玩具上蓋的注射模具設(shè)計(jì)-抽芯塑料注塑模含NX三維及19張CAD圖帶開題

玩具上蓋的注射模具設(shè)計(jì)-抽芯塑料注塑模含NX三維及19張CAD圖帶開題,玩具,注射,模具設(shè)計(jì),塑料,注塑,nx,三維,19,cad,開題 Intelligent Manufacturing Process Tool For Plastic Injection Molding Aravind Kumbakonam, Terrence L. Chambers, Suren N. Dwivedi Department of Mechanical Engineering University of Louisiana, Lafayette Bill Best Ash Industries Aravind Kumbakonam, Terrence L. Chambers, Suren N. Dwivedi Bill Best Ash IndustriesAbstract This paper presents an overview of ongoing research aimed at the development of a Computer-Based Intelligent Manufacturing Process Tool, at the University of Louisiana at Lafayette. The Manufacturing Process Tool is a computer program, which would help the manufacturer in solving problems associated with Injection Molding. These problems include long process set up time, non-optimized cycle time, and poor control of the molding process. The Manufacturing Process Tool would eventually help the machine operator (who need not be an expert) in setting up, optimizing, and controlling the Injection Molding Process; thus maximizing the production rate on that particular Injection Molding machine. Introduction Plastic Injection Molding is the worlds most common method of producing complex commercial plastic parts with excellent dimensional tolerance. According to the C-mold design guide, 32% by weight of all plastics processed go through Injection Molding machines, making Plastic Injection Molding one of the most important manufacturing processes. It is seen that the final molded part quality is chiefly dependent on the type of material, mold design and the molding process settings. Once the material and the mold to be used are specified, the part quality basically depends on the molding process. The molding process is quite complex involving many variable process parameters like pressure, temperature and time settings. These process parameters have to be optimally set in order to improve part quality and maximize the production capacity of the Injection Molding machine. Educated and experienced individuals are required to set up and optimize such a complex process. These individuals control the molding process on a trial and error basis, which is usually time consuming. This method of controlling the molding process relies heavily on operator intuition and a few “rules of thumb,” which the operator develops over a period of time while working with different materials, pressures, temperatures and time settings. This paper presents an outline of ongoing research at the University of Louisiana at Lafayette involving the development of Intelligent Knowledge-Based Engineering Modules (IKEM) for . IKEM has different modules, namely: Parsing, Mold Design, Cycle Time and the Manufacturing module, which are linked to each other. This paper mainly concentrates on the Manufacturing module, which involves the development of an Intelligent Manufacturing Process Tool called “The Optimizer.” Figure 1 and 2 show the transfer of data between the different modules of IKEM.Figure 1.Intelligent Knowledge Base Engineering Modules For Plastic Injection Molding Figure 2. Data Flow Diagram The Optimizer captures non-deterministic knowledge in the Injection Molding process from an expert in this field, and also uses deterministic knowledge available in the form of relations, look up tables, etc. The Optimizer is written in Visual Basic, and would assist the machine operator in setting up, optimizing and controlling the Injection Molding Process and thus maximize the production rate on that particular Injection Molding machine. As shown in Figure3, The Optimizer helps the manufacturer in the set up of the molding machine, by giving him the initial optimal process parameter values. These values could be later fine tuned for personal benefits by the operator using his intuition and guesswork.Figure 3. Product Development Vs Time圖3：產(chǎn)品開發(fā)與時(shí)間Knowledge-Based Engineering Modules An expert system, or a knowledge-based system, is defined as “a model and associated procedure that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert” Of the different kinds of expert systems available, which have their individual advantages and disadvantages, the “Rule Based” type of knowledge-based system is the one that is most commonly used. It basically consists of a knowledge base, containing a set of “IF-THEN” statements, called “rules.”The IKEM project deals with creating an expert system containing a set of IF-THEN rules collected from the human expert with the help of knowledge engineers. Two of the most important issues that are essential to the reliability of this rule-based type expert system are, the Knowledge Acquisition and Knowledge Representation. Knowledge Acquisition This is the initial approach wherein the knowledge engineers extract the rules from the human expert. This step is quite labor intensive, and has been considered the bottleneck in the expert system development process. Knowledge acquisition mainly depends on the skills of the knowledge engineer. His primary aim is to extract strategies or rules of thumb from the human expert(s) and transfer it to the knowledge base. Care has to be taken during the knowledge acquisition process, as it directly affects the knowledge representation scheme, later. Its seen that there are two basic types of Knowledge Acquisition: 1. Knowledge Acquisition directly from the human expert (non deterministic knowledge) 2. Knowledge Acquisition thorough previous cases, relations, look up tables (deterministic knowledge). The Intelligent Manufacturing Tool being developed intends to capture nondeterministic knowledge which is gained by experience in the field, as well as more deterministic knowledge available in the form of relations, look up tables, etc.A student knowledge engineer from the University of Louisiana at Lafayette has been working in conjunction with Ash Industries, Lafayette, which is primarily a Plastic Injection Molding plant. The knowledge engineer interviews the human expert at Ash Industry. He then organizes this extracted data in a logical fashion. This knowledge extracted from the expert is in the form of heuristics, or more precisely “rules of thumb.” The expert develops these heuristics or rules of thumb intuitionally and from his prior experience in this field. These “rules of thumb” act as the guidelines, using which; the molding process is operated for the most optimal product quality. A typical example of a set of “rules of thumb” or heuristics developed by the expert useful in the calculation of clamp tonnage is: Rule 1: IF part wall thickness = 0.04 inch And material = crystalline THEN clamp tonnage = 2.0 ton/ inch2 * Rule 2: IF part wall thickness = 0.04 inch And material = amorphous THEN clamp tonnage = 3.0 ton/inch2 Rule 4: IF part wall thickness 0.04 inch And material = amorphous THEN clamp tonnage =3.6 ton/inch2l Inch2 represents the cross sectional area of the total number of parts in the mold that are perpendicular to the nozzle of the injection molding machine.To this rule set we add a couple more rules, to determine whether the material selected is amorphous or crystalline. Using these six rules the expert system calculates the required clamp tonnage. Rule 5: IF mold shrink, linear flow rate* of the material = 12 THEN material = crystalline.Knowledge Representation Knowledge Representation is the second stage of the knowledge engineering process, wherein the knowledge acquired is coded into the Knowledge Base. The heuristics obtained by the knowledge engineer from the human expert are represented in the knowledge base using IF-THEN rules so that conclusions can be drawn by the expert system. These IF-THEN rules are then coded in VISUAL BASIC. A separate material database is created in Microsoft Access and then linked with the Visual Basic program. This is shown in Figure 4.Mold shrink, linear flow rate obtained from the material database.The Outputs The Optimizer gives out the most optimized values of different parameters affecting the Injection Molding process, which have to be controlled in order to ensure that a high quality part is produced in the most economical way. The outputs of The Optimizer could be confined to four different categories, namely: the Temperature, Pressure, and Time And Distance. Each of these outputs are represented in Figure 5 and discussed in detail below.Temperature: Approximately 80% of the plastic products produced today are made of thermoplastics. Thermoplastics could be defined as “ plastic materials which, when heated, undergoes physical change1.” The different types of thermoplastics are: Amorphous materials, “ which basically soften as the temperature is increased and get softer and softer as more heat is absorbed, until they degrade1.” Crystalline materials, “ these dont have a softening stage but they stay firm until they are heated to a particular point at which they start to melt and later degrade if more heat is added1. ” Figure 5. The OptimizerConsidering the differences in the properties of amorphous and crystalline materials, we set the different temperatures in the Injection Molding machine. 1. Melt temperature: The temperature to which plastic material has to be heated before it is injected into the mold. Optimizing the melt temperature results in controlling the flow rate of the material, material degradation, brittleness and flashing.2. Barrel temperatures: The different temperatures to be set at the rear, middle and the front end of the barrel of the injection-molding machine. 3. Nozzle temperature: The temperature set at the machine nozzle, which is right in front of the heating zone (barrel) of the plastic. 4. Mold temperature: The temperature at which the injection mold has to be set to obtain a plastic part of high quality with a lower cycle time. The optimized mold temperature helps in obtaining reduced cycle time and better part quality having a glossy finish, less warp and less shrinkage Pressure: There are various pressures to be optimized in the Injection-Molding machine. 1. Clamp Pressure: The amount of pressure to hold the injection mold tightly against the injection pressure. The optimal clamp pressure prevents the mold from flashing due to less clamp tonnage. It even saves energy and the mold from collapsing due to high clamp tonnage. 2. Injection Pressure: The amount of pressure required to produce the initial filling (95%) of the mold cavity. The optimized injection pressure helps to attain a part of high quality, less shrinkage, less warp, and that is easy to eject. 3. Holding Pressure: The second stage of the injection pressure, and usually fills up the remaining 5% of the mold cavity. It is usually needed to hold the plastic in the mold, from flowing back into the barrel. 4. Back Pressure: The pressure exerted by the plastic on the screw spindle. Optimized backpressure helps in obtaining a part of better density and fewer voids.Time: Cooling time: It is the amount of time required by the plastic part in the mold cavity to solidify and get ejected safely. The optimized cooling time helps in achieving better cycle time. Distance: Mold open distance: The distance for the mold halves to open apart in order to eject the part safely. Optimal mold open distance is necessary for better cycle time.Conclusion The Manufacturing Process Tool, which has been discussed in this paper, is being developed at the University of Louisiana at Lafayette. When completed this Tool will be able to give the initial optimal process parameter values, which are crucial to start off the injection molding process. These values could be later fine tuned for personal benefits by the operator using his intuition and guesswork. References 1. Douglas M.B., Fundamentals of Injection Molding: Material Selection and Product Design Fundamentals, Vol. 2, Society of Manufacturing Engineers. 2. C-MOLD Design Guide. - A Resource for Plastic Engineers. 3. Dym. J. B., Injection Molds and Molding, 2nd edition, Van Nostrand Reinhold. 4. Xinming Jin, Xuefeng Zhu, “Process Parameters Setting Using Case-Based and Fuzzy Reasoning for Injection Molding.” Proceedings of the 3rd World Congress on Intelligent Control and Automation. June 28-July 2, 2000, Hefei, P.R. China. 5. Ignizio J. P., Introduction to Expert Systems: The Development and Implementation of Rule-Based Expert Systems, Mc Graw Hill. 6. Bob Hatch, On the Road with Bob Hatch: 100 Injection Molding problems solved by IMMs Troubleshooter, Injection Molding Magazine. 7. Mok S.L, Kwong. C.K,Lau. W.S “ Review of research in the determination of process parameters for plastic injection molding.” Advances in Polymer Technology, V 18, n 3, 1999, p 225-236. 8. Shelesh-Nezhad. K, Siores, E. “Intelligent system for plastic injection molding process design.” Proceedings of 1996 3rd Asia Pacific Conference on Materials Processing, Nov 12-14 1996, Hong Kong, Hong Kong, p 458-462 9. Yeung.V.W.S., Lau.K.H., “ Injection Molding, C-MOLD CAE package, Process Parameter Design and Quality Function Deployment: A case study of intelligent materials processing.” Published in Journal of Materials Processing Technology. ARAVIND KUMBAKONAM Mr. Kumbakonam is a graduate student of the Mechanical Engineering Department at the University of Louisiana at Lafayette. He had done his B.S. form Bangalore Institute of Technology, Bangalore, India. His areas of research include Design, Solid Modeling, Artificial Intelligence and Supply Chain Management. SUREN N. DWIVEDI Dr. Dwivedi is the Endowed chair Professor of Manufacturing in the Mechanical Engineering Department at the University of Louisiana at Lafayette. His research interests include Integrated Product and Process Development (IPPD), Concurrent Engineering, Manufacturing Systems, and CAD/CAM. TERRENCE L. CHAMBERS Dr. Chambers is an Assistant Professor and the Mechanical Engineering/LEQSF Regents Professor in Mechanical Engineering at the University of Louisiana at Lafayette. His research interests include design optimization, artificial intelligence. He is a member of ASME and ASEE, and is currently serving as the Vice-President of the ASEE Gulf-Southwest Section. Prof. Chambers is a registered Professional Engineer in Texas and LouisianaBILL BESTMr. Bill Best is currently working as a plant manager at Ash Industries in Lafayette, LA. He is an expert in the field of injection molding having an experience of more than 40 years in this field.