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Decision-Based Design of Networked Manufacturing Systems (NMS)

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Architecting Networked Engineered Systems

Abstract

In global economy characterized by dynamic markets changes in customer preferences necessitate timely adjustments in manufacturing. Conventional manufacturing processes are designed for mass manufacturing and are not suited for agile, flexible and highly reconfigurable smart manufacturing. Industry 4.0 attempts to address the design of smart manufacturing systems by leveraging the advantages of digitization.

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Abbreviations

X k :

Part accumulated variation up to Station k including Station k

\( X_{k - 1} \) :

Part accumulated variation up to Station k − 1 including Station k − 1

\( U_{k} \) :

Control vector at Station k, that is defined as the fixture error vector for both subassembly parts at Station k

\( Y_{k} \) :

Measurement obtained on Station k

\( \xi_{k} \) :

Noise due to unmodeled effects, independent from other noise

\( \eta_{k} \) :

Sensor noise, independent from other noise

\( A_{k - 1} \) :

Dynamic matrix, characterizes variation change due to part transfer from Station k to/and Station k + 1

\( B_{k} \) :

Input matrix, determines how fixture variation affects part variation at Station k

\( C_{k} \) :

Sensor locations information on a station

\( D_{N} \) :

Diagnosability matrix

\( \rho ( \cdot ) \) :

Rank of a matrix

\( m_{k} \) :

Number of potential fixture faults at Station k

\( \overline{u}_{k} \) :

Vector of input parameters at Station k

\( T_{k} \) :

Realizability matrix

\( C_{k} \) :

Controllability matrix

\( Y_{k} \) :

Measurement obtained at Station k

\( c_{1} \) :

Monetary cost of total number of sensors

\( c_{2} \) :

Monetary cost of sensing station

\( c_{3} \) :

Monetary cost for using PT control actions

\( c_{4} \) :

Monetary cost for reducing the number of sensing station

\( s_{k} \) :

Estimated control actions

\( Q_{k} \) :

Weighting coefficient matrix, shows differences in the importance and characteristics of the measured points

\( x \in {\mathbb{R}}^{{n_{x} }} \) :

State vector

\( u \in {\mathbb{R}}^{{n_{u} }} \) :

Input/control vector

\( d \in {\mathbb{R}}^{{n_{d} }} \) :

Disturbance vector

\( y \in {\mathbb{R}}^{{n_{y} }} \) :

Output vector of the process

\( \dot{x} \) :

Time derivative of the state vector

\( \dot{u} \) :

Time derivative of the input/control vector

\( f:{\mathbb{R}}^{{n_{x} + n_{u} + n_{d} }} \) :

Nonlinear function

\( g:{\mathbb{R}}^{{n_{x} + n_{u} + n_{d} }} \to {\mathbb{R}}^{{n_{y} }} \) :

Nonlinear function

\( \mu \) :

Measure function for calculating the size of the corresponding space

\( t_{f}^{*} \left( {y_{sp} ,d} \right) \) :

Minimum time necessary to respond to a change in the set-point, \( y_{sp} \), and to a disturbance d, and

\( {\mathcal{M}} \) :

Include the final-time constraints

\( t_{f}^{d} \left( {y_{sp} ,d} \right) \) :

Desired dynamic performance, or the maximum allowable response time, in tracking a set-point change, \( y_{sp} \), in DOS and/or recovering from disturbance, d, in EDS

CT :

Configuration tree

u :

Nodes

v :

Edges

\( U \) :

Node set of the RMT configuration tree

\( V \) :

Edge set of the RMT configuration tree

\( u_{ijk} \) :

Element in the node set U

\( i \) :

Type of a node; see Table 2.1

Table 2.1 Variable i and corresponding node type
Full size table
\( j \) :

Identifier of the node of the same type

\( k \) :

Section of the configuration tree to which the node belongs

\( v \) :

Ordered pair that specifies one edge in the configuration tree

\( u_{ijk} \) :

Parent node in the edge

\( u_{{i^{\prime} j^{\prime} k^{\prime} }} \) :

Child node

\( f_{1} \) :

Discriminant function of the edge

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Author information

Authors and Affiliations

  1. Systems Realization Laboratory, Division of Industrial Design, University of Liverpool, Liverpool, UK

    Jelena Milisavljevic-Syed

  2. Systems Realization Laboratory, School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA

    Janet K. Allen

  3. Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV, USA

    Sesh Commuri

  4. Systems Realization Laboratory, School of Aerospace and Mechanical Engineering, University of Oklahoma, Norman, OK, USA

    Farrokh Mistree

Authors
  1. Jelena Milisavljevic-Syed
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  2. Janet K. Allen
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  3. Sesh Commuri
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  4. Farrokh Mistree
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Corresponding author

Correspondence to Jelena Milisavljevic-Syed .

Glossary

ACRONES

Adaptable Concurrent Realization of Networked Engineering Systems

AIS

Available Input Space

AOS

Achievable Output Space

Big Data Analysis

Ability to analyze large volumes of data

cDSP

Compromise Decision Support Problem

Connectivity in the System

Enabling distinct and independent processes to communicate with one another through well-defined protocols and strategies

DAIS

Dynamic Available Input Space

DAOS

Dynamic Achievable Operating Space

DDOS

Dynamic Desired Operating Space

DFDM

Design for Dynamic Management

DIS

Desired Input Space

DOI

Dynamic Operability Index

DOM

Dynamic Operability Model

DOS

Desired Output Space

EDS

Expected Disturbance Space

Flexible Production

Ability to adapt to changes in the product being manufactured, both in type and quantity

FWC

Feedforward Control

Mixed Variable

Problems Problems with multiple goals where goal functions are linear and/or non-linear, system variables are continuous, Boolean, linear and/or non-linear inequality constraints, equality constraints, and system boundaries

NMS

Networked Manufacturing Systems

NOI

Nominal Operability Index

OI

Operability Index

Original System Design

New system design without previous information to build the model

Process Controllability

Capability of the process to mitigate the errors and drive the system from an arbitrary state to a desired state along specified state trajectories

Process Diagnosability

Capability of the process to detect faults and identify their cause

PT

Programmable tooling

RIS

Reconfiguration of Inspection System

RMS

Reconfigure of Manufacturing System

RMT

Reconfiguration of Machine Tool

Sensing Cost

Sensing cost relates to the expense of building sensing stations, using Programmable Tooling (PT) control actions, and penalties for reducing the number of sensing stations

Sensing Stations

Stations with installed sensors

Smart Manufacturing

Digitized Manufacturing

SOIIS

Servo Operability Index in the Input Space

SOIOS

Servo Operability Index in the Output Space

SoV

Stream of Variation model

SSOM

Steady-State Operability Model

TDS

Tolerable Disturbance Space

Uncertainty

Inherent randomness or unpredictability of a system, model parameters uncertainty , and model structure uncertainty

Variant System Design

System design based on the existing information

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Milisavljevic-Syed, J., Allen, J.K., Commuri, S., Mistree, F. (2020). Decision-Based Design of Networked Manufacturing Systems (NMS). In: Architecting Networked Engineered Systems . Springer, Cham. https://doi.org/10.1007/978-3-030-38610-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-38610-8_2

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  • Print ISBN: 978-3-030-38609-2

  • Online ISBN: 978-3-030-38610-8

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