Upload
others
View
11
Download
0
Embed Size (px)
Citation preview
Automated Product Quality Inspection Submitted by
Robert Bosch Engineering and Business Solutions Limited
Presenters : Prabhakaran Sengodan, Utkarsh Siddu
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.2
1• Introduction
2• Current pain areas
3• Conventional Approach
4• Drawback: Conventional Approach
5• Our Approach
6• Application architecture and technology stack
7• Key features & Benefits
8• Why MATLAB ?
Agenda
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.3
Even in this machine era, manual inspection of products (products like sea food,
grains, products at end of line etc.) in processing industries is widely practiced
Large variance in appearance within a class and small inter class variance make
the automation of visual quality inspection complex, thereby demanding manual
inspection
Some of the consequences that the industries face in case of compromised
quality inspection are :
• Negative impact on the brand value which leads to loss of business
• Incur high costs in case of product recall which are delivered to
market
Introduction
Current pain
areas
Replacing a skilled
inspector has implications on
the cost and quality of inspection
Accuracy and repeatability of
manual inspection is very less due to fatigue and
boredom
Manual quality inspection of products is
subjective in nature
Huge effort & time involved during manual
inspection
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.4
Current Pain Areas
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.5
For automation of quality inspection, manual analysis of the subject to be
inspected will be performed to understand what are the key features that need to
be extracted - colour, texture, frequency etc.,- for efficient representation of
characteristics of the subject
The selected features must represent information that is key for solving the
business case
The selected features should not have high correlation
Once the features are finalized, suitable pre-configured thresholds are applied on
the extracted features for making decision on the quality
In some approaches a Machine Learning (ML) classifier/model will be trained on
the features extracted from the labelled training dataset
Conventional Approach
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.6
For a complex inspection task, selection of suitable features is an exhaustive
process which may take up weeks or months of design and development effort
Resulting features will be in the order of hundreds, making it virtually impossible to
have pre-configured thresholds
For a new Inspection requirement feature extraction process may require rework
Drawback: Conventional Approach
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.7
To overcome the drawbacks of conventional approach Neural Network based
Machine Learning is well suited
State of the art, Convolutional Neural Networks (CNNs) are a special form of
neural networks designed to exploit local correlation present in data such as
images, speech data, sensors data or any time series data
The task of feature extraction can be automated in CNNs with multiple hidden
convolutional layers. In other words, CNNs can be trained on raw data without any
feature extraction
Rich feature extraction is performed by the neurons in the hidden convolutional
layers of CNN
Convolutional neural networks with large number of neurons/parameters can
learn/model complex functions
Our Approach
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.8
As the defect can vary in appearance, we employ convolutional layer for surface
analysis
For extraction of dimensional data we segment the object and extract the features
like length, width, etc
With deep architectures and sufficient amount of training data CNNs generalize
well for the given application; resulting in increased accuracy for unseen samples
Our Approach[contd…]
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.9
Proposed solution
PreprocessingConvolutional Neural
Network Layers
Images
Trained Dense Neural
Network Layers
Generic Feature
ExtractionSegmentation
Image Labels
Dense
Neural
Network
Layers
Neural Network Model Training
Trained Convolutional
Neural Network Layer
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.10
Proposed solution[contd..]
Classification by Trained Neural Network Model
Reports
Preprocessing
Images
Trained
Dense
Neural
Network
layersGeneric Feature
ExtractionSegmentation
Report
Generation
Trained Convolutional
Neural Network Layer
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.11
Application architecture and technology stack
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.12
High accuracy results as both CNN and classical approaches are combined
Easy configuration of system for newer variant or product with minimal cost and time overhead
Reduces dependency on human expertise and error due to human negligence
With regular user feedback, system can be designed to increase its accuracy over the period of time
Interfaces for integration with vision system and control system
The trained neural network model can be easily ported to any hardware platform that support floating point computations
Key features & Benefits
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.13
Why MATLAB
Provides all basic tools needed for
development of scientific
computing application
Supports image acquisition from wide range of cameras and standards like GiGE Vision, Camera Link
Efficient off the shelf algorithms
for Image Processing and
Machine Learning
Features Apps that can be
used for quick analysis by developers
Database integration tools
and Deployment
tools which are vital for
application development
RBEI/ETC1 | 4/18/2017
Why MATLAB ?
RBEI/ETC1 | 4/18/2017
© Robert Bosch Engineering and Business Solutions Private Limited 2017. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.14