Plastics Process Analysis, Instrumentation, and Control
Description
Process analysis is the starting point since plastics processing is different from processing of metals, ceramics, and other materials. Plastics materials show unique behavior in terms of heat transfer, fluid flow, viscoelastic behavior, and a dependence of the previous time, temperature and shear history which determines how the material responds during processing and its end use. Many of the manufacturing processes are continuous or cyclical in nature. The systems are flow systems in which the process variables, such as time, temperature, position, melt and hydraulic pressure, must be controlled to achieve a satisfactory product which is typically specified by critical dimensions and physical properties which vary with the processing conditions.
Instrumentation has to be selected so that it survives the harsh manufacturing environment of high pressures, temperatures and shear rates, and yet it has to have a fast response to measure the process dynamics. At many times the measurements have to be in a non-contact mode so as not to disturb the melt or the finished product. Plastics resins are reactive systems. The resins will degrade if the process conditions are not controlled. Analysis of the process allows one to strategize how to minimize degradation and optimize end-use properties.
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Johannes Karl Fink is Professor of Macromolecular Chemistry at Montanuniversität Leoben, Austria. His industry and academic career spans more than 30 years in the fields of polymers, and his research interests include characterization, flame retardancy, thermodynamics and the degradation of polymers, pyrolysis, and adhesives. Professor Fink has published 20 books on physical chemistry and polymer science with the Wiley-Scrivener imprint, including A Concise Introduction to Additives for Thermoplastic Polymers, The Chemistry of Biobased Polymers, 2nd edition, 3D Industrial Printing with Polymers, The Chemistry of Environmental Engineering and Flame Retardants.
Content
Preface i
1 General Aspects 1
1.1 Subjects of the Book 1
1.2 Special Issues 2
1.3 Injection Molding 3
1.3.1 Cost Estimation in Injection Molding 3
1.3.2 Cost Prediction Models 4
1.4 Miniature Molding Processes 6
1.5 Computer Determination of Weld Lines in Injection Molding 6
1.6 Extrusion Blow Molding 8
1.6.1 Rapid Thermal Cycling Molding 8
1.6.2 Rapid Heat Cycle Molding 8
1.6.3 Injection Molding: Heating 16
1.7 Microcellular Injection Molding 22
1.8 Mold Cooling 23
1.9 Microcellular Foam Processing System 27
1.9.1 Gas-Assisted Injection Molding 27
1.9.2 Water-Assisted Injection Molding 32
1.10 Molding Machine for Granules 32
1.11 Foam Curing of Footwear 33
1.12 Injection Compression Molding 35
1.13 Hot Press System 35
1.14 Stamper Mold 38
1.14.1 Recoding Media 38
1.14.2 Microscopic Structured Body 39
1.15 Plastic Waste 42
1.15.1 Marine Pollution 43
1.15.2 Human Health Effects 45
1.15.3 Recycling 45
References 57
2 Process Analysis 65
2.1 Concepts and Strategies 66
2.1.1 Chemometrics 67
2.1.2 Safety Risks 68
2.1.3 Feedback Procedures 68
2.2 Linear Systems 68
2.2.1 Simple First-Order Systems 68
2.2.2 Fractional Order Systems 69
2.2.3 Nonlinear Systems and Linearization 69
2.2.4 Characteristics of Systems 75
2.2.5 Controllers and Controller Settings 84
2.3 Twin-Screw Extrusion 91
References 92
3 Examples of Process Analysis 99
3.1 Greenhouse Gas Balance 99
3.1.1 Poly(ethylene furandicarboxylate) 99
3.1.2 Polyester Binder 100
3.2 Injection Molding Technology 101
3.2.1 Module for CAD Modeling of the Part 103
3.2.2 Module forNumerical Simulation of Injection Molding Process 104
3.2.3 Module for Calculation of Parameters of Injection Molding and Mold Design Calculation and Selection 105
3.2.4 Module for Mold Modeling 106
3.2.5 Examples of Testing 107
3.2.6 Molding Air Cooling 108
3.2.7 Cavity Pressure 109
3.2.8 Plastics Extruder Dynamics 110
3.2.9 History of Mathematical Modeling 110
3.2.10 Current Physical Components Concept 112
3.2.11 Process Stages 112
3.2.12 Data Envelopment Analysis 116
3.2.13 Taguchi Method 118
3.2.14 Tait Model 119
3.2.15 Phan-Thien-Tanner Model 121
3.2.16 Product Quality Prognosis 121
3.2.17 Production Predictive Control 122
3.2.18 Parameter Optimization for Energy Saving 123
3.2.19 Multilayer Control System 124
3.2.20 Smoothed Particle Hydrodynamics Method 125
3.2.21 Temperature-Dependent Adaptive Control 126
3.2.22 Micro-Injection Molding 128
3.2.23 Immiscible Polymer Blends 131
3.2.24 Resin Injection Molding 133
3.2.25 Foam Injection Molding 137
3.2.26 Self-Optimizing Injection Molding Process 138
3.2.27 Machine Setup 140
3.3 Shrinkage in Injection Molding 146
3.3.1 Factors that Affect the Shrinkage 146
3.3.2 Effect of a Cooling System 147
3.3.3 Influence of Molding Conditions on the Shrinkage and Roundness 148
3.3.4 Shear Viscosity 148
3.3.5 In-Situ Shrinkage Sensor 149
3.3.6 Semicrystalline Polymer 151
3.3.7 Thermoplastic Elastomers 151
3.3.8 Reprocessing of ABS 153
3.3.9 Sequential Simplex Algorithm with Automotive Ventiduct Grid 155
3.3.10 Taguchi, ANOVA, CAE, and Neural Network Methods 156
3.4 Recycling by Extrusion 166
3.4.1 Multiple In-Line Extruders 166
3.4.2 Mixed Post-Consumer Plastic Waste 167
3.4.3 Poly(methyl methacrylate) 168
3.4.4 Poly(ethylene terephthalate) 169
3.4.5 Poly(lactic acid) 169
3.4.6 Expanded Poly(styrene) 169
3.5 Batch Washing of Recycled Films 171
3.5.1 Recycling of Poly(styrene)Waste 171
3.5.2 Textile Finishing 172
3.5.3 Removing Scrap from Containers 173
3.5.4 Adsorption Isotherms and Desorption Rates 175
3.6 Self-Purging Microwave Pyrolysis 176
3.7 Purging and Plasticization in Injection Molding 177
3.7.1 Automatic Purging 177
3.8 Hot Runner Systems 179
3.8.1 Hot Runner Mold with Runner Pipe 180
3.8.2 Hot Runner System in Plastics Molding Tools 183
3.8.3 Manufacturing and Assembling of Hot Runner Systems 184
3.9 Blown Film Extrusion and Thickness Control 185
3.10 Residence Time Distribution for Biomass Pyrolysis 186
3.11 Reactive Extrusion 187
References 187
4 Process Instrumentation 201
4.1 In-Mold Measurement 201
4.2 Temperature 202
4.2.1 Soft Actuator 202
4.2.2 Thermocouples 202
4.2.3 Resistance Temperature Detectors 206
4.2.4 Thin Film Miniature Temperature Sensors 214
4.2.5 Neural Networks 214
4.3 Position Transducers 215
4.3.1 Rotary Position Transducer 215
4.3.2 Linear Variable Differential Transformers 216
4.3.3 Optical Encoders 218
4.3.4 Thickness Gauges 218
4.4 Composition of Matter 222
4.4.1 IR Interferometer for Multilayer Film 222
4.4.2 X-Ray Diffraction 225
4.4.3 Ion Mobility-Mass Spectrometry 226
4.4.4 Test for Ice Adhesion Strength 226
4.4.5 Piezoelectric Coaxial Filament Sensors 228
4.4.6 Instrumentation for Impact Testing 228
4.4.7 Treatment of Titanium Surfaces 229
4.4.8 Spatial Differentiation of Sub-Micrometer Domains 230
4.5 Medical Issues 231
4.5.1 Endoscopic Plastic Surgical Procedures 231
4.5.2 Medical Catheters 231
4.5.3 Multichannel Plastic Joint 237
4.5.4 Transluminal Endoscopic Surgery 238
4.5.5 Wire-Actuated Universal-Joint Wrists 238
4.5.6 Musculoskeletal Disorders 239
References 240
5 Actuators and Final Control Elements 245
5.1 Servo Valves 245
5.1.1 Nozzle Assembly for a Servo Valve 245
5.2 Servo Motors 248
5.2.1 Hydraulic System 248
5.2.2 Functionally Graded Materials 248
5.3 Solenoid Valves 251
5.3.1 Design Verification Methodology 251
5.3.2 Small Solenoid Valve 252
5.3.3 High-Speed Solenoid Valve 252
5.3.4 Numerical Simulation 252
5.4 Heaters 253
5.4.1 Conduction Heaters 253
5.4.2 Radiant Heaters 255
5.4.3 Heater Controls 255
5.5 Drive Motors and Motor Speed Control for Extrusion 256
5.5.1 Single-Drive Motor 256
5.5.2 Linear Induction Motor 256
5.5.3 Motor Power Consumption in Single-Screw Extrusion 257
5.5.4 Dual Motor Multi-Head 3D Printer 258
References 258
6 Analysis of Melt Processing Systems 261
6.1 Process Parameter Determination of Plastic Injection Molding 261
6.1.1 Case-Based Reasoning Method 261
6.1.2 Knowledge-Based Reasoning Method 264
6.1.3 Rule-Based Reasoning Method 265
6.1.4 Fuzzy Reasoning Method 266
6.2 Process Parameter Determination of Plastic Injection Molding of LCDs 267
6.3 Processing History 267
6.3.1 Flow Defects 267
6.3.2 Biocomposites 269
6.3.3 3D Printing 271
6.3.4 Semiconducting Polymer Blends 272
6.3.5 Van Gurp-Palmen Plot 272
6.3.6 Nanocrystal Composites 273
6.3.7 Melt-Mastication 274
6.3.8 Crystal Nucleation in Nanocomposites 275
6.4 Shear History 276
6.5 Extrusion Product Control 278
6.5.1 Branched Structures 278
6.5.2 Big Area Additive Manufacturing 279
6.5.3 Single-Screw Extrusion Control 280
6.5.4 Blown Film 284
6.5.5 Chill Roll Cast Film 285
6.5.6 Sheet 292
6.5.7 Profiles 294
6.5.8 Pipe and Tubing 297
6.5.9 Automatic Screen Changers 303
6.6 Extrusion Blow Molding Parison Control 306
6.7 Injection Molding 310
6.7.1 Ram Velocity Control 310
6.7.2 Pressure Control 313
6.7.3 Gas-Assisted Control 319
6.7.4 System Diagnostics 322
6.7.5 Statistical Process and Quality Control 328
6.8 Thermoforming 329
6.8.1 Twin Sheet Thermoforming 329
6.8.2 Rotary Thermoforming 330
6.8.3 Process Model for Thermoforming 331
6.9 Rotomolding 332
6.9.1 Polymer Compositions for Rotomolding 334
6.10 Compounders 348
6.10.1 History of Compounding 348
6.10.2 Types of Compounders 348
6.10.3 Special Applications 350
References 352
7 Auxiliary Equipment 363
7.1 Crammer Feeder 363
7.1.1 Crammer Feeder for Extruder 363
7.1.2 Devulcanization of Scrap Rubber 363
7.2 Dryers 364
7.2.1 Drying Temperatures 364
7.2.2 Moisture Content 366
7.2.3 Resin Dryers 366
7.2.4 Pellet Dryers 369
7.3 Pullers 379
7.3.1 Pullers in Extrusion 379
7.3.2 Pullers in Injection Molding 381
7.4 Chillers 384
7.5 Robots 385
References 387
Index 389
Acronyms 389
Chemicals 394
General Index 399
1
General Aspects
1.1 Subjects of the Book
This book introduces the subject of process analysis, instrumentation and control for modern manufacturing in the plastics industry. Process analysis is the starting point since plastics processing is different from processing of metals, ceramics, and other materials. Plastics materials show an unique behavior in terms of heat transfer, fluid flow, viscoelastic behavior, and a dependence on the previous time, temperature and shear history which determines how the material responds during processing and its end use.
Many of the manufacturing processes are continuous or cyclical in nature. The systems are flow systems in which the process variables, such as time, temperature, position, melt and hydraulic pressure, must be controlled to achieve a satisfactory product, which is typically specified by critical dimensions and physical properties which vary with the processing conditions. Instrumentation has to be selected so that it survives the harsh manufacturing environment of high pressures, temperatures and shear rates and yet it has to have a fast response to measure the process dynamics. Many times the measurements have to be in a non-contact mode so as not to disturb the melt or the finished product. Plastics resins are reactive systems. The resins will degrade if the process conditions are not controlled. Analysis of the process allows one to strategize how to minimize degradation and optimize end use properties.
Linear systems in which there exists a one-to-one relationship between the input variable and the output response are the easiest to analyze and control. Plastics on the other hand show a nonlinear dependence on part/product cooling which varies with the square of the part thickness, laminar flow which varies with the cube of the wall thickness and mechanical strength/stiffness which varies with the cube of wall thickness. Also, wall thickness influences the crystallization, shrinkage, morphology and critical dimensions of the product.
In order to make corrections to the process, actuators, also known as final control elements, must introduce energy to the system. This hardware is in the form of servo valves, solenoid valves, servo motors, heaters, and blowers. The sizing, response time, ruggedness and linearity must be considered. All the above hardware has to be assembled into a system and programmed with a suitable algorithm to carry out automatic control. The control configuration and the algorithm are dictated by the system itself. Common control modes are feedback setpoint control which is common in extrusion, servo control which is common in injection molding and blow molding cyclical processes, and combinations and variations thereof.
1.2 Special Issues
A simplified, practical, and innovative approach to understand the design and manufacture of plastic products in the World of Plastics has been presented (1).
The information defines and focuses on past, current, and future technical trends. This handbook reviews more than 20,000 different subjects.
Various plastic materials and their behavior patterns were reviewed. Examples are provided of different plastic products and critical factors relating to them that range from meeting performance requirements in different environments to reducing costs and targeting for zero defects (1).
1.3 Injection Molding
1.3.1 Cost Estimation in Injection Molding
Cost and performance estimation are frequently used at the early stages of product development to determine the feasibility and drive critical design decisions. Early cost estimation has been hampered by the unavailability and uncertainty of information.
Here, cost estimates were derived from a complexity metric as defined by the number of dimensions that uniquely define the part geometry (2).
The cost drivers of manufacturing an injection molded plastic part Cpart are expressed in Eq. 1.1.
[1.1]The material cost contribution, Cmat, is very significant, typically 50% to 80% of the total part cost. Tooling and processing costs are also significant cost drivers. The processing cost, Cproc, is dependent on the hourly rate charged for the usage of the injection molding machine as well as the processing yield, yproc, which is the ratio of good parts to the total number of parts produced. The tooling cost, Ctool, is amortized over the estimated production quantity N for the life of the tool.
Eq. 1.2 is an expression for the assembled product cost.
[1.2]The m parts that constitute the product include both injection molded and standard purchased parts. The cost of the assembly is the product of the assembly shop hourly rate, Rassy, and the total time required to assemble the m parts constituting the product. Thus, the assembly cost decreases as part-count m decreases. The overhead cost per product COH includes both the shop and the administrative overheads.
Dimensionality and other critical design variables can be automatically assessed within modern computer-aided design systems throughout the product development process to provide continual feedback regarding tooling, process, and material costs (2).
The complexity-based models were developed and tested with empirical data for thirty injection molded parts from different suppliers and was found to have a highly significant correlation with mold costs and tooling lead times. Models for estimating material and processing costs and yield at the early stages of design are also developed. The developed methods enable real-time evaluation of the effects of a product design on its tooling cost, tooling lead time, processing costs, and yield at the early stages of design (2).
1.3.2 Cost Prediction Models
With the recent evolution of additive manufacturing, accurate cost prediction models are of increasing importance to assist decision-making during product development tasks (3). Estimating the cost is a challenging task in that it requires a vast amount of manufacturing knowledge in which many aspects, from design to production, need to be synchronized. As a result, various additive manufacturing cost models have been developed.
The state of the art in product cost estimation covering various techniques and developed methodologies has been reviewed (4). The overall work can be categorized into qualitative and quantitative techniques. The qualitative techniques are further subdivided into intuitive and analogical techniques, and the quantitative ones into parametric and analytical techniques. Also, the importance of cost estimation in the early phases of the design cycle is discussed in the review (4). The cost classification techniques are summarized in Table 1.1.
Also, more recently, an overview was presented of the costing models being developed and utilized associated with the additive manufacturing product development phases (3). Here, it was observed that the contexts and views described during the development of the models were often targeted at specific applications as well as technologies and were classified in many ways. Accordingly, different aspects of the cost estimation classification technique were detailed and definitions of some of the key terminologies were reported.
Since 2006, a total of ten review works related to costing in additive manufacturing were reported in which each differed significantly in terms of their scope. These works are collected in Table 1.2.
Table 1.1 Cost classification techniques (3).
Classification techniques Definition Method-based Qualitative: Intuitive Based on the experience of the estimator Qualitative: Analogy Based on historical data. A comparison is often made between old parts and new parts during estimation Quantitative: Parametric Based on statistical regression expression where variables are referred to as cost drivers Quantitative: Analytical Based on product decomposition into units, operations, or activities that relate to how to manufacture the product Task-based Design-oriented Based on design-related activities Process-oriented Based on the process of commissioning the product development activities covering production-related and post-processing costs Level-based Process-level Based on the production cost, which involves entire product development phases (pre-processing, production and post-processing) System-level Based on product life cycle that covers supply chain, operation management and system-level services1.4 Miniature Molding Processes
In a chapter of a monograph, particular processing strategies and techniques for injection molding of precision parts, thin wall parts, microstructured parts, and microparts have been described (5).
The importance of incorporating size effects into the filling simulation of microcavities has been demonstrated. The standard injection molding simulation and special simulation needs for miniature molding processes have been discussed (5).
1.5 Computer Determination of Weld Lines in Injection Molding
A weld line is one of the most...
