Innovation in management system by Six Sigma An empirical study of world-class companies

Introduction
 

Presently companies are challenged due to technology advancement, customer expectation evolution, and short product life cycles. Innovation plays a pivotal role for gaining competitive advantages. Innovation in management system is believed as one of the crucial competitive advantages for enhancing and sustaining business performance. Innovation in management system is defined as improving the way people are managed and works are organized as a response to the changing environment. However, due to its complexity, it is not easy to innovate. There is a lot of difficulty in understanding the main factors, such as management strategies, degree of company integration, and resources. Hence, innovation needs a systematic approach to be understood and effectively applied.

On the other hand, Six Sigma, as a management tool, provides a systematic approach for enhancing performance. The emergence of Six Sigma has become one of the major developments in management practices. Six Sigma has been widely accepted as a management model that will guarantee in attaining competitive advantage if implemented successful.

Impact of Six Sigma on management system

The goal of innovation is the positive changes whereby a company improved. This impact of Six Sigma is derived from two systematic processes: the define, measure, analyze, improve and control process (DMAIC) problem-solving methodology and the design for Six Sigma (DFSS) approach. DMAIC is more focused on problem solving of existing condition in organization for improving performance and cost reduction. Meanwhile, DFSS tends to be more proactive by designing new products, services, and business processes. Therefore, it is believed that DMAIC methodology allows for incremental innovation because it is based on existing condition to promote an improvement. The DFSS approach is more of a radical innovation by redesign or design new products, services, and business processes, according to customer requirement and expectations.

This paper uses the two Six Sigma processes to analyze the application of Six Sigma on innovation in management system. This section discusses the positive impact of Six Sigma in management system based on Osada’s management system model. This model classified management system into driver, enabler, and performance, as follows:

 Driver: Company direction, vision, mission, objectives, strategy, and organizational   expectations.

 Enabler:  Soft infrastructure, stock resources, process, and flow resources.

 Performance: Output, outcome, and financial result.

Sharpening leader development method

 Six Sigma training was an ironclad prerequisite for promotion to any professional or managerial positions in the company and a requirement for any award of stock options. In addition, all professional, supervisory, and managerial employees must be at least green belt (GB) trained and have done a project. The same as GE, Du Pont has required BB and GB certification as a requirement for promotion to the ranks of management, while Caterpillar utilized a structured process to select high-potential employees to become Six Sigma BB. After two-year assignments, mostly BBs are promoted to higher level positions within Caterpillar operations and included in the succession management process. Honeywell was also proactively assigning high-talent employees to important Six Sigma project, as part of developing the next generation of leaders.

Improving the effectiveness of training. This is done by linking training process and outcome of the project with financial outputs.

Improving employee spirit. This is done by connecting the reward system (financial benefit and career promotion) with the project accomplishment to energize people on Six Sigma initiatives. The promotions are mostly awarded for team leader, while financial benefit is mostly awarded for team members.

^.

 Enhancing the quality of communication

Six Sigma has a standardize methodology and a clear step of project accomplishment. Thus, it is easy to be understood, followed, and shared within the business units. The successful and ongoing projects are being recorded by software for tracking Six Sigma-related progress and information dissemination. This system is able to store and share the collected data among related units in company wide.

Upgrading production process.

 Six Sigma projects allow companies to significantly upgrade the production process with less investment.

^.Improving inventory utilization.

 Increasing inventory utilization and production speed by reducing variations and removing wasted steps from manufacturing

^.Enhancing elimination of non-value-added process.

 One of Du Pont projects, namely the solutions for document processing in Major Litigations, has eliminated ten of the 14 steps previously required. As a result, the unit cost of processing one page was reduced by 53 percent.

Providing significant financial benefit. 

 The Six Sigma has driven undoubtedly contributions to profitability improvements

Uniqueness of Six Sigma as innovation tool in comparison with TQM

There are differences between Six Sigma and TQM in areas such as financial performance, time frame, objective, origin, infrastructure, and methodology. A Six Sigma project is highly related to strategic planning based on priorities to changes the daily activities of an organisation. Hence, it decreases the complexity and provides focus on specific and strategic problems to solve. Six Sigma has defined two types of projects which are DMAIC projects for providing corrective action to existing product, services, and business processes and DFSS project for generating which provides more radical approach to create new value. Moreover, each type of project is conducted through a difference methodology according to the nature of the problem to enhance possibility for success. On the other hand, TQM does not provide an infrastructure like Six Sigma to decrease its complexity. However, TQM countermeasures the complexity through kaizen, a continuous improvement process, that is smaller scale, and people based by rotating PDCA methodology. TQM uses total employee participation to accumulate these improvements in company wide. Thus, in total will provide significant improvements.

Therefore, the following discussion is intended to clarify the strength of Six Sigma as an innovation tool in management systems compared to TQM. The following findings have been identified from the Six Sigma characteristics which can provide additional critical distinction between Six Sigma and TQM.

(1) Disseminating commitment

At the early time of implementation, Six Sigma leverages firstly to early adopters. This method has low resistance and provides an experience to learn from mistakes. This step is part of change acceleration process (CAP) step. According to the CAP model, the effectiveness (E) of change initiatives is equal to the product of the quality initiatives (Q) of the technical strategy and the acceptance (A) of the strategy. The prerequisite of CAP is the involvement and skills of the leader. By utilizing CAP tools, the leader can deliver message of change to help decreasing the resistances of people through the following steps:

(1) creating a shared need;

(2) shaping a vision;

(3) mobilizing commitment;

(4) making change last;

(5) monitor process; and

(6) change systems and structures.

(2) Sustaining spirit

Six Sigma starts by focusing on solving strategic problems, which has a direct linkage with strategic plans within a limited time by assigning their very best people on a full-time basis. The success stories of Six Sigma projects, especially on the beginning of deployment, have an impact on strengthening opinion that Six Sigma really works. Since Six Sigma has also direct linkage with financial criteria, it is easy to communicate its benefit and tie some percentage of financial reward to the achievements. In comparison with TQM, especially at the very beginning implementation, TQM did not treat improvements of the project base, thus it has no specific time accomplishment. TQM does not assign specific elite team members, since their main focus was to gain a total employee involvement Six Sigma has a unique way of introducing projects and innovation in management systems.

Conclusion

The Six Sigma initiative has a comprehensive impact on its driver, enabler, and performance cluster, such as directing the organization way, enhancing the effectiveness of strategic project management, establishing a culture of data-driven approach, sharpening the way to develop leader, and so on. Six Sigma has defined two types of projects which are DMAIC projects for providing corrective action to existing product, services, and business processes and DFSS project for creating new value which provides more radical approach.

In comparison with TQM, Six Sigma has at least two additional critical differences:

(1) disseminating commitment; and

(2) sustaining spirit.

These aspects are critical for innovation in management system as to deal with people’s resistance through enthusiastic early adopter to pioneer the deployment of Six Sigma. As a result, successful initial projects help to clarify for others the real business value of Six Sigma. In sustaining spirit Six Sigma creates a milestone to maintain interest and commitment for long-term activities through accomplished projects and provides direct financial reward for successful projects.

References

Anderson, R., Eriksson, H. and Torstensson, H. (2006), “Similarities and differences between TQM, Six Sigma, and lean”, The TQM Magazine, Vol. 18 No. 3, pp. 282-96.

Antony, J. (2004), “Some pros and cons of Six Sigma: an academic perspective”, The TQM Magazine, Vol. 16 No. 4, pp. 303-6.

Antony, J. (2008), “What is the role of academic institutions for the future development of Six Sigma?”, International Journal of Productivity and Performance Management, Vol. 57 No. 1, pp. 107-10.

Antony, J. (2009), “Six Sigma vs TQM: some perspectives from leading practitioners and academics”, International Journal of Productivity and Performance Management, Vol. 58 No. 3, pp. 274-9.

Antony, J., Banuelas, R. and Kumar, A. (2006), World Class Application of Six Sigma: Real World Examples of Success, Elsevier, London.

Azis, Y. and Osada, H. (2009), “Six Sigma impact on innovation management system and its comparison with TQM”, Prosperity through Quality – The ANQ Way, Proceedings of the ANQ Congress, Tokyo, Japan, pp. 998-1007.

Becheikh, N., Landry, R. and Amara, N. (2006), “Lessons from innovation empirical studies in the manufacturing sector: a systematic review of the literature from 1993-2003”, Technovation, Vol. 26, pp. 644-64.

Black,  K.  and  Revere,  L.  (2006),  “Six  Sigma  arises  from  the  ashes  of  TQM  with  a  twist”,

International Journal of Health Care Quality Assurance, Vol. 19 No. 3, pp. 259-66. Brue, G. and Launsby, R.G. (2003), Design for Six Sigma, McGraw-Hill, New York, NY.

Caterpillar Annual Report (2002-2008), available at: www.cat.com/cda/layout?m¼294196&x¼7 (accessed 25 June 2009).

Chakrabarty, A. and Kay, C.T. (2007), “The current state of Six Sigma application in services”, Managing Service Quality, Vol. 17 No. 2, pp. 194-208.

Christensen, C.M. (2002), “The rules of innovation”, Technology Review, Vol. 105 No. 5, pp. 32-8.

Cronin, M.A. and Cleotilde, G. (2007), “Understanding the building blocks of dynamics systems”,

System Dynamics Review, Vol. 23 No. 1, pp. 1-17.

Dean, J.W. and Evans, J.R. (1994), Total Quality Management, Organization and Strategy, West, St Paul, MN.

Deming Prize Committee (2007), “The Deming prize guide for overseas”, Union of Japanese Scientist and Engineers, JUSE, Tokyo.

Dow Annual Report (2003-2008), available at: www.dow.com/financial/fin_reports/index.htm (accessed 20 July 2009).

Ehigie, B.O. and McAndrew, E.B. (2005), “Innovation, diffusion, and adoption of TQM”, Management Decision, Vol. 43 No. 6, pp. 925-40.

A heuristic approach to meet geometric tolerance in High Pressure Die Casting

                        Engineering Research Paper

Author:

G. Campatelli , A. Scippa

Department of Mechanical Engineering and Industrial Technologies, University of Firenze – Italy, Via di S. Marta, 3, 50139 Firenze, Italy

Abstract

In High Pressure Die Casting (HPDC), geometrical distortions usually happen during the cooling phase, due to the reduced cooling time and the high thermal gradient inside the product itself. This phenomenon affects most the thin walled products. The usual die design practice considers only the linear shrinking of the product during the cooling as a consequence of the difficult to take in account also the geometrical deformations. In this essay a simple finite element design strategy that allows the designer to improve the die shape is presented. The proposed approach uses an automatic iterative optimization tech-nique based on a heuristic algorithm, which could be easily applied to most of the Finite Element (FE) commercial software: the basic concept of the method is simply to move the nodes defining the die surface in the opposite direction to the error due to the cooling phenomena. An automotive component has been selected as a case study: the aim was to improve the planarity tolerance of a planar surface of the casted product. Results show the efficiency of the proposed method that, despite its simplicity, is able to provide an optimal solution with a small number of iterations.

Introduction

For many metal components, with perhaps the exception of some powder forming process, the part can rarely be finished exactly to the required final tolerance in a single forming operation. Thus, in general, a forming operation is carried out to produce a ‘near-net-shape’ product, which is subsequently brought into the required tolerance by a finishing operation.

In aluminum casting, High Pressure Die Casting (HPDC) is used when high productivity and good quality of the rough product is required. Both productivity and reduction of finishing operations (neat shape or near net shape processes) allow a heavy reduction in the product manufacturing cost.

.

The mechanical properties of a die-cast product are related principally to the die temperature, the metal velocity at the gate, and the applied casting pressure. The integrity of the cast component is affected by: the combination of die’s thermal profile, mold filling capacity of the molten metal, geometrical complexity of the parts and cooling rate during die casting. The pressure applied to the casting during solidification is crucial to the production of high integrity parts. Porosity reduces with increasing intensification pressure, but enlarges with increasing casting velocity. If these parameters are not controlled adequately, various defects within the finished component will be generated.

The main drawbacks of HPDC are the porosity and the deformation of the product during the cooling phase of the material. During the solidification phase, the natural casting shrinkage, constrained by the presence of the die, force the component to stretch plastically: this produce residual stresses and complex springback during the cooling phase subsequent to the extraction of the product from the die.

Usually the die’s shape design process takes into account only the thermal shrinkage of the product without considering the geometrical deformation that could arise due to the temperature gradients in the product during the cooling phase. Most of the die designers start from the geometry of the final product and ‘‘scale’’ this geometry using the thermal shrinking coefficient of the material using the company knowhow or a very expensive and time consuming trial-and-error approach. This usually takes place during the die try-out stage in a manufacturing plant, when the die has to be repaired or re-manufactured. The method is highly dependent upon the skill, experience, and luck of those carrying out the procedure.

The general aim is to provide some guideline for the building up of Finite Element (FE) simulation of the cooling phase for a HPDC product and to present an algorithm for the automated optimization of the die geometry, using commercial FE software. It is a heuristic method, based on the difference between the work piece after cooling and the desired shape. No parameterisation of the die geometry is needed: since no parameters are used to describe the shape of the die geometry, it can be modified in an arbitrary way without the restriction of the design space spanned by design parameters.

Proposed approach

Assuming that the deformed shape due to cooling can be predicted accurately, there still remains the problem of how to use such results to obtain a suitable die design able to meet the required tolerances. That is, the cooling predictions allow ‘‘forward’’ analysis, while a ‘‘backward’’ analysis is needed to obtain, from these results, an optimized die design. The proposed approach is based on iteratively comparing a target part shape with the FE simulated part shape after cooling. The displacement vectors at each node are used to adjust the trial die design until the target part geometry is achieved. Thus, the results from one cooling simulation give the input for the next iteration. By making the geometry changes on the FE description of the work piece, new die geometry is directly obtained for the next cooling simulation.

The proposed strategy is based on four phases:

1. Hypothesis of the thermal map of the product before the cooling phase; this could be obtained from the study of similar product (company experience), from technical consideration or by FE simulation of the casting and cooling inside the die.

2. Hypothesis of the heat transfer parameters during cooling: fluid type and temperature, geometry of the product and its orientation in the cooling bath.

3. Determination of the cold product geometry and residual stresses by means of FE simulation.

4. Iterative design process to define the optimized die geometry able to meet the tolerances required for the final product.

Setting up the FE simulation

Distortions are basically the result of the temperature gradient inside the piece, the constrained shrinkage during the solidification phase, and the high thermal shock due to the cooling phase: therefore it is necessary to perform a coupled thermal stress analysis. The thermal stress calculation procedure is as follows:

For a coupled thermal-stress problem, important topics are:

– the time integration scheme;

– the appropriate integration time step;

– defining both mechanical and thermal properties, using a mechanical constitutive model that allows entry of a thermal coefficient of expansion and mechanical properties that are function of temperature;

– realistic boundary conditions;

In a coupled thermal-stress analysis, the thermal time step is independent from the mechanical time step. The rate of mechanical motion, mechanical deformation, and rate of heat transfer must all be considered in selecting an appropriate time step.

 Heat transfer coefficients

To set up a realistic simulation is necessary to provide the heat transfer coefficients for all the phases of the cooling (opening of the die, transport of the product to the cooling bath, etc.), and the thermal map of the work piece at the beginning of the cooling process. This last one can be evaluated knowing the heat transfer from the material to the die, which is usually cooled by water circulation. Some foundry software can provide such information. The heat transfer can be calculated knowing the orientation of the product during transportation and cooling in the bath and the external thermal condition. The approximations of the heat transfer coefficients and of the thermal map constitute the greatest source of error in the simulation.

 Conclusions

The method for die geometry compensation has shown to produce a die shape which minimizes the product geometrical error such as planarity. This approach could highly reduce the errors that are generated by the simple die design at nominal geometry or compensated only for uniform shrinkage. The method is characterized by a very short response time : only few cooling simulations are needed and the FE model developed has a very high convergence rate. Moreover the algorithm developed to obtain the optimal die geometry, has proven to be very efficient and consists in an iterative procedure that eliminate the trial-and-error optimization by the use of a Matlab_ script that automatically run a sequence of iterations in order to get the final die shape. The different stages in the iteration, i.e., deviation calculation and result mapping are solved by programs written solely for these purposes.

The main advantages of the method are:

– The high generality, so it can easily be applied for modeling other manufacturing processes (e.g. extrusion, welding, sandcasting).

– The ease of implementation that make it easily applicable to most of commercial FE software.

– The excellent convergence rate of the FE simulation, which results in very short response time.

– It is possible to separate the filling phase optimization and the tolerance optimization.

The critical parameters for obtaining a coherent solution are the heat transfer coefficient and the initial thermal map

And the most careful care has to be used for the evaluation of these characteristics in order to obtain a realistic FE simulation and it is most likely that the method will work on a variety of different shapes.

Design and manufacturing of Die Casting Dies

INTRODUCTION

Die-cast components are being used increasingly in the automobile, aerospace, electronic and other industries after Doehler manufactured diecasting product by using Al alloys in 1915.Diecasting is not suitable for a small quantity production because of the high cost. But it has various advantages such as manufacturing products of complex geometry and thin-wall sections, high productivity, smooth surface of cast and excellent dimensional accuracy. Therefore, diecasting process is developing sharply with establish thousands of diecasting machines.

Diecasting die design consists of the selection of materials for diecasting alloys, the application of shrinkage, and the casting plan including designs of cast, gate, runner and overflow. While manufacturing die design is highly demanded for high precision and shorts the date of delivery, in most of the case, it is designed by determining product geometry. So it is needed experienced know-how and experts who have a skill for manufacturing die. In result, such diecasting die design has much economical losses and wastes of time by trial and error method. Therefore, designs of automatic shape of die and to makes a 3D modeling for diecasting die is done by CAD/CAM system.

Diecasting die design includes a process of determining geometrical figure of the product and dies and selecting condition for forming products. Mechanical and external quality of the ultimate die casting product is determined by interaction of each variables of the design. Therefore the die designer has to design after due consideration of the problems that can be caused at the time of production. The traditional die design has been carried out a designer who experienced for many years and followed a process of trial and error that happens in the time from designing product and die to producing the ultimate product. Such processes cause the term of production to extend and have the prime cost rise. As a result, there have been attempts to reduce them in various ways.

One of them is construction of system that assists initial step developing diecasting product and die design CAD system. The other is finding formability of product and mechanical defects before manufacturing process and considering the countermeasure in advance by simulating diecasting process.

 Generally speaking, die design still depends on experience, due to lack of analytical ability in die and melting metal flow and heat transfer. Current shop floor practice uses the trial-and-error method to determine die design, when new molds are used. This method is costly and results in a lot of wasted casting. To solve this problem a study was done on the runner and gating system to simulate the molten metal flow and to analyze the pressure and metal movement during the casting process.

Although some finite element analysis software is capable of analyzing the melting process and flow conditions of the products (work piece) under various injection conditions, they are only giving some limited suggestions and information to die design.

Diecasters usually carry out the diecasting experiments before producing new casts. At the diecasting stages, the runner-gate part is always repeatedly corrected, which leads to a lengthened processing time and increased processing cost. The diecasting die design should consider component system factors, such as runner, gate, over flow and air vent. A large amount of experience is essential in manual assessment and if the design is defective, much time and a great deal of efforts will be wasted in the modification of the die. Thus human negligence should be minimized.

DESIGN OF DIE CASTING DIE

Design is done in three stages i.e. cast design, Die layout design and Die generation

1) CAST DESIGN

The cast must be designed because the dies can be generated from the cast in diecasting die design. The cast design consists of three parts; cast input, material selection and application shrinkage

a)      Cast Input

In cast input part, the cast modeling in commercial modeler as IGES file format is input. The input cast is located fitting viewpoint from desirable direction. And the parting surface should be determined for detailed die design for diecasting.

b)     Material Selection

After inputting the cast in this system, the material of the cast should be selected. Most of the diecasting processes are used to shape or form parts made from both ferrous and nonferrous metals, principally aluminum, magnesium, and zinc.

c)      Application Shrinkage

Next, the cast should be applied to shrinkage. In establishing dimensions for cavities, an allowance must be added to the dimensions specified for the part to be cast, for shrinkage of the casting metal. The shrinkage allowances normally used are: 0.005in. per inch for zinc alloys, 0.006in. per inch for aluminum alloys, and 0.007in. per inch for magnesium alloys. Shrinkage allowances for copper alloys vary from 0.008 to 0.018 in. per inch, the allowance used depending largely on foundry experience with the type of alloy being cast. The above values are influenced by several variables, primarily size and shape of the casting. For castings that have irregular surface contours, die sections and cores are designed to prevent free shrinkage in specific areas. Die sections or cores so designed are often called “shrink resistors”.

For close-tolerance castings, it may be necessary to make an allowance for the expansion of the die cavity caused by the difference in the temperature at which it was made and the operating temperature. In general, the calculation of shrinkage allowances at room temperature is illustrated below equation.

                                         DL = b (T - 20 ) - a (t - 20 )

2) DIE LAYOUT

 In the process of die layout design, the gate, runner and overflow are designed for constructing dies. In this system, the die layout design is divided four parts; gate design, runner design, runner-gate design and overflow design.

    (A) Gate Design

In gate design part, the properties are input for gate design and the gate sectional area is determined by filling speed and time. The main function of the runner and gating system is to deliver molten metal passed into the mold into all section of the molten cavity. First, casting material is selected and cavity volume is calculated. Once

mechanical properties of cast are input and filling speed is selected, the gate area is generated.

The cross-sectional area of thegate Ag is shown by  below equation

             A_g Q_a /V_g*t_g   ……………………………………………………………..(1)

The filling time of die cavity tg is assigned to be that a fraction of solidus comes up to 70 %.

Heat capacity per unit volume, K is given by

K  = [L + C_p  × ( T_m-T_s )] × p × S ×X                 ……………..(2)

The flow rate heat per unit time, q' is given by

q=x ×S (T_m-T_a) /X                       ……………….(3)   

From the equation (1) and (2), filling time, tg can be obtained.

t_{g =} (K/q) ×0.7

Generally, the gate thickness, t is selected properly, which is between 0.5 and 3.0 mm, considering rimming etc. The width of gate L is determined by following equation from gate area calculated by equation (3).

L= A_g/t

   (B) Runner Design

Runners should be designed with a stepped increase in cross-sectional area from the gate via branch runners to main runners, and on to sprue or biscuit, to promote uniform metal velocities and uniform ratios of cross section to perimeter. The cross-sectional area of a feed runner is equal to, or less than, the sum of the cross sectional areas of the branch runners. On runners of different lengths feeding identical parts, the longest runner should be given a slightly larger cross section. A runner that converges into a long gate should increase in cross section toward the feed runner, to keep metal velocities as uniform as possible. Theoretically, these runners should taper out at the ends to the thickness of the gate, but practical considerations require a compromise. Turns and leading edges should have generous radii and should be smoothly blended where thickness or width changes occur. Runners should have a reasonably smooth surface finish. A thick runner will not solidify fast enough for the cycling rates generally used. A thin, flat runner will cause the metal to lose too much heat before it enters the gate. As a compromise, a standard width-to-depth ratio of 1.6:1 to 1.8:1 , side angle is 10~20 inch  each corner radius is over 6mm. has been adopted. This ratio provides for reasonably fast cooling without excessive heat loss during cavity filling. And then the shape of runner is selected from database. The width and depth of runner varies with the volume of metal to be injected into the cavity.

    (C) Runner Gate Design

The part of connecting gate and runner can be designed and assembled with cast in runner-gate system. To obtain “gate-controlled fill” of the die cavity, the cross-sectional area of a runner must be larger than of the gate. However, for minimum heat loss, metal velocity in the runner feeding a gate must be as high as possible. For these reasons, a runner-to-gate area ratio of 1.15:1 to 1.5:1 is generally used. Oversize runners will increase metal losses and remelting costs.

   (D) Overflow design

The placing of overflows is generally predictable, and their location and size are designed into the gating system of a die. However, the addition or relocation of overflows is the most frequent cause of failure in the 15% of dies for which first-shot success is not achieved. The weight of metal in overflows should be added to the part weight in calculating the total weight of metal flowing through the gate.

Airvent on the die faces usually lead out of overflows. The total of the cross-sectional areas of vents should be at least 50% of the gate area. Self-cleaning of vents can be ensured by making vents 20 – 30mm thick, 0.1 – 0.15mm length. Venting may also be provided by small grooves cut across the parting plane of the die, or by the clearance around the ejector pins or movable cores and slides. The shape of the finished component determines the design of a diecasting die. But there are a number of aspects involved in the design and sizing of a die, which can have an influence and important bearing on die life.

3) DIE GENERATION

The cavity block can be generated by geometry recognition and rule base. After generating the cavity block, the type of dies is determined according to the geometry of the cast. In this system, the types of dies are set up in two types. Thus, One of them is the case that the cast is located at one side of dies and the other is the case that the product is divided by parting surface. Here, because of difficulty of detailed geometry recognition user can determine the selection of die. Consequently, the cavity block is generated and the type of dies is selected, and ultimately the dies can be generated.

DIE MANUFACTURING AND PREPARATION

Dies are typically machined from tool steel. Dies last between 15,000 and 500,000 castings, depending on the casting temperature of the alloy. Dies for aluminum, a moderate-temperature alloy, have an expected lifetime of 100,000 castings.

Dies are a large capital investment, especially for small firms, and their cost must be distributed over a long use phase. Similarly, the environmental investment in die-making can be amortized over the 100,000 casting lifetime. A die for 170 cm3 of casting requires a shot size of 370 cm3, including overflow wells and feed system. Removing that much metal from 800 cm3 of stock requires 4300 kJ.

Lubricants are used both in making the die and preparing the die for each casting. Oil-based cutting fluids are the most popular for machining, such as when making steel dies for

casting. They frequently include naphtha, and, despite being diluted to 95% v/v with water, release more volatile organic compounds than their water-based counterparts. To make the representative die considered above would require 0.04 L soluble oil cutting fluid and 0.8 L water diluents. Both oil-based and water-based lubricants are commonly applied to the die and plunger tip before casting. On the die, lubricants act as releasing agents.

Despite the seemingly small volumes, oil-based lubricants are a major source of air releases from die casting facilities, as reflected in the Environmental Protection Agency’s Toxics Release Inventory (EPA TRI) for aluminum die casting, standard industrial code (SIC) 3363. Actual emissions vary with the composition of the lubricant, but typically volatile organic compound (VOC) emissions are associated with oil-based lubricants.

Products containing alkylbenzene sulfonate, 1,2-epoxypropane, alkylether, and poly(oxyethylene) nonyl phenyl ether are commonly used (SCE, 2001). Water-based lubricants have lower VOC emissions, but may be associated with increased hazardous airborne particle (HAP) emissions. Cumulative VOC emissions are around 1 kg per tonne of produced casting (Roberts, 2003). Throughout the die casting process, because of the proprietary nature of the input compounds and the wide variety of reactions that can occur, the exact composition of VOCs is not as closely monitored or regulated as the total emission of VOCs. VOCs include any compound of carbon, excluding carbon monoxide, carbon dioxide, carbonic acid, metallic carbides or carbonates, and ammonium carbonate, which participates in atmospheric photochemical reactions (US GPO, 2003).

In some foundries, dies are preheated to reduce thermal stress and extend die life. This is most common in dealing with high-temperature copper and magnesium alloys (US DOE, 1999).

ADVANTAGES OF USING DIE CASTING

Some of advantages of using die casting are as follows

1.   Excellent dimensional accuracy (dependent on casting material, but typically 0.1 mm for the first 2.5 cm  (0.005 inch for the first inch) and 0.02 mm for each additional centimeter (0.002 inch for each additional inch).
2.  Smooth cast surfaces (Ra 1–2.5 micrometres or 0.04–0.10 thou rms).
3.   Thinner walls can be cast as compared to sand and permanent mold casting (approximately 0.75 mm or 0.030 in).
4.  Reduces or eliminates secondary machining operations.
5.   Rapid production rates.
6.   Casting tensile strength as high as 415 MPa.
7.   Casting of low fluidity metals.

APPLICATION OF DIE CASTING

Some of the application die castings are as follows

1.  Automotive parts
2.  Lighting
3.  Electronics
4.  Aircrafts
5.  Boats
6.  Hardware
7.  Speakers
8. Appliances

REFERENCES:

1)                  J.C. Choi*, T.H. Kwon**, J.H. Park**, J.H. Kim**, C.H. Kim***

Dept. of Mechanical Design Engineering, ERC for NSDM at Pusan Nat'l University Graduate School, Dept. of Precision Mechanical Engineering at Pusan Nat'l University Dept. of Mechanical Engineering, Dong-eui University

2)                  Life cycle analysis of conventional manufacturing techniques: DIE CASTING

By Stephanie Dalquist and Timothy Gutowski Massachusetts Institute of Technology

3)                  Casting product–process–producer compatibility evaluation and improvement  

M. M. AKARTEy and B. RAVI*z

International Journal of Production Research, Vol. 45, No. 21, 1 November 2007, 4917–4936

4)                  Simulation-enabled casting product defect prediction in die casting process M.W. Fua* and M.S. Yongb  ,International Journal of Production Research , Vol. 47, No. 18, 15 September 2009, 5203–5216