Research Services in Manufacturing

MALTA

Exploiting ICT for the Maltese Manufacturing Industry

Key Experts: Dr Ernest CachiaDr John AbelaDr Ing Saviour Zammit

Researchers: Mr Armand SciberrasMr Joseph Bonello

ICT in ManufacturingICT in Manufacturing

• History of Manufacturing– Assembly line (Henry Ford)– Robots

• Increasingly on the path of innovation– To offer a broadened service

range fulfilling world-wide customer requirements

ICT in ManufacturingICT in Manufacturing

• ICT potential– Fast– Accurate– Reliable– Updateable – Centralised Data– Assets remain in the company

Process 1 – Process 1 – Automated Automated

Optimisation of Optimisation of Production LinesProduction Lines

Importance of ManufacturingImportance of Manufacturing

• The Manufacturing Industry operates in a very competitive market– Requires continuous innovation– Needs to provide added value services

• Lower Costs• Higher Productivity• Faster product turnout• Better planning• Reduce Order-To-Shipping

time

Importance of ManufacturingImportance of Manufacturing

• Efficient Scheduling and Planning are main contributors to cost reduction and efficiency– Helps the company allocate and

prepare resources to meet demand

• ICT can add value by providing low-cost tools that aid companies– Understand the scheduling

problems they face– Provide near-optimal schedules

Scheduling in ManufacturingScheduling in Manufacturing

• Scheduling refers to the problem of finding an efficient sequence of utilisation of human and machine resources

• An efficient schedule reduces costs through optimal allocation of resources

• But it is a very difficult problem– Enumerating all possible schedules takes too

long to be feasible

Scheduling in ManufacturingScheduling in Manufacturing

• In Computer Science, scheduling problems are well known– As examples of computationally complex

problems– As being notoriously difficult to find an

algorithm to solve them efficiently

• Computationally complex problems cannot be solved in a “reasonable” amount of time

Scheduling in ManufacturingScheduling in Manufacturing

• To determine an optimal schedule, one must analyse the performance of all possible solutions of the problem– Enumerating all the possible

solutions takes a very long time

• Also difficult is determining the type of problem one has– Thus one is not sure how difficult

the problem is

Scheduling in ManufacturingScheduling in Manufacturing• Operators have a number of rules

that help them find a schedule• Not always accurate and not

necessarily optimal• The project aims at helping

operators by– Helping them identify the problem– Help them identify the complexity of

the problem– Provide them with tools to generate a

schedule

Scheduling in ManufacturingScheduling in Manufacturing

• The project helps operators model production lines using Graphs

• The graph visually represents the relationship between resources, jobs and operations

• Additional information is captured to allow the application to detect the complexity index of the problem

Scheduling in ManufacturingScheduling in Manufacturing

• The following types of problems can be modelled:– Single Machine Problems: Bottleneck machine

Problems– Parallel Machine Problems: Jobs can be

processed on any of the different machines available

– Shop Problems: Machines are dedicated to a particular operation of a job (Multi-Operation Model)

Scheduling in ManufacturingScheduling in Manufacturing

• The application is able to classify scheduling problems in the following categories:– Polynomially Solvable: These are the “easy”

problems i.e. one can obtain a schedule is a reasonable timeframe

– Pseudo-polynomially Solvable: Problems that are “easy” within certain bounds

– NP-Hard: These are “hard” problems, it is difficult to obtain a schedule for these problems

– Open: problems for which the complexity status is not known

Scheduling in ManufacturingScheduling in Manufacturing

• Schedule generation depends on the type of problem– Easy problems have an efficient algorithm– Hard problems may be approximated using

different techniques• Using heuristic techniques such as Genetic

Algorithms• Using mathematical

solutions such as approximation algorithms

Scheduling in ManufacturingScheduling in Manufacturing

• Generated schedules for hard problems are near-optimal– Finding the optimal schedule is

not possible– Heuristic methods provide a

“good” trade-off between time to generate the schedule and optimality

– Usually better than a human-generated schedule due to computer’s speed in analysing solutions

Scheduling in ManufacturingScheduling in Manufacturing

• A number of schedule generation solutions are being investigated– A generic solver based on Genetic

Algorithms– Solvers based on mathematic

formulations

• The proposed solution has a plugin system aimed at– Extensibility to allow addition

of new algorithms and solvers

Process 2 – Process 2 – Automated QA for Automated QA for

Print Output Print Output using Neural using Neural

NetworksNetworks

Quality Assurance in ManufacturingQuality Assurance in Manufacturing

• Quality is a key factor in the selling price of manufactured goods

• Quality inspection is traditionally done by human beings either:• At the end of the production line on

each and every item removing defective products (slow and expensive)

• Inspecting samples from a produced batch and assessing a batch quality (defective products are left in batch)

MotivationMotivation

• Human inspection:• Not adequate for certain

inspection jobs• Not accurate enough• Slow and expensive• Not consistent and decisions

vary from one inspector to the other

• With recent advances in hardware and development of AI techniques automating the inspection process is more possible

Printing in an industrial setupPrinting in an industrial setup

• Printing is usually carried out using:• Pad printing• Hot-foil stamping• Offset printing• Silk-screen printing

Printing ProblemsPrinting Problems

• All the printing techniques have their own advantages and disadvantages and are prone to particular defects• Changes in temperature• Sensitive to different surfaces• Incorrect ink mixtures• etc.

• Preventing changes to these variables is generally either impossible or very expensive

Common DefectsCommon Defects

• Scratches and cracks

• Missing or extra ink

• Missing or incorrectly placed components on PCBs

• Smudges and misalignment defects

Advantages of an automated systemAdvantages of an automated system

• Reliable 24x7

• Accurate

• Fast

• Consistent

• Cheap

Towards an automated systemTowards an automated system

• The project aims at helping operators by• Guiding them in training and

setting up an automated classifier system for manufactured goods

• Speed up the inspection process of manufactured goods

• Increase the quality of manufactured goods

Automated Optical InspectionAutomated Optical Inspection

Image capturing

Image correction, background

removal, image segmentation

Further image processing and

feature extraction

Data Input to Classifier and other

QA tests

Output from Classifier

Image ProcessingImage Processing• Alignment

• Depending on the setup used, images can be aligned to a template

• Background removal• Unwanted areas from the image can be

removed through masks and region selection

• Captured images can be broken down into regions using intelligent algorithms in order to be treated separately

Artificial Neural NetworksArtificial Neural Networks

• Mathematical models inspired from the structure and functional aspects of biological neural networks

• Made up of an interconnected group of artificial neurons• Based on input, the structure

and connections of the network change accordingly to learn a particular task

• Great for modelling complex relationships between inputs and outputs

The WISARD Neural NetworkThe WISARD Neural Network• At training stages the provided data

is extracted from the image and stored in the nodes

• During live mode, the data is extracted the same way from the image and tested in the nodes

• A similarity percentage is calculated and the object can be discarded if the value is under a specific threshold

Other testsOther tests• Additional QA tests can be set to run in

parallel with the neural network

• Label offset• Checking that a specified label printed on

the product is contained in a specified boundary

• Colour inclusion/exclusion• Checking for the presence of areas

defined by colour and size at specified areas

• Optical Character Recognition• Checking text at specified areas

ClassificationClassification

• All QA tests selected during setup time are executed on the captured image of the product

• All tests can either fail or pass

• The tool determines whether the product should be discarded or not

Thank you for

listening