Page 1/Lect. 7
Process Design & Process Quality Control
History (in brief):
 Specialized discipline (~1920, H. Ford)
 Societies for quality control (~1945, World War II)
 Wide applications in industries ~1970
o Motivations:
 Manufacturing of highly complex systems
 Needs for best quality and efficiency
o Way of solution - development of proper mathematical tools
Key methods and their founders:
 ~1875, F.W. Taylor Foundation of principles for enterprise
scientific control
 ~1925, Sheward
Statistical approach to production process
description (on-line process monitoring)
 ~1930, Dodge & Roming
Random sampling inspection methods
 ~1950, Deming
Methods for improvement of production
efficiency and production quality
(focused on top-management)
 ~1980, Taguchi & Deming
Overall dissemination and applications in
industries (U.S.)
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Design Process and it’s Quality
 Traditional approach to design process as a feedback system with
cycle operation:
User Requirements
(Inputs)
System Design
Testing
Product or Process
Design (outputs)
Major disadvantages of the previous (classical) approach:
o Tends to drop into cycle operation, repeating the testing/design
phases
o Optimizes exclusively the design process output (!)
(The product or process design is not optimal with respect to it’s
economical conditions and properties - e.g. final product price)
o Weak relations between the System Design and the Testing
steps – occurrence of a so called “over the wall problem
solving”
(the system designer and the tester do not accept cooperation on
joint problems, isolated approaches to problem solution what
leads to multiple iterations of the previous cycle to achieve the
required objectives of the design (quality, price, complexity,
maintenance properties, reliability, etc.)
o Strong influence of mentality of the manpower involved
(depending on the cultural background, manpower and product
market in the region, etc.)
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Typical shape of the design/test iterations:
No. of
Design
Changes
Due Date
Jap.
U.S.
Months
20-24
14-7
3-1
3
Question: How to extinguish the negative phenomenon?
...leads to Taguchis’ approach to design process:
Core properties of the Taguchis’ method:
A general design criterion (objective function) drives the design process,
which should be clearly defined as:
1. Variance (scatter) minimization within the design process leads to
minimization of the variance also in the resulting process → tool
to improve quality (in fact, what means quality in this context?)
2. Pushing the mean value of the designed process output towards the
required value as much as possible (a straightforward condition)
Additional conditions:
3. Efforts to conduct possibly environment-independent design
(could also be derived from experiments in the design phase of
the process)
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Taguchis’ method is a 3-level procedure consisting of:
User Requirements
(Inputs)
System Design
Parameter Design
Tolerance Design
Product or Process
Design (outputs)
Taguchi design levels:
Level 1: System design
Integrating scientific state of the art about the task solution, existing
technologies, previous experiences, etc. → leads to
development/selection of basic alternatives to the design for the end-user
(also called as “mapping function”)
Level 2: Parameter design
 Selection and tuning of nominal parameter values of the chosen
method for the designed system
 Optimization and tuning of previously chosen parameters with
respect to the final sensitivity of the designed system to input
variations (noise) - close to classical sensitivity analysis.
 (e.g. .varying quality of assembly parts, raw material input, etc.).
 High importance of a design-phase experiment for identification of
such parameters and sensitivities.
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System Sensitivity
Analysis
Process Inputs + Noise
(parts, manpower, raw materials,
market, etc.)
Designed Process
Control Factors
(System internal parameters)
Process Output
Level 3: Tolerance design
Identification and selective reduction of parameter tolerances
(optimization) to achieve minimal quality loss while decreasing price of
the designed process (e.g. using less expensive parts, materials, less
qualified manpower, etc.)
General Approach to Evaluation of Process Quality
Aspects for evaluation of process quality:
1. Sources of variances, variance (scatter) can be considered for basic
criterion to evaluate a product or process. The main driving force to
achieve the best process/ product parameters (quality, price, reliability,
etc.) is always to minimize the variance.
2. The signal-to-noise ratio method (SNR) applies relative measure
related to the effective signal of the input (particular observed property
of the system input). Improving SNR ratio provides better
performance of the process/product.
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1. Variance (or scatter) minimization (examples):
Possibilities for error appearance:
 Impossibility to achieve required behavior (properties of the
process or its’ products) following the design
 Overshooting variance at normal behavior of the process (=
nominal mean value of the process output or product
parameter)
Process Error Cases
Real Mean Value
Real Process Variance
Required Mean
Value
Required Process
Variance
Why to suppress the variance in process/product parameters?
→ The method of minimization variances leads directly to extinguishing
of sources of production/product losses and inefficiencies
(experimentally verified by Deming)
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Scatter minimization – example:
Let’s have a smelting device controlled by two parameters. Both of the
parameters influence mass of the final product, so that we can assume:
Control
Parameters
Product Mass
Smelting
Device
2
Let’s also assume an experiment as:
Product Mass
High
22.5kg
26.5kg
22kg
26.5kg
Control
Parameter 2
Low
Low
Control Parameter 1 High
...where the previous drawing corresponds to average product mass
depending on the parameters 1&2, (e.g. in a set of 25 experiments).
It can also be deduced from the drawing that:
 Parameter 1 has significant influence on the final product mass
 Parameter 2 has low response into the product mass
Also assume that we have a time sequence of the product mass
measurements for particular range-limit values of the parameters 1&2, so
that:
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Product Mass & Mass Scatter
High
22.5kg
26.5kg
22kg
26.5kg
Control
Parameter 2
Low
Low
Control Parameter 1 High
So, it can be deduced moreover:
 Parameter 1 has low influence on the product mass scatter
 Parameter 2 provides significant influence on the product mass
scatter
2. Achieving the best signal-to-noise ratio (examples):
As to the previous example:
 Parameter 1 sets the required nominal (mean) value of the process
respecting the requirements.
 Parameter 2 minimizes the scatter → improves the process quality
Scaling quality by composite measure of SNR defined as:
SNR = E(X(t))/sqrt(D(X(t)),
..where E and D stand for mean value and dispersion of X(t),
respectively.
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How to improve the SNR value?
 Suppression of noise, disturbances, etc. – efficient solution,
decreases also losses as (D(X(t)) decreases
 Raising the level of effective signals – inefficient solution, losses
remain at the same level, E(X(t)) increases (costly)
Practical solutions for improving the SNR ratio:
1. Parallel running of multiple processes (separate inputs) , also referred
as “additional resource usage”, ensures desired capacity of the
production:
Single Process Efficiency is 0.8
Process A
100 pcs. per day * 0.8 = 80 error-free pieces
Process A1
125 pcs. per day in total * 0.8 = 100 error-free pieces
Process A2
2. Parallel redundancy at design phase. The system is designed as fully
parallel running processes (with common inputs):
Process A
MTBF = 1/failure_rate
Process A1
MTBF = 1.5/failure_rate (for 2 systems)
Process A2
For n systems: MTBF = (1+1/2+...+1/n)/failure_rate
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3. Low frequency band-pass with complete (100%) inspection:
Upper
Bound
Complete
Inspection
Process
Inputs
raw materials
parts
manpower
machinery
technology
Output
Lower
Bound
Garbage (loss)
4. Probabilistic (stochastic) process control:
Process
Inputs
raw materials
parts
manpower
machinery
technology
Implementation
Output
product
Observation
Evaluation
Time
Diagnosis
Decision
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5. Usage of better inputs & adjustments of needs and requirements:
Modification and/or taking the advantage of the systems’ transfer
function. The function denotes relation between the input errors (e.g.
noise and disturbances) and the systems’ output and is typically
nonlinear. Therefore, the process output distributions may vary:
Process
Output
Process Static Transfer
Function
Process
Output
Input Property, Etc.
Process Static Trasfer
Function
Input Property, Etc.