Virtual design and testing of protective packaging buffers

Computers in Industry 54 (2004) 209–221

Virtual design and testing of protective packaging buffers S.W. Lyea,*, S.G. Leea, B.H. Chewb a

School of Mechanical & Production Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore b Motorola Electronics Pte Ltd, 12 Ang Mo Kio, Street 64, Ang Mo Kio Industrial Park 3, Singapore 569088, Singapore Received 1 February 2002; accepted 1 November 2002

Abstract Manufactured products are commonly encased in moulded protective packaging buffers to protect them from damage due to impact shock during handling and transportation. The materials used to fabricate these buffers are well-known and cost-effective but not friendly to the environment. New bio-degradable materials such as paper pulp and starch have emerged as formidable alternatives, but little is known about how to design buffers from them. This paper describes a novel intelligent methodology for the virtual modeling, testing and design of protective packaging buffers. The methodology employs the use of genetic algorithms, finite element model and design routines developed to determine the optimal buffer design. Based on an ANSYSTM finite element model of the buffer, simulated drop tests were performed. The magnitudes of the largest reaction forces for the simulated drop tests as encountered by the model are computed and translated into the highest G value that the buffer can sustain without damage to the product. From the results, a more superior set of buffer designs is then derived with each passing generation. Validation tests were conducted on six different buffer configurations designed to protect six common consumer electrical appliances. The simulated G values were found to differ by a maximum of 11.8% from empirical results. The industrial norm of 10% deviation between empirical and simulated values can easily be realized when further refinements are made to the basic finite element model of the buffer. The findings validate the new methodology in buffer design in particular for new packaging materials where there are only a limited number of explicit or heuristic design rules. # 2003 Elsevier B.V. All rights reserved. Keywords: Virtual design and testing; Protective package design; Computer aided design; Genetic algorithms

1. Introduction The annual world-wide packaging market is estimated to be worth about US$ 500 billion, and expected to grow at an annual rate of between 1.5 and 2.5% [1]. In terms of tonnage, worldwide consumption is expected to increase from about 150 Mtonnes in 1985 to about 270 Mtonnes by 2000; the volume of * Corresponding author. E-mail address: [email protected] (S.W. Lye).

plastic packaging itself is expected to treble in the same period of time [2]. Because of the sheer volume of packaging material consumed, it is imperative that material consumption be minimised not only to save costs, but to protect the environment as well. The most common packaging materials are expanded polyurethane (EPE), expanded polypropylene (EPP) and expanded polystyrene (EPS) which are steam-molded under high temperature and pressure. Of the three, EPE is the least suitable while EPS is the most suitable [3]. However, as these plastics are not environmentally

0166-3615/$ – see front matter # 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.compind.2003.01.001

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benign, biodegradable alternatives have been sought. One such material is a starch-based material, ECOFOAM1 [4]. However, as these bio-degradables are new on the market, not much is known about their material properties and processing characteristics, and so it is difficult to adopt the traditional design methodologies for protective packaging buffers owing to the lack of design heuristic rules. The function of protective packaging is to shield a product from shock due to impact and vibration. The degree of fragility of a product is often measured in terms of a fragility or impact load factor, G. The impact load factor, G, is the ratio of the specific deceleration of the packaged product at the point of impact to the acceleration due to gravity [5]. Thus, a product with a G of 50 is said to be able to withstand a reaction force at the point of impact equal to 50 times that of acceleration due to gravity. In other words, a product with a high impact load factor can endure more severe impact shock and thus requires less cushioning protection. A cushioning curve describes the variation of the impact load factor, G, with the static stresses encountered by a packaging material. The static stress is the ratio of the weight of the product to the cushioning area. A family of such curves may be constructed for a given packaging material of different densities and height/thickness ratios. To determine the amount of cushioning area required in each face of the product, the density, drop heights, and lowest tolerable impact load factor, G, of the product are first determined. From these values, the corresponding maximum permissible static stress can be derived which when divided over the product weight would give the minimum cushioning area required for that face. The cushioning curves are plotted after much experimentation involving free-fall drop tests of the packaged product. Such cushioning curves are not easy to come by for new packaging materials such as Eco-Foam1. Apart from this, the heuristic knowledge amassed over the years is not easily transferred to novice designers and is often lost through resignation or retirement. Although proposals have been made to automate the design of protective buffers [6– 8], the existing industrial practice is still largely manual and heuristics in nature, especially the selection and placing of buffer features, such as ribs and bosses.

2. Artificial intelligence in protective packaging buffer design Common artificial intelligence (AI) techniques are mainly inductive (or self-learning) which is ideal for the design of protective packaging buffers especially in the selection and placing of buffer features, such as ribs and bosses. These techniques include expert (rulebased) systems, neural networks, fuzzy logic, simulated annealing and genetic algorithms. PROPACK is an example of a graphical expert consultation system that selects and positions cushioning features on buffer designs using expert production rules, drawing on a database containing design, material and freight data [6]. While a neural network was developed and trained to design buffers based on 175 proven designs [9] and to estimate the cost of buffers from the design and manufacturing costs of 60 products [10]. However, neural networks require a vast amount of training data sets that is lacking for new packaging materials. Like neural networks, although fuzzy logic can solve multicriteria problems even if the data sets are imprecise or incomplete, the technique is still very dependent on the existence of data sets. As for simulated annealing, this is an optimisation procedure that seeks to minimise the cost function. It describes the mechanism by which atoms settle down from a chaotic state into a more ordered state during cooling [11]. The procedure starts with a trial solution and a small incremental step in a certain direction. If the cost is reduced, then this trial solution is retained as the new trial solution. The rate of convergence depends on the initial values of the critical parameters. Simulated annealing has been applied extensively to multi-objective, multiple constraint shape and topology optimisation [12,13]. Although simulated annealing is a promising technique for the design of protective packaging buffers, difficulties may arise in establishing the appropriate initial values for critical set of parameters especially for new materials. Genetic algorithms are a stochastic, evolutionary search/optimization technique that has its roots in biological evolution, natural selection and genetic recombination [14]. Genetic algorithms (GAs) work on the principle of survival of the fittest in a population of potential solutions, called chromosomes, which are made up of genes. The parameter of interest, e.g. a protective buffer design, is coded as a chromosome.

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Genetic algorithms have been applied extensively to shape optimisation problems [15–19] and since feasible buffer designs are shape optimised, GAs was therefore adopted in this work.

3. Virtual modeling and analysis of protective packaging buffers This paper describes an intelligent design methodology for the virtual modeling, testing and analysis of the protective packaging buffers. The methodology employs the use of genetic algorithms that was written in ANSI ‘‘C’’ Language, finite element model developed on the ANSYSTM version 5.4 [20] and some proprietary design routines for deriving of the optimal buffer design.

4. Implementation of genetic algorithms Fig. 1 shows the iterative design cycle, beginning with inputting two data types. The first data type concerns buffer design data relating to its desired or targeted G value, likely drop height and the design parameters or space of the product. In the design space, the weight distribution of the product, the regions required for protection and support on a given face are considered. On the weight distribution, different weights are assigned accordingly to the nine rectangular segments (see Fig. 2) in a given face. It was found that segmenting into nine portions would provide reasonable accuracy and adequate for the analysis [8]. For example, consider a 14 in. television set, whose weight of 12 kg is shown ‘‘projected’’ onto the bottom face. Since it is known that 90% of the weight of the television set is evenly distributed over the front of the set, where the picture tube is located, segments 7, 8 and 9 are each assigned 3.6 kg. The remaining 10% of the television weight i.e. 1.2 kg is evenly distributed among the remaining six segments. As for the regions of protection, ribs/bosses would be placed on them to cushion off the impact whereas the regions of support would have square/rectangular bosses placed on them mainly to provide stability owing to possible imbalance in the buffer placement. For the second data type relating to genetic algorithm computation, population size, number of gen-

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erations, crossover rate, mutation rate and time step values are to be entered. Population size concerns with the number of buffer designs to be evaluated per generation or iteration. In this work, the final population size of 70 would need to undergo through 100 generations. Crossover and mutation are genetic operators used to randomly permutate or change the order of search direction to ensure global convergence. Typical value ranges for crossover and mutation in this work are between 0.95 to 0.99 and 0.002 to 0.006, respectively. After the input phase, an initial population of design solutions is to be randomly generated. Each buffer design solution is represented in the form of a chromosome coded in binary format. In this work, each chromosome contains 30 genes or parameters of (230) bits long. Each chromosome contains information about the number of considered sections per face (up to a maximum of four), size of the supporting ribs/bosses, the orientation, length, width and thickness of each primitive used to construct a buffer design (see Fig. 3). Typically, most buffer rib/boss profiles can be constructed based on the primitive shapes of rectangle, L, T and ‘þ’. To account for the manufacturing and design considerations as well as limit the search space, user defined boundaries could be placed onto each of the design parameters. In generating the population, an objective function needs to be defined. As buffers are usually designed to offer a level of impact protection, one approach is to maximise the objective (fitness) function in that the simulated G value is required to satisfy a targeted G value. The objective function would compose of two (2) aspects; the difference, X, between the targeted or designed G value and the simulated G value, and the other would be the weightage placed on meeting the requirement. The evaluation would be made based on selected sections (up to four) for a given buffer face as most buffers do not usually have more than four sections of interest or sensitive areas to contend with. F¼

1 jTargetGValue 

P4

i¼1 ðwi

 Gi Þj

where TargetGValue is the designed G value for that area in that face; Gi the simulated G value of the designated area i; wi the weightage difference for the designated area i. The sum of the weightages ranges between 0 and 1; | | penalty check to ensure difference

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S.W. Lye et al. / Computers in Industry 54 (2004) 209–221 Initial Data Entry

Design Space

Population Size

Weight Distribution of Product

No of Sensitive Areas

Number of generations

Level of Protection

Drop height

No of Support Areas

Initial population from data file containing previous results from earlier runs

Random Generation of Chromosomes to fill population size

Encoding Chromosomes

LOOP Repair module where unsunitable gene is regenerated

Check 29 genes in chromosome for validity

Loop back to decode next chromosome in population until end of population list is met Non-Linear Finite Element Analysis

Chromosome Fitness value determined (Objective Function)

Fitness value stored in temporary memory

If end of population save fitness value and chromosomes in sortresults.txt file

Selection Method: Roulette Wheel

Cross over of Chromosomes

Mutation of Chromosomes

Offsprings form new generation. The 10 fittest chromosomes in last generation is brought forward to next generation

Fig. 1. Flow chart of implementation of genetic algorithms.

While < Total Generations

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Fig. 2. The weight distribution of a television set as projected onto the top face.

is within a certain tolerance limit. If incurs, a very large value would be assigned. From the objective function, one can infer that if the difference between the values approaches to zero,

F, the fitness value will tend towards infinity. Given the objective function, a randomly generated chromosome must meet a certain fitness value or range of values.

Chromosome Representation

Group 1 Number of sensitive areas (1 parameter)

Group 2 Size of supporting bosses (2 parameters for each of four possible support areas (8))

Group 3 Orientation of Primitive shapes (4 parameters)

Group 4 Primitive Thickness (1 parameter)

Group 5 Length of Primitive (2 parameter for each sensitive area (8))

Group 6 Width of Primitive

(2 parameter for each sensitive area (8))

4 different orientations for each sensitive area

Sensitive Area One

Sensitive Area Two

Sensitive Area Three

Sensitive Area Four

Fig. 3. The genes of a chromosome representing a buffer design.

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After validating the genes in each of the chromosomes, a simulated shock test analysis on each buffer design is conducted. In a flat drop test, it is assumed that the buffer does not dis-integrate upon impact and that the product strikes the ground on the same face all the time. It is also assumed that the applied impact force or load, Fa, occurs over a very short span of time, which can be sub-divided into equal time intervals. The size of these time intervals influences the rate of convergence to a solution and length of computational time. In order to study the time-dependent response of the protective buffer to an impulsive load, a non-linear analysis of the problem based on the finite element method was adopted. This is because the behaviour of a 3D protective buffer impacting a surface is more likely to exhibit non-linear displacements, forces, stresses and strains that vary dynamically with time. To perform the drop simulation, the first step is to create the buffer model geometry and mesh that constitute of various elemental types and properties. In this work, ECOFOAM, a new biodegradable packaging material, modeled using ANSYSTM, a finite element software, was found to have the following material property values, derived from trial experiments, as stated in Table 1. After the geometry model and mesh of the buffer have been created, the contact and target surfaces in each face need to be established. The contact and target surfaces refer to the surface subject to impact and the surface that is subjected to impact respectively (see Fig. 4). Besides that, the time of contact, load prediction, sticking and normal contact stiffness need to be specified.

Table 1 Material property of test specimen Material property

Weight (isotropic material)

Buffer (orthotropic material)

Rigid body (isotropic material)

Young modulus (MPa)

9  1010

9  1010

Density (kg/m3) Poisson’s ratio

User defined 0.3

X: 1:5  105 ; Y: 1:5  105 ; Z: 7:15  106 53.1 0.3

2313.33 0.3

After the model parameters have been established, the loading, motion and boundary conditions to be applied on the buffer surface are to be made. The first solution step is to perform a pseudo static load analysis. The primary objective is to establish the initial set of conditions in preparation for the dynamic analysis. To simplify the analysis, all transient effects, inertia and damping are ignored. The time interval for this first step therefore tends to be small and the value derived would be inconsequential compared to the overall time analysis. The second solution step examines the dynamic analysis of the impact by simulating the loss of energy due to internal material damping owing to the transient effects of inertia and structural damping when impact forces are applied to the buffer model. The amount of damping to be applied onto the model is dependent on the natural frequencies and mode shapes of a structure. In this work, the damping factor to be applied is usually equaled to 1% of the first fundamental frequency of the modal analysis. Table 2

Fig. 4. The three parts of the FEM model.

S.W. Lye et al. / Computers in Industry 54 (2004) 209–221 Table 2 Dynamic analysis parameter setting Weight Element type Contact time/load Prediction Real constants Sticking contact stiffness Normal contact stiffness

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Table 3 Steps in performing the shock test simulation Buffer

Rigid body

Solid 45 Solid 92 Contact 49 Reasonable T/L increment 1:43  108 9:53  105

tabulates the set of dynamic parameter setting used in the analysis. To commence the drop test simulation, ANSYS1 begins with the default time step value, increasing it gradually to the maximum value, until the contact face impacts the target face. Once impact is detected, ANSYS1 uses the smallest time step to compute the response of the model of the buffer to the impact. The time step is then increased until the maximum value is reached. The magnitudes of the largest reaction forces for the simulated drop tests as encountered by the model were then computed and translated into the highest G value that the buffer could sustain without damage to the product. The steps in conducting the simulation are tabulated in Table 3. Each chromosome or buffer design would have its own computed G value derived from the drop test simulation. From the computed G values, the methodology would then compare them with the targeted G values. In this research, a candidate chromosome with a high fitness match with the target G value will be considered for replication. Using genetic operators such as crossover and mutation, chromosomes with high fitness values are selected to spawn child chromosomes

Step Step Step Step Step Step

1 2 3 4 5 6

Step Step Step Step

7 8 9 10

Create the model geometry and mesh Identify the faces that are experiencing impact Define the rigid target face Define the deformable contact face Set the element key options and real constants Define/control the boundary conditions of the rigid target face Apply necessary boundary conditions Define solution options and load steps Solve the contact problem Review the results

in the population of the next generation. In practice, the crossover rate used is usually much higher than the mutation rate because it is generally accepted that the crossover operator produces new chromosomes which are fitter than those produced by mutation. Based on recommendations [21,22], the ranges of crossover and mutation values of between 0.95 and 0.99, and 0.002 and 0.006 were used in this work, respectively. The process is repeated over many generations. With each succeeding generation, fitter chromosomes (i.e. better solutions) emerge. This iterative design-test cycle is repeated until the best buffer design is generated. In this work, a reasonable buffer design solution can be derived after iterating for about 20–50 generations. As an illustration, a protective buffer whose product face has two areas of interest is designed to sustain 30G. In this example, area 1 is more sensitive and critical than area 2 in that it could only sustain an impact shock not exceeding 30G while area 2 should not exceed 50G. Penalty values would be incurred if these limits are exceeded. Assuming area 1 has a weightage of 90%, it can be seen from Table 4 that

Table 4 The results of weighting sensitive areas differently Sample Arr.

Sensitive area 1

Sensitive area 2

Aggregate (G)

Fitness value with limits (G)

Fitness value without limits (G)

1 2 3 4 5 6 7 8

45 70 28 25 55 100 28 25

20 15 70 28 10 20 45 37

42.5 64.5 32.2 25.3 50.5 92 29.7 26.2

0.00 (41.7%) 0.00 (115%) 0.00 (7.33%) 0.213 (15.7%) 0.00 (68.3%) 0.00 (307%) 1.000 (1.01%) 0.263 (14.5%)

0.08 (41.7%) 0.029 (115%) 0.455 (7.33%) 0.213 (15.7%) 0.049 (68.3%) 0.016 (307%) 1.000 (1.01%) 0.263 (14.5%)

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only sample combinations 4, 7 and 8 satisfy the requirements. On the other hand, sample combination 3 has a relatively high fitness value of 0.455 if the 70G value exceeding the maximum desirable 50G in area 2 was not taken into account. Thus, buffers can be better designed thereby catering to specific needs by establishing constraints and ‘‘weights’’ placed onto the objective function.

5. Case studies The GA enhanced buffer design methodology was validated by applying it to the six most commonly used consumer electronic appliances. From a study conducted, it was found that the basic design configuration of a particular appliance is very similar despite it being made by different manufacturers. Take for example, a television set. The front portion of the television set where the picture tube is usually located is always a sensitive region containing delicate/fragile components. For a desktop personal computer, the sensitive region is at the upper left corner of the chassis where the shock-sensitive components like hard disks, CD-ROM and disk drives are to be found. It is, therefore, important to ascertain the weight distribution of these products and their corresponding

sensitive regions for each of the faces. A brief description of the product characteristics can be found in Table 5. To capture the product characteristics for protective package design, this is generally expressed by specifying the weight distribution, regions of sensitivity and fragility values for that face. This is done using a nine grid or rectangular segmented map, in Fig. 2, for a given product face. Presently, the user needs to indicate or input the weight distribution, areas of sensitivity and corresponding fragility values on the set of grids of that face. The user interface could be further improved by directly extracting such data from the CAD solid model for protective package design. This would require incorporating fragility values in the key components, designing based on actual weights and highlighting of sensitive areas during the CAD solid model creation of the product assembly. For comparative and evaluation sake, the work would illustrate an example based on calculations performed on the top face. The projection of the weight distribution of all six products is illustrated in Fig. 5. The shaded segments in the figure represent the regions of higher weight concentration having distinctly different weight distributions. The desired (target) impact load factors for each of the six electronic products are set at 60G but dropped at different

Table 5 Description of product characteristics Test

Product

Characteristic description

1

Video cassette recorder/player

2

Washing machine

3

Television set

4

Vacuum cleaner

5

Refrigerator

6

Computer

The VCR has three sensitive areas; the front where the display panels are located, the mid-region containing the video recording ‘‘heads’’ and the power supply at the back. The metal alloy video heads in the mid region accounts for the bulk of the VCR’s net weight. The washing machine has two sensitive areas; in the front where the controls are located and at the back where the motor is located. The motor accounts for the bulk of the weight of the washing machine. The television set has two sensitive areas. The first sensitive area at the front houses the electrical controllers and picture tube, while the antennas and output connectors are located in the second sensitive area at the back. The electrical controls are used to adjust the TV picture, e.g. brightness, contrast and channel tuning. These electrical connectors, commonly located at the top left corner of the bottom face, are more sensitive to shock than the picture tube. As the picture tube is made of glass and the rest of the TV set is made of plastic, the picture tube accounts for the bulk of the total weight of the TV set. The sensitive area and center of gravity (CG) lie at the back of the vacuum cleaner where the motor is located. Two other sensitive areas are located at the sides of the vacuum cleaner where the accessories are placed when the unit is packed into corrugated boxes. The weight and sensitive regions of the refrigerator are located at the back where the motor and heat dissipating fins are to be found. Three sensitive regions were found to adequately protect the refrigerator. As mentioned earlier, the sensitive region of a desktop computer is at the top left corner of the chassis where the hard disks, CD-ROMs and floppy disk drives are to be found. Three sensitive areas are defined on the face where they are located.

S.W. Lye et al. / Computers in Industry 54 (2004) 209–221 Test 1 (Video Cassette Player) Weight = 11KG Drop height = 0.68M Buffer Design 1

Test 2 (Washing Machine) Weight = 24KG Drop height = 0.53M Buffer Design 2

Test 3 (Television) Weight = 24KG Drop height = 0.53M Buffer Design 3

Test 4 (Vacuum Cleaner) Weight = 17.5KG Drop height = 0.57M Buffer Design 4

Test 5 (Refrigerator) Weight = 24KG Drop height = 0.53M Buffer Design 5

Test 6 (Personal Computer) Weight = 11KG Drop height = 0.68M Buffer Design 6

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Fig. 5. Weight distribution (shown shaded) of six electrical appliances.

heights. Besides the weight distributions, areas of protection and support would also need to be identified. For example, cushioning ribs are only placed at

the areas of protection (shaded regions) in test case 6 (see Fig. 6) for a personal computer. This would result in a skewed feature distribution that does not ensure

Fig. 6. Regions of Interests for the various appliances.

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S.W. Lye et al. / Computers in Industry 54 (2004) 209–221 Table 6 GA parameter values for various buffer designs

Configuration 1

Configuration 2

Population size Cross over rate Mutation rate Generations

Buffer designs 1, 3–6

Buffer design 2

70 0.99 0.02 100

45 0.99 0.02 100

Table 7 Number of chromosomes with fitness value 1.00 Configuration 3

Configuration 5

Configuration 4

Configuration 6

Buffer design

Chromosomes with fitness value of 1.00

1 2 3 4 5 6

13 4 15 19 1 21

Fig. 7. The six rib configurations/primitive cross sections.

stability and may affect the cushioning performance. Thus, where the weight concentration is less intense as depicted in the non-shaded regions, additional supports or bosses are needed to provide for stability. Fig. 7 shows the rib configurations to be used in the virtual modeling analysis.

6. Evaluation of case studies To evaluate the effectiveness of the methodology, the simulated results of the various buffer designs were compared with experimental ones.

solutions. Besides this, it was also generally found that at the seventh generation, the larger the population size, the greater the number of feasible design solutions. By feasible buffer design solutions, this refers to the number of chromosomes with a fitness value of 1.00 (see Table 7). For example, in the seventh generation, buffer designs in test 2 had only 4 very fit chromosomes while buffer designs in tests 1,3, 4 and 6 had more than 10. This was probably due to the small population of chromosomes from which buffer designs generated in test 2 was generated.

8. Experimental setup of the drop tests 7. Buffer design simulations To obtain the feasible buffer designs for each of the six electrical appliances, an initial population for each of the appliances was first generated. Due to the varying weight distributions, areas of protection and support, drop heights and rib shapes, the values of the initial GA parameters are different for varying appliances (see Table 6). Each of these populations needs to undergo through seven generations of crossover and mutation to derive the feasible buffer

The six electrical appliances was simulated using a wooden test block with nine apertures into which aluminum blocks were inserted to simulate any of the six weight distribution patterns (see Fig. 8). The six protective buffers were fabricated by gluing EcoFoam1 planks together using a latex proprietary to National Starch & Chemical Company, the manufacturers of Eco-Foam1. The wooden test block, secured by buffers, was packed into a corrugated box that was then dropped from a pre-determined height using a Lansmont Model 65/81 Shock Test Machine shown in

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9. Results and discussion 9.1. Experiment 1: accelerometer placed at the impact face centre of the applicance

Fig. 8. The test rig with aluminum blocks installed.

Fig. 9. It consists of a steel base, two solid chromeplated guide rods, a table and hoist positioning system. The corrugated box was placed on the table and raised to the pre-determined drop height. The table was then released. The shock signals generated upon impact were measured by a single-axis accelerometer with a range of 1000G, and a resolution of 0.01G, mounted by petrol wax onto the center of the buffered test block, and transmitted to the computer via a signal conditioner-cum-interface module. Average experiment drop readings based on two sample tests were obtained from two locations namely (a) with the accelerometer was placed at centre of the appliance’s face and (b) at the areas of protection or interest. Besides that, repeated drops were also performed.

Table 8 shows the experimental and simulated (ANSYS) G values for the six electrical appliances. Feasible buffer solutions that are designed and simulated to withstand a 60G force were obtained. The simulated solutions were all able to have a fitness value of 1 that have an average target value of 60G for all the nodes on the ANSYS contact surface. Based on the experimental results from fabricated buffers, all tests for the first drop registered impact values of between 26.3G (56.2%) and 59G (1.7%) that were less than 60G. The large deviations, except for test 1, between the targeted and experimental results can be attributed owing to the location where the accelerometer recorded its readings and the location where the largest impact forces were being transmitted through the areas of protection. This was evident in test 1 where the accelerometer was placed at the segment (area of protection) where the cushioning features were located as shown in Fig. 5. Another observation made concerns force localization. For a drop height of 0.53 m, product weight of 24 kg but having different weight distributions, it seemed that test 2 (washing machine) experienced a higher impact force than test 3 (television) and 5 (refrigerator) where the weight distributions were placed diagonally opposite one another than on the same side. The findings imply that for off centred product, it would be best to concentrate the load on one side of the product where appropriate cushioning can be focused with less impact being made in other areas. The problem of generalised buffer design based

Table 8 A comparison of the experimental and design (ANSYS) G values

Fig. 9. The Lansmont Model 65/81 Shock Test Machine.

Buffer design

Experimental

ANSYS

Deviation (%)

1 2 3 4 5 6

59.0 46.8 30.2 38.8 26.3 43.4

60 60 60 60 60 60

1.7 22.0 49.7 35.4 56.2 42.8

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Table 9 Experimental shock test results for buffer designs 3–6 against simulation ANSYS1 results—accelerometer mounted on areas of interest Buffer design

Experimental

ANSYS

Deviation (%)

3 4 5 6

50.1 62.0 51.6 51.0

56.8 57.0 49.1 51.3

11.8 þ8 þ4.88 0.62

on average G value per face can be more accurately addressed. This would reduce over design of buffers, a common practice in industry. 9.2. Experiment 2: accelerometer placed at the area of weight distribution Table 9 reveals the experimental and simulated (ANSYS) G values for the six electrical appliances at the various areas of weight distribution for test buffer designs 3–6. Unlike experiment 1, the experimental results were recorded based on the accelerometer mounted in the vicinity of the interest and the simulated results are on the average G value of the summation of G force experienced in all the nodes on the areas of interest. From Table 9, it can be seen that the variances between the experimental and simulated ones range between 11.8% and þ8%. Such variances are quite acceptable by typical industrial standards of about 10% for such application. The findings reveal that the genetic algorithm approach can model quite accurately the drop impact behaviour of a product.

10. Conclusion An intelligent methodology for the virtual design, modeling and testing of 3D protective buffers is proposed, which employs genetic algorithms to optimize key buffer design parameters, and the finite element method to analyse the resistance of the buffers to impact shock. The genetic algorithm (GA) enhanced design methodology assumes a certain distribution of the product weight over its external faces and the regions of protection where fragile or delicate components may be present and impact protection is to be

made. With each succeeding generation (iteration), superior buffer designs are evolved. The GA enhanced design methodology is not dependent on any design rules and was therefore found to be ideal for the design of protective buffers made from new materials. Preliminary tests conducted found that buffers made from biodegradable corn starch, a new material, can be designed adopting this proposed approach. This would result in reducing the overall buffer design cycle time, saving of materials and minimizing the dependency on expert knowledge. Six case studies were conducted to validate the GA enhanced design methodology for common consumer electrical appliances i.e. refrigerators, television sets, washing machines, video recorders, vacuum cleaners and personal computers. The tests revealed that the experimental and simulated G values recorded over the areas of interest were quite comparable to the results of the simulated shock tests, with a variance of not more than 11.8% which is acceptable by industry. Experiment tests conducted indicate that the recorded impact force tended to be localised. The GA enhanced design methodology is able to simulate more accurately the impact force registered in an area of interest and thereby design buffers more optimally as compared adopting a generalised approach of specifying an average G value over the entire face.

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[20] ANSYS user’s manual Revision 5.1., Swanson Analysis Systems, Houston, PA, 1998. [21] Z. Michalewicz, Genetic algorithms þ data structures ¼ evolution programs, Springer-Verlag, New York, 1994. [22] P. Collard, C. Escazut, Genetic operators in a dual genetic algorithm, Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, Piscataway, NJ, USA, 95CB35878, 1995, pp. 12–19. Sun-Woh Lye is a Professor and Vice Dean from the School of Mechanical and Production Engineering, Nanyang Technological University, Singapore. His research interests are in integrated product/package design, computer aided intelligent design and decision support tools, optimization layout and cushioning material charaterisation and modeling.

Lee Siang Guan, Stephen, P.E., is an Associate Professor from the School of Mechanical and Production Engineering, Nanyang Technological University, Singapore. His research interests are in design methodologies, assemblability, intelligent scheduling and collaborative and concurrent engineering.

Chew Boon Hock, Edward is currently working as an information technology engineer in Motorola, Singapore. His research interests are in computer networking and databases and computer aided engineering applications.