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by: Jace Schamberger


Marketplace > University of Kentucky > Systems Courses > MFS 605 > SYSTEMS FACTORY INFO CONTROL
Jace Schamberger
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Class Notes
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This 40 page Class Notes was uploaded by Jace Schamberger on Friday October 23, 2015. The Class Notes belongs to MFS 605 at University of Kentucky taught by Staff in Fall. Since its upload, it has received 18 views. For similar materials see /class/228172/mfs-605-university-of-kentucky in Systems Courses at University of Kentucky.

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Date Created: 10/23/15
MFS605EE605 Systems for Factory Information and Control Lecture 2 Fall 2004 Larry Holloway Dept of Electrical Engineering and Center for Robotics and Manufacturing Systems Review of Previous Class Basic Manufacturing Classifications Little s Law Advantages of simplicity Review Little s Law Little s Law WIP Production Rate x Manufacturing Lead Time a a apaclty n x 9 5 3 39D 9 3 WIP d E 39D E d 4 E E WIP Implications If not near capacity then increasing WIP increases rate without time increase Everything keeps busy If near capacity then rate cannotincrease more so increasing WIP increases throughput time Problems with Complexity Problems with Complexity Difficulty in maintaining and managing complexity Decreased Reliability If you can t make a simple machine work then you can t make a more complex one work Throwing technology at problems without understanding them first may be a costly mistake Law 3 The Larger the System Scope the Less Reliable the Svstem Law9 Combining 39 quot and quot 39 SaveTimeMoney and Energy Review volume Classification of Discrete Manufacturing Mass Production Batch Production Job Shop Job Shop Production L 7 Variety Modeling What do we mean by a model A representation of a system Used to understand the important factors and their relationships Insight Used to predict performance Used to optimize decision values Used to determine control rules Used to justify investments What kinds of models Physical models Mathematical models Deterministic stochastic simulation Mathematical Models What is a good model Depends on what questions will be asked Analytical vs Experimental Experimental describes such as simulation Good for What if questions Analytical determines an answer through analysis or heuristic What is the lowest cost product mix What is the fastest throughput Learning Objectives on Serial Systems What are the characteristics of a serial system What is the takt time What is a minimum manning What are the benefitsdrawbacks of paced vs unpaced lines Given a set of tasks how do we assign them to sequential workstations Show how this is done with COMSOAL method Show how this is done with RPW method Show how this is done with tree searches Serial Assembly Systems A A A A I 239 Classic assembly line Good for singleproduct or restricted family of products Advantage Very ef cient low WIP low cost Disadvantage requires limited variation on times and types Demands fairly good reliability What is a Serial Assembly System AA 239 I V Prod uct m oves down assem bly line Division of work into work elements Smallest unit of productive work Examples Work elements assigned to stations Work done in sequence as it moves down line History Chicago meatpacking plants 9 Henry Ford automotive Taylorism Scientific Management FW Taylor developer of scienti c management published in 1911 Taylorism assumes Mfg enterprise can be subdivided into independent functions tasks and subtasks Boundaries between mgt Levels and functions should be well de ned and tasks should be formally codi ed into workrules Most ef cient way to do work is to train each worker to do one task or subtask There is one best wayto accomplish a given task a d we can find it by time and motion studies Organization should be directed from top by who have absolute control over every aspect of the business Problems of Taylorism Taylorism fails to extract best from workers since it is based on restrictive notions of their abilities Poor flow of information Organizational boundaries impede flow Assumes info flows only top down Optimization of individual steps may not be optimum of whole process Hierarchy best only if static environment Today s environment is not static Neglects issues of quality inventory waste overhead etc Focuses primarily on labor costs reflected by traditional accounting rules that don t consider reducing costs that add no value Side effects of Taylorism Problems of Taylorism Breakdown of tasks and functions leads to management hierarchy and sometimes extreme division of labor gt poor communication Ignores advantages of synergism between functions Optimization of subfunctions does not imply global optimization Side effects thinking or deciding function lies with mgt only no feedback from workers Quality function lies with separate group quality is not responsibility of worker worker only must produce keep line running Efficiencies of Serial Assembly System AA 239 I V Focus on single part type or family of part types Repetitive Operations Minimal setups Potential for high equipment and person utilization Standard rate takt time for production Minimal WIP Issues with Serial Lines Balancing How to divide work between stations Sequencing If multiple parts then what order to process Paced vs Unpaced Single line vs Multiple lines Advantages of Multiple Multiple means longer work time per part to meet demand Thus allows greater work content per station Simplifies balancing more scheduling flexibility Added robustnessreliability Possible Disadvantages more equipment and setup cost Higher skill requirements Basic Calculations TargetRate Demand Iperiod C 1ITargetRate Note Our book refers to C as the cycle time but often it is referred to as the Takt Time Example 1000week at 37 hours per week 9 27hour C 037 hours 222 minutes each Other issues Line efficiency corrections To correct for assembly quality equipment labor issues etc Typical values 9098 C 222 x 95 211 minutes Minimum Manning ManningStations TWC total work content TWOC number of workers round up Example TWC 20 minutes 9 9 workers minimum Can we achieve this Losses Repositioning Losses Line Balancing Losses Task Time Variability Quality Problems 9 Benefits in reducing wasted motion reducing variability ensuring consistent quality How do we best allocate tasks to stations 17 How many lines Task times must be less than Takt time C If task times not fine enough may be unable to fill C Hn One possible solution multiQIe Qarallel lines Table 21 AdvantagesDisadvantages of Multiple Parallel Lines Advantages Disadvantages Easier to balance work load between stations Higher setup cost Increased scheduling exibility Higher equipment costs Job enrichment Higher skill requirements Higher line availability worker independence Slower learning Increased accountability More complex supervision 125 Paced vs Unpaced Paced Worker given strict time to complete work Synchronous work transport Problem variation in times stress incomplete work Possible solutions Extra time included Work station boundaries extended Small buffers to avoid starving blocking Problem response system Andon cord Unpaced IAsynchronous Workers work when product available work until tasks done Interesting result section 26 Time in line and WIP is less for unpace Disadvantage lack of timing feedback lack of tying to demand rate Unpaced example 20 Other issues Layouts Linear Serpentine Ushaped Buffer sizing Cushion stations from each other Depends on variation issues Production Control Parallel work at workstation 21 Example layouts of serial lines Straight line Ushaped Serpentine 22 Line Balancing Problem Line Balancing Problem How do we allocate tasks to stations Concerns Ordering constraints Example Zoning restrictions Forbidden groupings Required groupings Performance Criteria Balance Delay otal workstation time total work content total workstation time Where total workstation time C x of stations Balance Delay is idle time over paid time 23 Example Alarm Clock Construction sks attach cord to mechanism add back to mechanism add base through back add knobs through back add face to mechanism add hands to face add front glass package I IQTWPQP 24 Total Work Time From diagram 25 min Takt time 1 min Min work manning 251 9 3 people 25 Approaches to Line Balancing Approaches to Line Balancing Exhaustive Search Problem For N tasks there are N sequences Example for clock N8 N 4032 Intelligent Search As we explore stop whenever we know we will do worse Benefit Optimal within limitations of our model Example later slide Heuristic Ranked Positional Weight RPW COMSOAL Others No guarantee of best solution but potentially good 26 Intelligent Search Example Explore branches of tree of possibilities Depth first Load up open workstations before opening new Truncate a branch if not better than before Truncate branch if not feasible schedule Other fathoming rules in book 27 Ranked Positional Weights RPW For each node sum up all times of all nodes following This sum is the RPW forthe node Note don t double count H for B In order of decreasing RPW assign nodes to stations 6 4 2 63 67 02 025 03 Example 28 RPW Example Cont Work El RPW Task time Assign A 175 5 1 B 125 3 1 E 095 2 1 F 075 25 2 C 0 6 4 2 D 055 35 2 G 05 3 3 H 2 2 3 Note ordering of RPW ensures that predecessors always assigned first RPW gave optimal solution 3 this time but no assurance in general 29 RPW nonoptimality Takt time 10 RPW says 3 stations Optimal is 2 stations 30 COMSOAL RPW was deterministic ranked tasks then assigned in order Would we do better if randomly picked next task to assign COMSOAL Computer Method of Sequencing Operations forAssemby Lines Developed by Chrysler Corp Key ideas Iterate through large of alternative sequences Keep track of best sequence so far Randomly select next task to add to sequence could weight random numbers to prefer certain criteria 31 COMSOAL algorithm sketch Given N is number of sequences to consider COMSOALMainN Initialize UnassignedSet as set of all tasks bestsofar is used to store best sequence found so far Define CurrStaton as 1 For N times ExtendSeqUnassgnedSet CurrStaton bestsofar is then your result 32 COMSOAL algorithm sketch ExtendSeq UnassignedSet CurrStation Define FitSet tasks in UnassignedSetsuch that predecessors aren t in UnassignedSet and timeCurrStationtimetask C If FitSet is nonempty Randomly pick task from FitSet Remove task from UnassignedSet Add task to CurrStation If UnassignedSet not em t Call ExtendSeqUnassignedSet CurrStation all tasks have been assigned if better than best so far then store it as best so far els else Close CurrStation adding to totaLide time If totaLidetime for this sequence gt bestsofar t n start over with new sequence Create NewStation Call ExtendSeqUnassignedSet NewStation 33 COMSOAL example Alarm Clock Unassigned FitSet Pick Assign to WS total time 34 Line Balancing Considerations Issues Did not consider zoning constraints or preferences Spread out idle time or concentrate it What if change in demand What about time variations Remove if possible Add additional time Add rework Add linestoppage method 35 Modeling What we just saw was modeling of serial assembly lines We modeled to help in system design allocation of tasks to workstation What other issues are reasons for modeling DesignModi cation Control What are typical measures of performance Lead time Work in process WIP Cash flow Production rates Flexibility Return on Investment Cost 36 Classes of Models Physical Models Physical ExperimentationActually build prototype Disadvantages costly time consumin Advantages provides tremendous insight Mathematical Models Set of math equations or logical relationships describe the system Descriptive For given inputs model gives outputs eg performance Example Simulation models Prescriptive Model indicates how to set the inputs of the system Example Indicates what product mix should we use May be optimization methods or heuristics 37 Major Classes of Models Deterministic Models Often simple rough quick and easy Good for finding unacceptable alternatives Example Does the system have enough capacity Example ls two shifts enough Example Our machine takes 12 minutes per part for A and 8 minutes per part for B We have avg demand of 30 of A per day and 60 of B How many shifts do we need minimum 38 Example Our machine takes 12 minutes per part for A and 8 minutes per part for B We have avg demand of 30 ofA per day and 60 of B How many shifts do we need minimum 1 shift 8hours60 minutes 480 min I shift 2 shifts 960 min Required time A 12 min 30Iday 360 min B 8 min 60Iday 480 min Total 360480 840 min per day assumes no blocking waiting etc and ignores variability 9 At least two shifts per day needed Math Programming Models Examples Linear Programming MixedInteger Linear Programming etc Optimization Problems may be solved exact or through heuristics Typical form Maximize f Subject to constraints 40 LP Example What is the best product mix given max of 140 units of either Price less materials for A 550 Price less materials for B 500 Overhead 100000 I month 336 hours I month Max Profit 550A500B100000 st M1timelt336 M2timelt336 M3timelt336 2 his A lt 140 B lt 140 41 Solution of LP through Excel uses Solver addin pro tA 550 pro tB 500 Overhead 100000 Variables TImeonM1 134 4 hours 2 A TImeonM2 336 hours 2 Sta eon 335 hours 2 A1 55 ProductA Net Pro t prodA pro tA predatpro tarolrerhead Constramts This is found lmeonw 336 hours tlme 336 hours by the solver 335 he prodA 67 2 lt 140 prodB 134 4 lt 140 Note that mlx of 68 I133 glves pro t of 3900 and mlx of 57 I134 glves 3850 42 Note LP solution not an integer LP objective function to maximize is linear combination of decision variables Other Programming methods lnteger program Nonlinear program Some require heuristics since search space is difficult Math Programming methods give solution Depends on correct formulation of constraints Depends on correct definition of the objective function Are you maximizing what you should be 43 Major Classes of Performance Anal cont Queueing Models Probabilistic primarily steady state averages Advantages Analytical solution to problems with randomness amp uncertainty gt fast solution if solution exists or is known Disadvantages Requires simplifying assumptions Best for smaller models solutions not always possible most suited for steady state analysis Examples Avg work in process VVIP Avg time in system 44 Major Classes of Performance Anal cont2 Qualitative Models Qualitative Behavior examples Will a system ever reach deadlock Will a system ever reach this particular state Does step A always happen before step B Example models process algebra Petri nets Sometimes models can incorporate probability also like Markov process Stochastic Petri nets How often does the product X get routed to machine A 45 Major classes of Performance Anal cont3 Simulation run a mathematical model of the system on the computer Advantages sophisticated models sophisticated questions can be asked intuition helped by watching the simulation runs Disadvantages can t ask questions like Is it possible that time consuming model development then multiple simulation runs to analyze relationships aren t as apparent as in analytical solutions 46 Uses of Models Optimization Find the best set of decision variables Issues Approximations in model and in solution The right objective function Are our constraints worthwhile Performance Prediction Answer What if Descriptive models Requires modeler to generate good set of scenarios Prescriptive Models Can get sensitivity information 47 Control Which control policies work best Evaluate performance under different scenarios Insight Thinking about a model requires thinking about the system Justification Selling and demonstrating 48 Responsibility of the Modeler What is effective versus what is efficient Models of a system are not unique require many choices What are the right choices Validation System and model correspond Verification model and its implementation correspond Models should Not be built to prove a point since that is biased from the beginning 49 MFS605EE605 Systems for Factory Information and Control Lecture 10 PLC programming cont Fall 2005 Larry Holloway Dept of Electrical Engineering and Center for Robotics and Manufacturing Systems PLC Review Standard Symbols Normally Open relay Normally Closed relay Simple Logic AND OR NOT PLC Programming Simple Combinatorial Logic State logic latching Sequential logic Simple Combinatorial Logic Key steps Identify inputs and outputs For each output determine expression for what makes it true Construct ladder logic to reflect expression Combinatorial Example If Oven On switch is activated S1 and oven door closed DLS1 and Temperature below threshold TLS 0 then heat is on H1 If oven is on H1 or temp gt thresh TLS 1 and door is closed DLS1 then fan is on F1 If oven is on H1 or light switch is on LS1 then light is on L1 State Logic Latching Key steps Identify inputs and outputs Consider outputs to be state variables For each state variable X determine expression ResetX of what makes state go false determine expression SetX of what makes state go true Construct ladder logic to reflect the following expression ResetX SetX 0 X If you have difficulty converting ResetX into Resetx then you may use a different rung to calculate Reset y then invert it in the rung for X Example Tank System S1 is turned on whenever level is below LLS switch LLS0 S1 is turned off when level above ULS switch ULS 1 Example Motor Control Turn on motor when Onswitch is pressed Turn off motor when Offswitch is pressed orwhen temperature sensor is high Demorgan s Laws Demorgan s laws are useful for negating an expression lABlEOE leBl2E To describe a reset which is a negated expression we can either apply Demorgan s laws or we can create a new rung to create an intermediate signal Sequential Logic Cascade method Sequential Logic has steps The functions that drive the outputs differ depending upon which step is being done Use extra state variables to represent which step is active Draw state diagram representing steps indicating what causes changes in the states Establish latching rung for each step Define Output rungs that depend on the steps Initialization rung may be required Basic format of cascade method program State logic with initializations Output logic Cascade method Revisit of Motor example Turn on motor when onswitch is pressed Turn off motor when off switch is pressed Example problem is on Problem what if temperature sensor is high and start switch Cascade Method revisit of tank method Tank System Two states filling and not filling Start filling whenever level is below LLS switch LLS0 Go to not filling state when level above ULS switch ULS 1 Sequential Logic Example An industrial oven has four states O Preheat Superheat and CoolDown The oven starts in the Off state If startis pushed then it enters the Preheat state and turns the heater on It remains in the preheat state until the temperature sensor T1 becomes true at which time it then enters the Superheat state In this state it continues to heat but locks the oven door Once temperature sensor T2 becomes true then it moves to state CoolDown where the heater turns off The door remains locked until the sensor T1 becomes false at which point the OFF state is entered and the door is unlocked There is a stop switch also but it only works during the preheat state and returns the system to off Industrial Oven state diagram Industrial Oven Ladder Program Industrial oven ladder continued Cascade method critique Cascade method Advantages States are explicit helps in debugging and maintenance Helpful when we have states without unique outputs example supersuperheat Methodical and relatively simple Disadvantages Program is larger than necessary in some cases Extra state variables are bits in memory which may be limited resource May depend on operation method of PLC and sequence of rungs depends on PLC mfg if updates states Wnile running program then could lose state More advanced structures Concurrent parallel paths 20 Builtin Sequencer Functions 21 Timers Example in SLC500 AllenB radleyIRockwell Timer begins timing when rung goes true Timer always reset whenever timer times out Counts using accum to preset value Sets DN when Accum preset Timing in 1 second or 100ths of second 22 Timer example Buzzer If overtemperature sensor is high then buzz for 15 seconds 23 Timer example Automatic lubrication system after machine runs for fixed time we activate a lube solenoid and reset the timer Note T410 will be true forjust one scan so solenoid must run on single pulse 24 Counters Counter similar to timer Increments counter on each falsetotrue transition 25 Counter example Wrapper Count four products before activating wrapper 26 Ladder Diagrams Advantages Graphical easy for simple logic Easy for maintenance and diagnosis Language understandable by floor personnel Ladder Diagrams Problems Note suited to structured programming Poor reuse of logic Poor data structuring Limited support for complex sequencing Limited execution control Cumbersome arithmetic Efforts to offer better programming options IEC 611313 Standard on programming languages for PLC s 27 IEC International Electrotechnical Commision IEC 61131 3 Encourages structured programming Strong Data Typing Execution control Sequence control Data Structures Multiple languages defined 28 Program Organization Unit POU Three types Function Traditional function no internal data Function Block Like objects data and operation Program Top level accesses O coordinatesPOU s Code of POU is either Instruction list Structured text Ladder diagram Function block diagram Pieces tied together using sequential function chart 29 Sequential Function Charts Explicit representation of sequencing and concurrency Ties together function blocks and other program elements into Basic Elements sequences Steps ActiveInactive Transitions Actions 30


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