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Problem Solving Techniques. MST326 lecture 3. Outline of lecture. Brainstorming Mind maps Cause-and-Effect diagrams Failures Mode and Effects Analysis Fault Tree Analysis Design of Experiments. Brainstorming. proposed by Alex Osborn “for the sole purpose of producing checklists of ideas”

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Problem solving techniques l.jpg

Problem Solving Techniques

MST326 lecture 3

MATS326-3 problem.ppt


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Outline of lecture

  • Brainstorming

  • Mind maps

  • Cause-and-Effect diagrams

  • Failures Mode and Effects Analysis

  • Fault Tree Analysis

  • Design of Experiments

MATS326-3 problem.ppt


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Brainstorming

  • proposed by Alex Osborn“for the sole purpose ofproducing checklists of ideas”

  • technique to identify causesand develop solutions to problems

  • “seeking the wisdom of ten people rather than the knowledge of one person” [Kaizen Institute]

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Brainstorming

  • no criticism is permitted

    • “only stupid question is one that is not asked” [Ho]

  • wild ideas are encouraged

    • often trigger good ideas from someone else

  • each person contributes one idea

    • further single ideas on second circuit

    • repeat until no further ideas

  • all contributions are recorded in view

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Brainstorming

  • Osborn proposed 75 fundamental questions

  • can be reduced to:

     seek other uses?  adapt?

    • modify?  magnify?

    • minify?  substitute?

       rearrange?  reverse?

       combine?

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TRIZ

  • Teorija Reshenija Izobretatel'skih Zadach

  • loosely translates asTheory of Inventive Problem Solving (TIPS)

  • 40 Inventive Principles

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40 inventive principles of TRIZ

IP 01: Segmentation IP 02: Taking out IP 03: Local quality

IP 04: Asymmetry     IP 05: Merging     IP 06: Universality

IP 07: Nested doll IP 08: Anti-weight IP 09: Preliminary anti-action

IP 10: Preliminary action IP 11: Prior cushioning IP 12: Equipotentiality

IP 13: The other way round IP 14: Spheroidality or curvature    IP 15: Dynamics

IP 16: Abundance IP 17: Another dimension IP 18: Mechanical vibration

IP 19: Periodic action IP 20: Continuity of useful action    IP 21: Rushing through    

IP 22: Blessing in disguise IP 23: Feedback IP 24: Intermediary

IP 25: Self-service IP 26: Copying     IP 27: Cheap short-lived objects

IP 28: Mechanics substitution IP 29: Pneumatics and hydraulics

IP 30: Flexible shells and thin films    IP 31: Porous materials IP 32: Colour change

IP 33: Homogeneity IP 34: Discarding and recovering    IP 35: Parameter change

IP 36: Phase transition IP 37: Thermal expansion IP 38: Strong oxidants

IP 39: Inert atmosphere IP 40: Composite materials

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Mind maps

  • attributed to Tony Buzan

    • classic book “Use Your Head”

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Mind maps

Image from http://www.loanedgenius.com/scrabble_2_letter_words.gif

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Cause-and-Effect diagrams

  • Cause-and-Effect diagram

    • often referred to as a fishbone diagram

    • or an Ishikawa diagram

  • introduced by Kaoru Ishikawa

    • simple graphical method to record and classify a chain of causes and effects in order to resolve a quality problem

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Cause-and-Effect diagrams

  • Clarify the object effect

  • Pick causes

  • Determine the priority causes

  • Work out the counteractions for priority causes

  • implement appropriate solutions to eliminate or reduce the causes of problems

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Cause-and-Effect diagrams I

  • Clarify the object effect

    • a numerical measurement should be established against which subsequent improvement can be judged

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Cause-and-Effect diagrams II

  • Pick causes

  • create a team of people to brainstorm possible causes that may lead to the effect

  • study the actual effect in the problem environment

  • on a horizontal line draw diagonal branches for direct causes of the effect

  • using arrows onto the branches create sub-branches for appropriate secondary causes

  • confirm all elements of the diagram are correctly positioned

  • quantify the causes wherever possible

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Cause-and-Effect diagrams III

  • Determine the priority causes

    • analyse any existing data for the problem

    • if practical, create a Pareto diagram. 

    • otherwise, determine a ranking of the relative importance of each cause.

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Cause-and-Effect diagrams IV

  • Work out the counteractions for priority causes

    • put in place appropriate solutionsto eliminate or reduce the causes of problems

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Cause-and-Effect diagram:

  • Image from http://www.ifm.eng.cam.ac.uk/dstools/gif/ishika.gif

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Failures Mode and Effects Analysis

  • FMEA is

    • a useful tool for reliability analysis

    • systematic check of a product or process

      • function

      • failure causes

      • failure modes

      • failure consequences

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Failures Mode and Effects Analysis

  • Requires a thorough knowledge of

    • functions of the components

    • contribution of those components to function of the system

  • For every failure mode at a low level,failure consequences are analysed at

    • the local level

    • the system level

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Failures Mode and Effects Analysis

  • FMEA is usually qualitative but may also be quantitative

  • initiated during planning and definitionof a project to investigate qualitative reliability demands of the market

  • during design and development, for quantitative reliability activities

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Failures Mode and Effects Analysis

  • design-FMEA for design reviews

    • definition and limiting of the system

    • choice of complexity level

    • check of component functions

    • check of system functions

    • identification of possible failure modes

    • identification of consequences of failures

    • possibility of failure detection and failure localisation

    • assessment of seriousness of failure

    • identification of failure causes

    • interdependence of failures

    • documentation

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Failures Mode and Effects Analysis

  • quantitative design-FMEA a.k.a. FMECAFailure Mode, Effects and Criticality Analysis

    • consider every component

    • quantify and rank different failure modes

      • F = probability of failure

      • A = seriousness (consequences of failure)

      • U = probability of detection

    • subjective judgements on a scale of 1-5 or 1-10

    • Product (F*A*U) = Risk Priority Number (RPN)

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Failures Mode and Effects Analysis

  • Process-FMEA for

    • pre-production engineering

    • design of process control

    • process improvement

  • FMEA is efficient where component failure leads directly to system failure

  • for more complex failures, FMEA may be supplemented by Fault Tree Analysis (FTA)

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Some URLs for FMEA

  • http://www.fmeainfocentre.com/

  • http://supplier.intel.com/ehs/fmea.PDF

  • http://www.cs.mdx.ac.uk/puma/wp18.pdf

  • http://www.sverdrup.com/safety/fmea.pdf

  • http://www.uscg.mil/hq/msc/fmea.doc

  • http://www.competitiveedge.net/pdfs/fmea.pdf

  • http://www.fmeca.com/ffmethod/methodol.htm

  • http://www-personal.engin.umich.edu/~wmkeyser/ioe539/fmea.pdf

  • http://www.engin.umich.edu/class/eng401/003/LCNotes/fmea.pdf

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Fault Tree Analysis

  • Logical chart of occurrences to illustrate cause and effects

  • developed by DF Haasl, HA Watson, BJ Fussell and WE Vesely

  • initially at Bell Telephone Laboratories then North American Space Industry

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Fault Tree Analysis

  • Common symbols used 1

    • main event

    • basic event

    • incompletely analysed event

    • restriction

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+

1

&

Fault Tree Analysis

  • Common symbols used 2

    • or-gate

    • and-gate

    • transfer to or from another place

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Design of Experiments

  • originally conceived byRonald Aylmer Fisherat Rothampstead Experimental Station during the 1920s

    • analysing plant growing plotsunder different conditions, andneeded to eliminate systematic errors.

      Image from http://www.csse.monash.edu.au/~lloyd/tildeImages/People/Fisher.RA/

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Experimental design

  • Randomisation

  • Replication - repetition so that variability can be estimated

  • Blocking - experimental units in groups (blocks) which are similar

  • Orthogonality - statistically normal.

  • Use of factorial experimentsinstead of one-factor-at-a-time

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Design of Experiments

  • full factorial experiment

    • where a number of factorsmay influence the output of a process, it is possible to study all combinationsof levels of each factor

    • if the number of factors considered increases, then number of experiments required increases more rapidly. 

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Design of Experiments

  • For two levels of n-variables,the number of experiments required is 2n

    • 4 experiments for two variables(low-low, low-high, high-low and high-high)

    • 16 experiments for four variables

    • 64 experiments for six variables.

  • If three levels (low - normal - high) or more are to be studied, then a full factorial experiment soon becomes impractical.

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Design of Experiments

  • results plotted to indicate the influence of each of the factors studied

  • when one factor affects the response,this is known as the main effect.

  • when >1 factor affects the response,this is termed an interaction.

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Design of Experiments

Genichi Taguchi developed orthogonal arrays

  • fractional factorial matrix

  • permits a balanced comparisonof levels of any factor with a reduced number of experiments.

  • each factor can be evaluated independently of each of the other factors. 

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Orthogonal arrays

L4: three two-level factors

L9: four three level factors

Arrays from http://www.york.ac.uk/depts/maths/tables/orthogonal.htm

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Common orthogonal arrays

Table from Tony Bendell “Taguchi Methods”, 1989

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Taguchi

  • Quality Loss FunctionL(x) = k ( x - t )2

    • L = the loss to society of a unit of output at value x  

    • t = the ideal target value

    • k = constant

  • as non-conformance increases,losses increase even more rapidly

MATS326-3 problem.ppt


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