玩具上蓋的注射模具設(shè)計-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-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,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 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)品開發(fā)與時間
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電腦化的智能制造過程工具。制造過程工具是一種有助于制造商解決與注塑相關(guān)問題計算機(jī)程序。這些問題包括：開始成型的時間，非優(yōu)化的周期時間和控制不良的注塑過程。制造過程中工具將有助于機(jī)器運行(不一定是專家), 優(yōu)化和控制注塑成型過程。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)計指導(dǎo),被處理的所有塑料的重量的32% 通過注入成型機(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)計及成型過程的設(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程。成型過程是一個相當(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。這樣一個復(fù)雜的過程要求用學(xué)歷與個人的經(jīng)驗建立和完善。這些獨立的成型工藝控制是在試驗和誤差的基礎(chǔ)上, 這通常是很耗費時間的。這些控制成型過程的方法主要依靠操作者的直覺和一些"經(jīng)驗法則"，這些“經(jīng)驗法則”是操作者在過去一段時間,用不同的材料,壓力,溫度和時間設(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程知識工程模塊(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,周期時間和制造模塊,這些是相互關(guān)聯(lián)的。本文主要集中在制造模塊 ，其中涉及開發(fā)一種智能調(diào)節(jié)工藝的過程工具,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庫
圖2：數(shù)據(jù)流程圖
優(yōu)化器的注塑過程從這方面的專家捕捉不確定性知識, 并且以查看表小冊子等形式使用確定性知識。優(yōu)化器是寫在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和猜測進(jìn)行微調(diào)。
基于知識設(shè)計模塊
一個專門的系統(tǒng),或以知識為基礎(chǔ)的系統(tǒng),定義為"模式和相關(guān)程序, 在一個特定的區(qū)域, 某種程度上的解決問題是相等于一個人類專家"，各類專家系統(tǒng)是有用的,都有各自的優(yōu)點和缺點?；谥R系統(tǒng)的"基于規(guī)則"這一項就是最常用 。它基本上由包含基于知識的含有“IF-THEN”的語句，稱作規(guī)則。
ikem該項目涉及建立一個專門系統(tǒng)包含一套if-then規(guī)則，這些規(guī)則是專家在知識工程師的幫助下建立的。最重要的兩個問題是這一基于規(guī)則專家系統(tǒng), 獲取知識和知識表達(dá)的可靠性。
知識的獲取
這是知識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)展過程的瓶頸。 知識的獲取,主要是靠知識工程師的技術(shù)。 其主要目的是提取策略或人類專家經(jīng)驗法則,并把它移交到知識庫。必須慎重獲取知識的過程,因為它直接影響到后面的知識表達(dá)計劃。這里有知識獲取的兩個基本類型:
1. 知識直接從人類專家獲取(不確定性知識)
2. 獲取知識是從先前的案件中的關(guān)系,查找表(確定性知識)。
智能工具制造業(yè)正在研制捕捉從這方面經(jīng)驗得到的不確定性的知識,以及更多確定性知識的小冊子形式的關(guān)系,查找表等。
拉菲特路易斯安那州大學(xué)一個學(xué)生知識工程師一直在工業(yè)灰努力工作,這主要是注塑廠。 知識工程師訪問的灰產(chǎn)業(yè)方面的人類專家。然后,他組織從邏輯上提取這個數(shù)據(jù)，這種從專家提取的知識是以的啟發(fā)形式或更精確的"經(jīng)驗法則" 規(guī)則。專家開發(fā)這些啟發(fā)形式或經(jīng)驗法則和從他在這個領(lǐng)域的經(jīng)驗。這些"通則"作為指導(dǎo)方針用于得到最優(yōu)化產(chǎn)品質(zhì)量成型過程。
一組典型的例子"經(jīng)驗法則" 或啟發(fā)形式的發(fā)展在計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
這個規(guī)則給我們一對另外的規(guī)則,以確定選定材料是無定形或晶體。 運用這六項規(guī)則專家系統(tǒng)可以計算所需鉗噸位。
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.
知識表達(dá)
知識表達(dá)是知識工程過程的第二階段, 知識以編碼形式編入知識庫。啟發(fā)形式是由知識工程師從人類專家在知識庫用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。一個單獨的材料數(shù)據(jù)庫系統(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 VisualBasicLinkage
注塑模收縮,從材料數(shù)據(jù)庫索取線性流速
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,時間和距離。 每個產(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越軟, 直到他們分解。"
?晶體材料, "這些材料并沒有軟化階段,但直至它們被加熱到某一溫度點是才開始融化，如果吸收了更多的熱量就會分解。"
考慮到非晶和晶體材料的不同性質(zhì), 我們在注塑機(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。
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高品質(zhì)塑料零件，降低成型周期時間。優(yōu)化的模具溫度有助于獲得更短的成型周期時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。
時間:
?冷卻時間: 它是塑料零件在型腔成型到安全取出所需的時間。 優(yōu)化冷卻時間有利于縮短成型周期時間。
距離:
?模具開模行程:開模距離為了使零件安全的從模具上取出。優(yōu)化模具開模行程有利于縮短成型周期時間。
結(jié)論
在本文討論的過程制造工具已在在拉法路易斯安那州大學(xué)開發(fā)。 當(dāng)完成了這一工具時，它將能作出初步優(yōu)化工藝參數(shù)值, 這對于起始注塑成型過程是關(guān)鍵的。為得到個人想要的效果,經(jīng)營者可以用自己的直覺和猜測對這些參數(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)計的基礎(chǔ),第二卷。 2,社會制造工程師。
2. c-模具設(shè)計指南。 -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 和自動化大會。 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IMM’s Troubleshooter解決100個注塑問題,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."注塑過程設(shè)計智能系統(tǒng)" 1996年第三屆亞洲及太平洋經(jīng)濟(jì)合作會議關(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,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)品和過程開發(fā)(IPPD),并行工程,制造系統(tǒng)和計算機(jī)輔助設(shè)計/制造
TERRENCE L. CHAMBERS
Chambers博士是一個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,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長。Chambers是一名在得克薩斯州和路易斯安那注冊的專業(yè)工程師。
Bill Best
Bill Best是在拉菲特灰產(chǎn)業(yè),LA的一個廠長經(jīng)理。他對注塑成型有經(jīng)驗超過40年,他是這方面的專家。
收藏