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(kāi)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(kāi)題,玩具,注射,模具設(shè)計(jì),塑料,注塑,nx,三維,19,cad,開(kāi)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 Industries
Abstract
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 world’s 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(kāi)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. It’s 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 non–deterministic 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 = crystalline
THEN clamp tonnage = 2.6 ton/inch2
Rule 3: 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/inch2
l 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 = amorphous.
Rule 6: 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 don’t 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 Optimizer
Considering 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 IMM’s 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 Louisiana
BILL BEST
Mr. 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.
注塑模的智能制造工具
機(jī)械工程部，路易斯安那大學(xué)
摘要
本文介紹了正在在拉法路易斯安那州大學(xué)研究的旨在建立一個(gè)電腦化的智能制造過(guò)程工具。制造過(guò)程工具是一種有助于制造商解決與注塑相關(guān)問(wèn)題計(jì)算機(jī)程序。這些問(wèn)題包括：開(kāi)始成型的時(shí)間，非優(yōu)化的周期時(shí)間和控制不良的注塑過(guò)程。制造過(guò)程中工具將有助于機(jī)器運(yùn)行(不一定是專家), 優(yōu)化和控制注塑成型過(guò)程。從而獲得最大限度地生產(chǎn)效率,特別是注塑機(jī) 。
注塑模是目前世界上最常用的方法用于制造復(fù)雜的商業(yè)塑料部件并具有良好的尺寸公差。依照 C-模子設(shè)計(jì)指導(dǎo),被處理的所有塑料的重量的32% 通過(guò)注入成型機(jī)器,制造塑料的注入成型是最重要的制造工藝之一。最終的成型質(zhì)量則主要取決于材料的類型, 模具設(shè)計(jì)及成型過(guò)程的設(shè)置。一旦材料和模具確定,零件質(zhì)量基本上取決于成型過(guò)程。成型過(guò)程是一個(gè)相當(dāng)復(fù)雜的,涉了及許可變的工藝參數(shù),如壓力,溫度和時(shí)間設(shè)定 。為了提高零件質(zhì)量,最大限度地提高注塑機(jī)生產(chǎn)能力，這些工藝參數(shù)必須被設(shè)定。這樣一個(gè)復(fù)雜的過(guò)程要求用學(xué)歷與個(gè)人的經(jīng)驗(yàn)建立和完善。這些獨(dú)立的成型工藝控制是在試驗(yàn)和誤差的基礎(chǔ)上, 這通常是很耗費(fèi)時(shí)間的。這些控制成型過(guò)程的方法主要依靠操作者的直覺(jué)和一些"經(jīng)驗(yàn)法則"，這些“經(jīng)驗(yàn)法則”是操作者在過(guò)去一段時(shí)間,用不同的材料,壓力,溫度和時(shí)間設(shè)定 得到的。
本文概要列出了目前正在拉法洲路易斯安那州大學(xué)進(jìn)行的涉及基于注塑成型過(guò)程知識(shí)工程模塊(ikem) 智能化發(fā)展的研究。ikem有不同的模式,即:句法分析,模具設(shè)計(jì),周期時(shí)間和制造模塊,這些是相互關(guān)聯(lián)的。本文主要集中在制造模塊 ，其中涉及開(kāi)發(fā)一種智能調(diào)節(jié)工藝的過(guò)程工具,所謂的" 優(yōu)化器"。圖1和2顯示了不同ikem單元的數(shù)據(jù)傳遞。
圖1：基于工程塑料注射成型的智能知識(shí)庫(kù)
圖2：數(shù)據(jù)流程圖
優(yōu)化器的注塑過(guò)程從這方面的專家捕捉不確定性知識(shí), 并且以查看表小冊(cè)子等形式使用確定性知識(shí)。優(yōu)化器是寫(xiě)在VisualBasic中,將協(xié)助機(jī)器操作建立, 優(yōu)化和控制注塑成型工藝,從而最大限度地生產(chǎn)效率,特別是注塑機(jī)。如圖3所示,優(yōu)化器幫助制造商設(shè)定成型機(jī)的初步優(yōu)化工藝參數(shù)值。為得到最大效率，這些參數(shù)值可以由操作者用自己的直覺(jué)和猜測(cè)進(jìn)行微調(diào)。
基于知識(shí)設(shè)計(jì)模塊
一個(gè)專門的系統(tǒng),或以知識(shí)為基礎(chǔ)的系統(tǒng),定義為"模式和相關(guān)程序, 在一個(gè)特定的區(qū)域, 某種程度上的解決問(wèn)題是相等于一個(gè)人類專家"，各類專家系統(tǒng)是有用的,都有各自的優(yōu)點(diǎn)和缺點(diǎn)?；谥R(shí)系統(tǒng)的"基于規(guī)則"這一項(xiàng)就是最常用 。它基本上由包含基于知識(shí)的含有“IF-THEN”的語(yǔ)句，稱作規(guī)則。
ikem該項(xiàng)目涉及建立一個(gè)專門系統(tǒng)包含一套if-then規(guī)則，這些規(guī)則是專家在知識(shí)工程師的幫助下建立的。最重要的兩個(gè)問(wèn)題是這一基于規(guī)則專家系統(tǒng), 獲取知識(shí)和知識(shí)表達(dá)的可靠性。
知識(shí)的獲取
這是知識(shí)工程師最初從人類專家提取規(guī)則的辦法。 這一步是勞力密集型,并一直被視專家系統(tǒng)的發(fā)展過(guò)程的瓶頸。 知識(shí)的獲取,主要是靠知識(shí)工程師的技術(shù)。 其主要目的是提取策略或人類專家經(jīng)驗(yàn)法則,并把它移交到知識(shí)庫(kù)。必須慎重獲取知識(shí)的過(guò)程,因?yàn)樗苯佑绊懙胶竺娴闹R(shí)表達(dá)計(jì)劃。這里有知識(shí)獲取的兩個(gè)基本類型:
1. 知識(shí)直接從人類專家獲取(不確定性知識(shí))
2. 獲取知識(shí)是從先前的案件中的關(guān)系,查找表(確定性知識(shí))。
智能工具制造業(yè)正在研制捕捉從這方面經(jīng)驗(yàn)得到的不確定性的知識(shí),以及更多確定性知識(shí)的小冊(cè)子形式的關(guān)系,查找表等。
拉菲特路易斯安那州大學(xué)一個(gè)學(xué)生知識(shí)工程師一直在工業(yè)灰努力工作,這主要是注塑廠。 知識(shí)工程師訪問(wèn)的灰產(chǎn)業(yè)方面的人類專家。然后,他組織從邏輯上提取這個(gè)數(shù)據(jù)，這種從專家提取的知識(shí)是以的啟發(fā)形式或更精確的"經(jīng)驗(yàn)法則" 規(guī)則。專家開(kāi)發(fā)這些啟發(fā)形式或經(jīng)驗(yàn)法則和從他在這個(gè)領(lǐng)域的經(jīng)驗(yàn)。這些"通則"作為指導(dǎo)方針用于得到最優(yōu)化產(chǎn)品質(zhì)量成型過(guò)程。
一組典型的例子"經(jīng)驗(yàn)法則" 或啟發(fā)形式的發(fā)展在計(jì)算卡式噸位是有用的:
規(guī)則1: IF部分壁厚"=0.04英寸
And物質(zhì)=結(jié)晶
THEN鉗重量=2.0噸/ 英尺2*
規(guī)則2: IF部分壁厚<0.04英寸
And物質(zhì)=結(jié)晶
THEN鉗噸位=2.6噸/ 英尺2
規(guī)則3: IF部分壁厚"=0.04英寸
And物質(zhì)=非晶
THEN鉗噸位=3. 噸/ 英尺2
規(guī)則4: IF部分壁厚"0.04英寸
And物質(zhì)=非晶，
THEN鉗噸位=3.6噸/ 英尺2
● 英尺2代表零件在模具中垂直于注塑機(jī)的噴管的總斷面面積
這個(gè)規(guī)則給我們一對(duì)另外的規(guī)則,以確定選定材料是無(wú)定形或晶體。 運(yùn)用這六項(xiàng)規(guī)則專家系統(tǒng)可以計(jì)算所需鉗噸位。
規(guī)則5: IF模收縮,材料的線性流速**<12
THEN物質(zhì)=無(wú)定形.
規(guī)則6: IF模收縮, 材料的線性流速>=12
THEN =物質(zhì)的結(jié)晶.
知識(shí)表達(dá)
知識(shí)表達(dá)是知識(shí)工程過(guò)程的第二階段, 知識(shí)以編碼形式編入知識(shí)庫(kù)。啟發(fā)形式是由知識(shí)工程師從人類專家在知識(shí)庫(kù)用if-then規(guī)則才能作出結(jié)論的專家系統(tǒng)獲取。這些if-then規(guī)則被編在VisualBasic中。一個(gè)單獨(dú)的材料數(shù)據(jù)庫(kù)系統(tǒng)被創(chuàng)建在Microsoft Access中,并且與VisualBasic程序相關(guān)聯(lián)。如圖4所示。
圖4.訪問(wèn) VisualBasicLinkage
注塑模收縮,從材料數(shù)據(jù)庫(kù)索取線性流速
產(chǎn)出
優(yōu)化器給出了影響注塑成型工藝的各種參數(shù)的最優(yōu)化值, 這是在生產(chǎn)中必須要加以控制以確保獲得高質(zhì)量的零件最經(jīng)濟(jì)方式。優(yōu)化器的產(chǎn)出限于四種不同類別,分別是:溫度,壓力,時(shí)間和距離。 每個(gè)產(chǎn)出如圖5所示并且下文有詳細(xì)的討論。
圖5 優(yōu)化器.
溫度:現(xiàn)今生產(chǎn)的大約80%的塑料制品是由熱塑性塑料組成。 熱塑性可以界定為“加熱時(shí)發(fā)生物理變化的塑料材料?！?
不同種類的熱塑性塑料有:
?非晶材料, "基本隨著溫度升高而軟化,并且隨著吸收更多的熱量而變得越來(lái)越軟, 直到他們分解。"
?晶體材料, "這些材料并沒(méi)有軟化階段,但直至它們被加熱到某一溫度點(diǎn)是才開(kāi)始融化，如果吸收了更多的熱量就會(huì)分解。"
考慮到非晶和晶體材料的不同性質(zhì), 我們?cè)谧⑺軝C(jī)上設(shè)定了不同的溫度。
1. 熔體溫度:塑性材料在再注入模具之前必須已被加熱. 優(yōu)化熔體溫度效果在于控制材料的流量,材料分解,脆性和閃點(diǎn)。
2. 筒內(nèi)的溫度:注塑成型機(jī)料筒內(nèi)的前，中，后段設(shè)置不同的溫度。
3. 噴嘴的溫度:設(shè)定機(jī)噴嘴右前方的加熱區(qū)(筒)的塑料溫度。
4. 模具溫度: 必須設(shè)定注塑模具溫度以獲得一個(gè)高品質(zhì)塑料零件，降低成型周期時(shí)間。優(yōu)化的模具溫度有助于獲得更短的成型周期時(shí)間以及更好的零件質(zhì)量。
壓力:
這里有很多在注塑成型機(jī)上需要優(yōu)化的壓力。
1螺絲鉗壓力:壓力保持模具緊緊合在一起來(lái)抵抗注射壓力.最佳的螺絲鉗壓力是能組織模子由于比較少的螺絲鉗噸位閃現(xiàn)。它甚至能節(jié)省能源和避免模具由于高鉗噸位而倒塌。
2. 注射壓力:該項(xiàng)壓力顯示型腔內(nèi)初次填充物(95%)。 最佳注射壓力有助于得到高質(zhì)量,不收縮,不變形的零件, 并且這是容易取出零件。
3.保壓壓力:注射壓力的第二階段,而且通常是填補(bǔ)了剩下的5%的型腔。它通常是保持模具中塑料不被流回注射筒。
4. 背壓: 由塑料施加在螺絲紡錘上的壓力。優(yōu)化背壓有助于取得較好密度及少空隙的零件。
時(shí)間:
?冷卻時(shí)間: 它是塑料零件在型腔成型到安全取出所需的時(shí)間。 優(yōu)化冷卻時(shí)間有利于縮短成型周期時(shí)間。
距離:
?模具開(kāi)模行程:開(kāi)模距離為了使零件安全的從模具上取出。優(yōu)化模具開(kāi)模行程有利于縮短成型周期時(shí)間。
結(jié)論
在本文討論的過(guò)程制造工具已在在拉法路易斯安那州大學(xué)開(kāi)發(fā)。 當(dāng)完成了這一工具時(shí)，它將能作出初步優(yōu)化工藝參數(shù)值, 這對(duì)于起始注塑成型過(guò)程是關(guān)鍵的。為得到個(gè)人想要的效果,經(jīng)營(yíng)者可以用自己的直覺(jué)和猜測(cè)對(duì)這些參數(shù)值可以微調(diào)。
參考資料
1. Douglas M.B..,基本成型:材料選擇和產(chǎn)品設(shè)計(jì)的基礎(chǔ),第二卷。 2,社會(huì)制造工程師。
2. c-模具設(shè)計(jì)指南。 -關(guān)于塑膠工程的資源。
3. Dym. J. B.,注塑模具和成型,第2版, Van Nostrand Reinhold。
4. Xinming Jin, Xuefeng Zhu "工藝參數(shù)設(shè)定使用案例和注塑的模糊推理."第3次世界智能控制 和自動(dòng)化大會(huì)。 2000年6月28日至7月2日,合肥方永明
5. Ignizio J. P.,專家系統(tǒng)的介紹:制定和實(shí)施了基于規(guī)則的專家系統(tǒng), Mc Graw Hill。
6. Bob Hatch, 與Bob Hatch一路同行:通過(guò)IMM’s Troubleshooter解決100個(gè)注塑問(wèn)題,注塑雜志。
7. Mok S.L, Kwong. C.K,Lau. W.S "確定工藝參數(shù)注塑研究綜述" 聚合物技術(shù) v18,n3,1999,p225-236.
8. Shelesh-Nezhad. K, Siores, E."注塑過(guò)程設(shè)計(jì)智能系統(tǒng)" 1996年第三屆亞洲及太平洋經(jīng)濟(jì)合作會(huì)議關(guān)于材料加工的記錄, 1996年11月12日至14日,香港,p458-462
9. Yeung.V.W.S., Lau.K.H., "注塑模,'三模'的CAE軟件包,工藝參數(shù)設(shè)計(jì)和質(zhì)量功能設(shè)定:研究智能材料加工的案例. "刊登在材料加工技術(shù)雜志. ARAVIND KUMBAKONAM
Kumbakonam先生是拉法路易斯安那州大學(xué)機(jī)械工程系的研究生。 他曾在1926年在印度班加羅爾班加羅爾技術(shù)學(xué)院舉行他的B.S.。他的研究領(lǐng)域包括設(shè)計(jì),造型,人工智能及供應(yīng)鏈管理。
SUREN N. DWIVEDI
Dwivedi博士是拉法路易斯安那州大學(xué)機(jī)械工程系天賦教。他的研究興趣包括:集成產(chǎn)品和過(guò)程開(kāi)發(fā)(IPPD),并行工程,制造系統(tǒng)和計(jì)算機(jī)輔助設(shè)計(jì)/制造
TERRENCE L. CHAMBERS
Chambers博士是一個(gè)助理教授以及拉法路易斯安那州大學(xué)機(jī)械工程系機(jī)械工程/ LEQSF教授。他的研究興趣包括優(yōu)化設(shè)計(jì),人工智能。他是ASME 和ASEE 的成員, 并且目前擔(dān)當(dāng)ASEE 海灣西南部分的副會(huì)長(zhǎng)。Chambers是一名在得克薩斯州和路易斯安那注冊(cè)的專業(yè)工程師。
Bill Best
Bill Best是在拉菲特灰產(chǎn)業(yè),LA的一個(gè)廠長(zhǎng)經(jīng)理。他對(duì)注塑成型有經(jīng)驗(yàn)超過(guò)40年,他是這方面的專家。
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