Download Intelligent Manufacturing Applications at Ford Motor Company

Working with Natural
Language Text: Tools and
Techniques
Nestor Rychtyckyj
Advanced & Manufacturing
Engineering Systems
Ford Motor Company
1
Agenda
• Introduction
• Description of problem– Why is language
so important?
• Dealing with Natural Language Text
• Application Examples
• Machine Translation
• Future Directions
• Conclusions
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Natural Language Text is
“everywhere”
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Internet
Web sites
Blogs
Customer Feedback
Dealer Feedback
Lessons Learned
Corporate Knowledge
Warranty Claims
Internal documentation
Spoken Dialog systems
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Dealing With Text Information
• Search Engines (Google, askjeeves.com)
• Excel
• Commercial Text Mining Tools (Wordstat, SAS
Text Miner, SMART Text Miner, etc)
• Open Source tools (Wordnet, Senseclusters,
etc.)
• Controlled Languages
• Ontologies
• Natural Language Processing
• Semantic Web
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Present Status
• Mostly key-word based
• Very little intelligence, no background knowledge
or context
• Limited natural language dialog interpretation
• Most of the processing is left to the human user
• Difficult to build computer systems that can
retrieve information in an “intelligent” manner
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Future State
• Semantic Web – information on the web is
organized using structured tagging based on
XML, RDF, OWL, SWRL
• machine-processable data on the web
• standard interface to data
• rich knowledge representations through
ontologies
• Allows for the development of systems that cab
retrieve information in an intelligent manner
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Semantic Web Architecture
Source: Tim Berners-Lee, 2000
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Artificial Intelligence (AI)
• Study on how to build human-level intelligence into
computer applications
• Uses learning, representation of human knowledge,
understanding of language, vision, speech, etc.
• Applies the built-in knowledge using inference and
reasoning
• Been very successful in limited problem domains – less
so for general applications
• Integrated into many applications areas including
manufacturing, planning, search, speech recognition,
financial analysis, games, customer analysis,
commercial fishing, etc.
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Current use of AI in Manufacturing
at Ford
• AI applications for manufacturing
• Bring appropriate knowledge about
manufacturing to the proper people at the right
time
• Improve manufacturing efficiency
• Reduce workplace injuries through better upfront ergonomics analysis
• Make assembly build instructions available to
operators in other languages
• Develop common framework for representing
knowledge and exchanging it between different
systems
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Knowledge Sources in
Manufacturing
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Process Build Information
Required Tooling
Part Information
Ergonomics Analysis
Plant Layout Information
Assembly Visualization
Safety Concerns
Manufacturing “Best Practices”
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Global Study Process Allocation
System (GSPAS)
• The Allocation system
used to assign
manufacturing processes
to plant operation
resources.
• Process sheets use
STANDARD LANGUAGE
(159) verbs
• Like - insert, select, grasp,
load …
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Global Study Process Allocation
System (GSPAS)
• Global System to handle Manufacturing Costing,
Process and Labor Management for vehicle
assembly.
• Standard Language and AI is an integral part of
GSPAS.
• Launched in North America and Europe in 1998
to support the Focus program.
• Currently deployed for almost all car and truck
manufacturing at Vehicle Operations assembly
plants world-wide.
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Step by Step Instructions
 Process sheets specify the operations, tasks, parts and
tools required to support the production of a vehicle.
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Standard Language
• Controlled language where the grammar and
syntax is restricted.
• Developed at Ford Body & Assembly to describe
the vehicle assembly process.
• Contains information about tools, parts and work
required to build a vehicle.
• Contains over 5000 words, 1000 abbreviations
that can be used by the process engineers.
• Standard Language is checked by Artificial
Intelligence (AI) system.
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Examples of Standard
Language
1. ALIGN-AND-SEAT DOOR TRIM PROTECTOR
2. FIRMLY PRESS SEALER INTO JOINT TO
AFFECT A POSITIVE SEAL
3. APPLY DAUB OF SEALER TO THE JOINT OF
THE CENTER FLOOR PAN AND FRONT
FLOOR PAN AT ROCKER PANEL
4. PUSH SEAT REARWARD TO EXPOSE
FRONT ATTACHMENTS
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Standard Language Rules
• Imperative form
• Sentence must start with verb clause followed by
noun phrase.
• Only one Standard Language (main action) verb
per sentence.
• Some prepositions have special meaning
(“using”, “with”).
• Size modifiers may follow nouns (“bumper
large”).
• Free form allowed for certain verbs “verify that..”)
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Standard Language Process
Sheet
Process Sheet Written in Standard Language from CAP (Focus) deck
TITLE: ASSEMBLE IMMERSION HEATER TO ENGINE
10 OBTAIN ENGINE BLOCK HEATER ASSEMBLY FROM STOCK
20 LOOSEN HEATER ASSEMBLY TURNSCREW USING POWER TOOL
30 APPLY GREASE TO RUBBER O-RING AND CORE OPENING
40 INSERT HEATER ASSEMBLY INTO RIGHT REAR CORE PLUG HOSE
50 ALIGN SCREW HEAD TO TOP OF HEATER
TOOL 20 1 P AAPTCA TSEQ RT ANGLE NUTRUNNER
TOOL 30 1 C COMM TSEQ GREASE BRUSH
Resulting Work Instructions Generated by DLMS For Line 20
LOOSEN HEATER ASSEMBLY TURNSCREW USING POWER TOOL
005 GRASP POWER TOOL (RT ANGLE NUTRUNNER) <01M4G1>
010 POSITION POWER TOOL (RT ANGLE NUTRUNNER) <01M4P2>
015 ACTIVATE POWER TOOL (RT ANGLE NUTRUNNER) <01M1P0>
020 REMOVE POWER TOOL (RT ANGLE NUTRUNNER) <01M4P0>
025 RELEASE POWER TOOL (RT ANGLE NUTRUNNER) <01M4P0>
.
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Natural Language Parsing
Secure bracket
using multiple motor nutrunner
Prepositional
Phrase
Verb Phrase
Noun Phrase
Verb
Noun
Preposition
Secure
Bracket
Using
Noun Phrase
Noun
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Process for Natural Language
Processing
• Parse the text (sentence by sentence) into parse
tree structure
• Bypass/ignore common words (articles, common
terms)
• Stemming (get the root of the word)
• Word lookup (synonyms, misspellings,
acronyms)
• Word understanding (deeper-level ontologies)
• Controlled languages with automated checking
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Parsing Information in Standard
Language
• Example of Standard Language parsing: “Feed 2 150
mm wire assemblies through hole in liftgate panel”
• (S (VP (VERB FEED)) (NP (SIMPLE-NP (QUANTIFIER
2) (DIM (QUANTIFIER 150) (DIM-UNIT-1 MM))
(ADJECTIVE WIRE) (NOUN ASSEMBLY))) (S-PP (SPREP THROUGH) (NP (SIMPLE-NP (NOUN HOLE) (NPP (N-PREP in) (NP (SIMPLE-NP (ADJECTIVE
LIFTGATE) (ADJECTIVE OUTER) (NOUN PANEL))))))))
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Ontology – used to represent
knowledge
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Individuals
Classes (with hierarchy); think sets
Properties (w/ hierarchy); not part of class
Equivalence
Property characteristics/restrictions
Complex classes
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GSPAS Ontology
Thing
Tools
Parts
Lexical Nodes
Operations
Intervening Concept Nodes
HAMMER
Attributes: Size,
Part of Speech,
Subsystem-id, etc….
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GSPAS Knowledge Base
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Ergonomics Analysis
• Check the assembly work instructions to
determine what type of physical action is being
described
• Check the assembly work instruction to
determine what object is manipulated
• Check the associated parts and tools for part
weight and tool properties
• Flag potential ergonomics concerns at the
process level and at the work allocation level
• Knowledge can be represented as a business
rule
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Machine Translation
• “The Spirit is willing but the flesh is weak”
• "The vodka is tempting, but the meat's a bit
suspect".
• “The alcohol is arranged, but the meat is weak.”
• “This kind of spirit is wants, but the flesh and
blood is weak.”
• “The spirit is willing, but the flesh is impossible”
• “The spirit puts out the flag and does, the flesh
omits but.”
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Machine Translation
• Use of computers to translate from one
language to another
• Examples: Babelfish
• Translation accuracy is highly dependant on the
quality of the source text
• Use proper grammar, punctuation, shorter
sentences, active voice to improve quality
• Customize translation systems for each
application domain
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Problem Description
• Need to translate assembly build instructions from
English to the language used at the assembly plants
• A single vehicle may require several thousand process
sheets to describe the assembly process
• Large amount of assembly instructions are frequently
modified
• Large volume of translations precludes the use of human
translators
• Specialized terminology requires technical glossaries
• MT performance can be improved greatly by improving
the source text
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Application Description
• Machine Translation is integrated into the process
planning for manufacturing system known as GSPAS
(Global Study Process Allocation System)
• The translation process is fully automated and does not
require human intervention
• Translation occurs automatically after a process sheet is
validated by the AI system and before it is released to
the assembly plants.
• We currently translate build instructions for 26 different
vehicle lines in 5 languages (we also have a separate
glossary for Mexican Spanish)
• Data is read in from an Oracle database, processed
through the translation system and the output is then
written out to the Oracle database
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Machine Translation
• Source: Process build instructions in English
• Target: Process build instructions in Spanish, German,
Portuguese, Dutch & Turkish
• Translate both controlled language and embedded freeform text
• Example: SECURE BUMPER BRACKET {FOR LHS
ONLY} TO VEHICLE BODY USING POWER TOOL
• Utilize customized SYSTRAN translation engine,
automotive and Ford-specific terminology glossaries and
embedded tagging
• Future plans include additional parsing and tagging
information to improve translation accuracy
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Machine Translation
Implementation in GSPAS
• Worked with Systran & Apptek to customize their
translation software for our requirements.
• Develop technical dictionaries that contain Ford
terminology with correct translation for each
language pair.
• Develop and integrate the translation process
into GSPAS.
• Developed a system to check and improve the
source text prior to translation
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Translation Statistics
• Language pairs being translated:
English/German, English/Spanish,
English/Dutch, English/Portuguese, EnglishSpanish (Mexican), English-Turkish
• Ford specific terminology in Standard Language:
over 5000 words, 13,000 noun phrases, over
1000 abbreviations and acronyms .
• Typically translate over 200,000 records each
month
• Over 10,000,000 records already translated.
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GSPAS Translation Process
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Standard Language Translation
Issues
• Sentence structure is not grammatical English (ROBOT
APPLY 50 MM TAPE-STRIPE)
• Ford terminology is complex and must be explicitly
translated as an entire phrase (INSULATION
ASSEMBLY BODY PILLAR)
• Use of abbreviations, misspellings, acronyms (ABS,
A.B.S)
• Use of compound verbs (PICK-AND-SPOON)
• Inverted phrase structure with modifiers (BODY PANEL
LRG)
• Embedded comments (LOAD BUMPER {LOWER} TO
VEHICLE)
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Standard Language Translation
• Use of slang (“shotgun”)
• Articles are seldom used (HAMMER HAMMER).
• Need to handle “British” English as well as
“American” English. (terminology, use, spellings)
• Source text is incorrectly written and not
understandable.
• Punctuation is rarely used.
• Standard Language is always evolving and
needs to be maintained.
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Uses of AI Technology
• Apply natural language processing (NLP) along
with knowledge representation and reasoning to
improve the source text
• Analyze the source text; utilize the ontology to
identify terminology
• Convert the source text to a more “translatable”
form by adding articles, replacing abbreviations,
improving grammar and punctuation
• Utilize XML tagging and ontology lookup to
improve the structure of free-form source text
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Improving Translation Quality
• Process the source text prior to translation
(Standard Language pre-processor).
• Add articles before the nouns.
• Adjust the word order to deal with size modifiers
coming after nouns.
• Replace acronyms, synonyms with original
expanded text (ASY -> ASSEMBLY)
• Verify that punctuation is correct.
• Pre-process the embedded comments to
improve translation quality.
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Issues with Machine Translation
Quality
• Localization issues (even with technical
terminology) – Spanish in Spain, Mexico,
Argentina, etc.
• Ensure that system correctly displays
special characters (umlaut, accents etc.)
• Have additional space available on screen
as target languages require more room
than English.
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Conclusions
• Machine Translation is a cost-effective way to
translate information with high quality if you are
willing to customize the application to your
requirements
• Machine Translation is not an “out of the box”
solution
• Machine Translation accuracy can be greatly
improved by controlling and improving the
quality of the source text
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Where are we going?
• Intelligent search w/ context and understanding
• Sharing of knowledge through ontologies
• Growth of user-defined knowledge
(folksonomies)
• Intelligent Dialog Systems – integration of
speech recognition w/ intelligent engines
(“Sync”)
• Automate the process of information retrieval
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Questions
?????
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