Pixel Neighbourhood 2.12 A 3 × 3 Region Mask

2.13 Prewitt Horizontal and Vertical Operators 2.14 Sobel Convolution Kernels

2.15 The Computation Method of a Pixel 2.16 Roberts Cross Convolution Masks 2.17 The Closed-Loop Control System 2.18 Cartesian Robot

2.19 Cylindrical Robot 2.20 Spherical Robot 2.21 Articulated Robot 2.22 Traditional Conveyor

2.23 Steps in Analog-to-Digital Conversion of Continuous Analog Signals from Process

2.24 Thru-Beam Photoelectric Sensor Detection

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2.27 Siemens Programmable Logic Controller, S7-200 2.28 Components of a PLC

3.1 Overall Work Flow Chart

3.2 The Flow Chart Illustrates The Data Collection and Data Study 3.3 The Flow Chart of Proposed System

3.4 Image Processing Flow Chart

3.5 The Schematic Diagram of Integrated System 3.6 A Belt Conveyor

3.7 Rhino Robot Arm 3.8 A Home-Used Webcam 3.9 A Personal Computer 3.10 Photoelectric Sensor 3.11 Siemens S7-200 PLC

4.1 CAD Drawing for Webcam Attachment on Conveyer 4.2 Actual Frame Mounting on Conveyor

4.3 Designed Logic Circuit Board 4.4 Overall Experimental Setup

5.1 Actual Sorting Result That is Performed by Rhino Robot 5.2 Designed Ladder Diagram

5.3 Develops and I/O Server Module using PC Access 5.4 GUI of The Integrated System

5.5 Select The Source Item in Data Binding 5.6 State Program for Vision Inspection 5.7 Inspection Step for State: Circle Red: x1

5.8 Relay Circuit Board Used to Triggered PLC and Motor of Conveyor

5.9 Relay Circuit Board Used to Trigger Robot from PLC Input Signal

5.10 Numeric Indicators for Accept and Reject Counting

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A1 Ladder Diagram A2 Ladder Diagram A3 Ladder Diagram A4 Ladder Diagram A5 Ladder Diagram

C1 Block Diagram for GUI

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LIST OF ABBREVIATIONS

ADC - Analogue-to-Digital Converter APS - Active Pixels

CAPS - Complementary Active Pixels Sensors CCD - Charged-Couple Device

CCTV - Close Circuit Television CID - Charge-Injection Devices

CMOS - Complementary Metal-Oxide Semiconductor DOF - Degree of Freedom

GUI - Graphical User Interface I/O - Input/Output

LED - Light Emitter Diode

OLE - Object Linking and Embedding OPC - OLE for Process Control PC - Personal Computer PCB - Printed Circuit Board PIC - Person-in-charge

PLC - Programmable Logic Controller PMOS - P-type metal-oxide-semiconductor PPP - Prismatic, Prismatic, Prismatic PPS - Passive Pixels

PVC - Polyvinyl Chloride RGB - Red, Green, Blue

RPP - Rotary, Prismatic, Prismatic ROI - Region of Interest

RRP - Rotary, Rotary, Prismatic RRR - Rotary, Rotary, Rotary USB - Universal Serial Bus

VBAI - Vision Builder Automated Inspection VDC - Voltage Direct Current

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CHAPTER 1

INTRODUCTION

1.1 Background

The essential goals in the implementation of automation system in the industries are to provide improvement in human operator variances and operational efficiency. It is known in that many cases, product quality consistency can vary depending on the characteristic of human operators who are on duty. Hence, automation system should be provided to improve this variance. The problem associated with human-assisted operation is characterized by cycle time, repeatability and material handling. Although operational efficiency has been given a top priority, human operators are still exposed to high probability of injuries, long-term similar working scope in fast motion, applications naked eyes inspection-based system and insufficient break time.

In order to improve operational efficiency and to improve safety of human operators, there is a need for efficient, high repeatability and cost effective human-assisted operation equipment. Mechanized and automized solution that are properly sized with suitable modularized structure can greatly improve the safety of personnel as well as efficiency and quality of production. In addition, such efficient automation system with high repeatability can greatly improve the operational efficiency, product quality and product consistency when applied in combination with intelligent instrumentation and advanced control.

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enlarge the approaching region and various orientation. Therefore, it is a wise solution to be used to substitute the folklore operation system like human operator in industries by robot arm. Besides, the evolution of conveyor from manual operate to automated conveyor system has enhanced the product handling and increase the cycle time as well. The implementation of vision system for inspection in production line becomes a booster for inspection compare to old-fashion naked eye inspection. The mechanized and automized systems are integrated with intelligent vision system enable the whole production processes execute in fully automatic. Thus, the operational efficiency is increase effectively and greatly decreases the variances of product quality.

1.2 Problem Statement

Nowadays, human-operator based industries require longer time to inspect using naked eye and sort the products into the correct slot or container. This action of inspection and sorting consume more time and results in the operators unable to meet the cycle time. Low repeatability is one the effects of long working time on similar, repeating working scope at fast motion. Therefore, the operators may get tired and bored resulting in bad inspection and sorting quality of the products. Due to the limitations, an automated inspection system based on a vision system integrated into a pick-and-place robot is required.

1.3 Objectives

Based on the problem statements stated above, several objectives are to be obtained at the end of this project. The objectives of this project are:

i. To design visual-based inspection system

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1.4 Scope

This project will focus on vision-based inspection and products sorting. The scopes of this project are:

i. Image acquisition using a webcam.

ii. Configure vision inspection algorithm based on Vision Builder Automated Inspection (VBAI).

iii. Validate vision inspection system by interfacing a pick-and-place robot, a webcam, a personal computer, a programmable logic controller (PLC), and a conveyor.

iv. Classify product in term of different colours and shapes.

The fabrication of conveyor, robot arm and development of software will not be covered in this project.

1.5 Expected Result

This project is expected to improve the inspection process in terms of colour sorting and shape orientation for packaging purpose. The product will be conveyed and inspected for its colour and differentiated in term of shape using image processing technique. A robot then removes the product and places it in a correct slot at right orientation.

1.6 Content

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CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter discusses the literature review related to the proposed vision-based inspection system. Review of previously developed systems in literature review are discussed and illustrated. This chapter discusses mainly the vision system, sorting system, conveyor and sensors that are relevant to the development of the proposed system.

2.2 Vision System

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Figure 2.1: Basic functions of machine vision system (Groover, 2000).