# The Estimation of Product Standard Time by Artificial Neural Networks in the Molding Industry

Authors: Ergün Eraslan
Journal: Mathematical Problems in Engineering
 (2009)

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## Abstract

Determination of exact standard time with direct measurement procedures is particularly difficult in companies which do not have an adequate environment suitable for time measurement studies or which produce goods requiring complex production schedules. For these companies new and special measurement procedures need to be developed. In this study, a new time estimation method based on different robust algorithms of artificial neural networks (ANNs) is developed. For the proposed method, the products that have similar production processes were chosen from among the whole product range within the cleansing department of a molding company. While using ANNs, to train the network, some of the chosen products' standard time that had been previously measured is used to estimate the standard time of the remaining products. The different ANN algorithms are trained and four of them, which are converged the data, are stated and compared in different architectures. In this way, it is concluded that this estimation method could be applied accurately in many similar processes using the relevant algorithms.


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## Body

## 1. Introduction

In recent years, due to
greater competition in the market, determining the exact standard time of
products has become a necessity [1]. It is very difficult to prepare
manufacturing plans and programs, short- and long-term forecasts, pricing, and
the other technical and managerial activities in a company without true
standard time. Unfortunately, direct or indirect work measurement procedures
are insufficient in most cases to determine the exact standard time. For
example, in addition to complex production scheduling and processes as well as
inadequate environmental situations, the cost of time study can be extremely
prohibitive. Thus, existing direct or indirect work measurement methods are not
deemed feasible for use all companies.There are some modern
measuring methods in the literature,  for example, activity sampling, standard data
synthesis, analytical estimation, comparison and prediction, and elementary
motion standards. The methods are not relevant for all studies or possess some
special adversities for application on the model. Therefore, three methods
could be advised for these kinds of problems. One of them, comparison and
prediction method (CPM), requires a value assigned according to the experience
of the one who has applied the time study that is the reason why it is
subjective. As it is a method where all the time effecting factors are not
taken into consideration, CPM is not able to provide accurate results in every
case.In searching for an alternative estimation
method to compare the results of ANNs, regression analysis appears to be a good
choice. When evaluating the results of regression analysis, it seems that there
is a significant difference with the real values. The maximum overshoot value
is significantly high. It is clear that this would present a great problem in
the production planning.In
combating the cost and complexity of the measurement techniques, ANNs have been
recognized as a simple and flexible tool for modeling and system analysis. Over
the last decade, ANN method has been utilized in many industrial fields. Most
of its applications are in marketing, electronic, manufacturing planning and
scheduling, supply chains, expert systems, risk theory, software, energy, food,
and beverage industries. The studies
in what follows can be given as examples in similar applications.Yarlagadda
[2] developed an ANN system to generate the process parameters for the pressure
die casting process in production; Laybeygier [3] studied on a constrained
hybrid NN model to improve the accuracy of conventional option pricing models;
Yahia et al. [4] studied a new model on knowledge processing of expert systems;
Mitchell et al. [5] examined the usefulness of perceived risk theory to
understand customer’s behaviors in package holidays; Brasquet and Cloirec [6]
used classical methods of ANN and that experimental study showed the influence
of specific parameters of cloths through textile fabrics; Lind and Sulek [7]
proposed a NN model approach to prediction of software development completion
time taking into account complexity and variability of worker’s knowledge and
attention span; Joshi and Weng [8] worked on autonomous mental development of
robots with two new learning algorithms in robot’s vision, audition,
manipulation and locomotion; Gestal et al. [9] studied a classification of
apple beverages using ANN with some fuzzy characteristics and supply chain of
foods; Duran et al. [10] submitted the results of analysis on the particulate
matter emissions of biofuels; Celebi et al. [11] used ANNs to approach total
computational time for QW laser technology, and Köker et al. [12] studied an ANN
based inverse kinematics solution on robotics.In this study, an alternative ANN based
indirect standard time estimation methodology is proposed. The method is specially
designed for the selected company in the molding sector using multilayer
perceptrons (MLPs) architecture with different types of learning algorithms. The
six factors that effect the time are determined and the ANN is used to estimate
the standard time based on the variation of these factors. While training the
networks, previously completed time study results of guide products of product
families are used. The accuracy of the model is tested with these standard
times and the divergences are compared among algorithms. Thereby, small
software is developed to estimate the complete product family’s standard time
with decreased cost and time and increased reliability for the cleansing
department.


## 2. The
Structure of the Artificial Neural Networks (ANNs)

The obtained ANN model
is optimized by using various learning algorithms with different network
configurations. A brief explanation of ANNs and the model used is explained
below:ANNs
are biologically inspired computer programs designed to simulate the way in
which the human brain processes information. An ANN is formed from hundreds of
single units, artificial neurons or processing elements connected with weights,
which constitute the neural structure and are organized into layers. The power
of neural computations comes from weight connection in a network. Each neuron
has weighted inputs, transfer function and one output. The behavior of the
overall ANNs depends on the transfer functions of its neurons, by the learning
rule, and by the architecture itself. The weights are the adjustable parameters
and, in that sense, a neural network is a parameterized system. During
training, the inter-unit connections are optimized until the error in
predictions is minimized and the network reaches the specified level of
accuracy. Once the network is trained and tested it can be given new input
information to calculate the output. ANN represents a promising modeling
technique, especially for data sets having nonlinear relationships that are
frequently encountered in engineering. The various applications of ANNs can be
summarized into classification or pattern recognition, prediction and modeling [13–15].There are many types of neural networks for various applications
available in the literature. Radial basis function networks and MLPs are
examples of feed-forward networks and both universal approximators. In spite of
being different networks in several important respects, these two neural
networks are capable of accurately mimicking each other.MLPs are the simplest
and most commonly used ANN architectures which are shown in Figure1 [16, 17]. As
shown in the figure, a MLP consists of three layers: an input layer, an output
layer and one or more hidden layers with previously defined number of neurons.
The neurons in the input layer only act as buffers for distributing the input
signals xi to neurons in the hidden layer. Each neuron j in the hidden layer sums up its input signals xi,
after weighting them with the strengths of the respective connections wji from the input layer and computes its output yj as a function f of the sum, namely(2.1)yj=f(∑wjixi), where f is one of the activation functions used in ANN architectures.Figure 1
General form of multilayer
perceptrons.Training is completed
successfully by optimizing the weights to achieve the desired response with the
use of a learning (optimization) algorithm. A learning algorithm givesΔwji(t) in the weight of a connection between neurons i and j at time t.
The weights are then updated according to the following formula:(2.2)wji(t+1)=wji(t)+Δwji(t+1). There are many learning
algorithms available in literature [14, 17]. The learning algorithms which give
accurate results and used in analysis are briefly summarized below [18]:Fletcher-Reeves
(CGF) algorithm is a second-order
method, which restricts each step direction to be conjugated to all previous
step directions. This restriction simplifies the computation because it is no
longer necessary to store or calculate the Hessian or its inverse [19]. Broydon-Fletcher-Goldfarb-Shanno
(BFG) algorithm is considered to be the best form of quasi-Newton
methods. It uses an update formula derived from the quasi-Newton update of
Hessian [13]. Levenberg-Marquardt (LM) algorithm represents the simplified versions of Newton’s method applied to
the problem of training MLP ANNs. Also it is a well-established numerical
optimization technique with quadratic speed of convergence [14]. Bayesian
Regularization (BR) method is the modification of the LM training algorithm
to produce a well-generalized network. This method can train any network as
long as its weight, inputs, and activation function [18].Although one of the selected algorithms mentioned
above could be converged a sample data, there is no convergence guarantee on other
data sets. Several trials with different learning algorithms and with different
network configurations are necessary to obtain a better performance. If the
most suitable network configuration is A× B × C × D × E, this means that the
number of inputs is A, the number of outputs is E, and the number of neurons is
B for the first hidden layer, C for the second, and D for the third,
respectively. This is the common use to designate the architecture [18]. (The
numbers of input and output layers are not stated in the following sections.
The structure is given with the hidden layers only.)Before training, the input and the output data
tuples are normalized between 0.0 and 1.0 in order to ensure the learning performance
since the normalization is an essential step to improve the training process of
ANNs. After completing the training process successfully, a test process is
carried out for one output parameter.


## 3. Time Estimation Using ANNs

Time and work
measurement studies in recent years have focused on comparing alternative
methods, balancing the work force, determining the number of machines that a
worker can simultaneously control, manufacturing planning and scheduling,
obtaining the machine and worker’s performance standards and workshop
performance evaluations and calculation of the direct cost of workmanship. Some
of the recent studies in the literature are outlined below:Koelling and Ramsey [20]
studied on the effects of multimedia in developing and applying in a work
measurement methods, Cohen et al. [21] examined successful integration of
automatic speech recognition (ASR) into industrial systems and found that the
generation of time standards is a time-consuming process that has always slowed
the work measurement task and increased costs, and that by using ASR, it
demonstrated a 70% time reduction, and Freivalds et al. [1] tested the effect
of work measurement and design systems in customer satisfaction on future
engineers using the 100 industrial engineering programs in US. Praszkiewicz [22]
presented the application of ANNs in small lot production in machining with a
purpose that is similar to this study. Apart from the fact that there are some
studies on the literature which uses genetic algorithms or optimization
techniques in the manufacturing systems/flexible manufacturing system processes
and they are used for forming materials.To manage the
activities of an establishment effectively, the exact standard time must be
known [1]. Activity sampling, standard data synthesis, analytical estimations
and elementary motion standards methods are developed to indirect measurement
but these methods are not applicable for the determination of standard time in
every company or of every product.In a process, the
activities that are the components of processes differ with the facts of the
structure of the work piece, machine used, process and environmental
conditions. In many companies, it is possible to observe that similar
production processes are repeated during manufacturing. It is very expensive or
not possible to measure the time in individual or small serial manufacturing,
small-sized enterprises, rework activities, or production of a new part [23].
In these circumstances, standard time is estimated rather then measured by past
documents but in this estimation method, there is the effect of personal
non-objective decisions. The evaluations are subjective during a comparison
between known and unknown standard time. There are not only the quantitative
differences between the products whose standard time will be estimated but
also the products whose standard time was previously known. The qualitative
facts that have unknown effects on time, but have important impacts on the
duration, need to be considered in time estimation. When the literature is
reviewed, it is seen that these qualitative factors are advised to be taken
into consideration by the one who performs the time study. This proposal is not
an acceptable engineering systematic. It is not possible to explain these
qualitative facts by numerical expressions. Thus, the method can not be
extended beyond a subjective method.In this study, a
different method that can estimate the standard time objectively by ANN is
developed. Different varieties of algorithms are compared. In ANNs, qualitative
factors have already been identified. However, quantitative factors are
expressed based on predefined numerical scales. The structure of the proposed
model is shown in Figure2.Figure 2
The inputs and outputs of
the developed model.


## 4. An Application in a Molding Company

The proposed
alternative method was applied to Turkey’s biggest molding and machining
company. Many types of motor blocks were molded, cleaned and tested in CNC
machines. The manufacturing processes of many products of different types and
dimension are almost the same to operate in various scheduling with 13 machines
in the selected cleansing department. It was reported by the managers that
there is a huge amount of products operating and that if is impossible to
measure the standard time because of product variations and the environmental
conditions.The proposed method can
be outlined as follows:(i)
Firstly, a
production system should be selected which manufactures numerous different
finished products or spare parts.(ii)
The factors that
affect the production time have to be determined. It is necessary to express
these factors numerically. In addition to quantitative factors such as length,
weight or area etc, there are qualitative factors that can not be measured. These
factors can be numerically expressed using Likert scale.(iii)
Then, the
production time of some of these products should be measured. The factors that
effect the time need to be clearly defined and numerically expressed. These
factors are the inputs and the production time will be the output for the
system.(iv)
In the last step,
by changing the network structure and parameters, the proposed network is
trained and tested. If the final network is capable of estimating the time in
the desired error band, the same network can be used to estimate the time of
other finished products, semiproducts or new products. This method is
summarized as a flow chart which is shown in Figure3.Figure 3
A Flowchart of the proposed   method.The most important step
in activating the MLP ANN is the determination of the factors. While
determining the quantitatively and qualitatively factors, some facts need to be
taken into consideration. These are the factors that effect the production
time, have the ease of measurement and do not effect the time linearly. For
example, it may not be known how much an individual factor affects the time or
if it is the only factor. The factors are determined by a decision group which
comprised of the company managers, engineers and experts. After time study, these
factors are determined to effect time. Therefore, they are selected as the
features of the products or semiproducts.The factors can be
divided into two; the ones that are affected by the product specifications and
the ones that are based on production method. Increasing the number of the
factors (input variables) that affect the standard time could interpret the
results better and the affect of each factor on the standard time can be
determined more accurately. Therefore, the most important factors examined, six
possible factors are identified as most determinant to achieving more reliable
results and these are listed as follows.(i)
Volume. The volumes of the motor blocks and spare
parts to be cleaned are changeable. The bigger parts are carried with forklifts
or the biggest ones with cranes which affects the time. The values of the
volumes are factored into the model numerically.(ii)
Weight. As the same with volume factor, carrying the
different weight parts effect the time. The values are also taken into model
numerically.(iii)
Process Number. There are 13 machines available in this
department. Some parts’ operating processes follow 4 machines only but some
others follow up to 13. The greater number of stations means more time need.(iv)
Cylinder Hole Number. The holes on the blocks to be cleaned require
extra time for detailed work. That’s why, the hole numbers are taken into
account.(v)
Arrival Status. The arrival status condition of the blocks and
parts are divided into three types that effect the cleaning time. They are
assigned to model “good” as 2, “moderate” as 1, and “bad” as 0.(vi)
Material Type. The materials that the parts are made of can
affect the cleaning process. However, the materials of all parts are almost the
same and the variation is very limited. Therefore, this factor is omitted.Before training, the input and the output data
tuples were normalized between 0.0 and 1.0 to normalize the data to improve the
training process of ANNs. Then, a test process provides one output parameter
which is the standard time of the work. In the study, time study is done on 50
products. The pre-measured 40 values are used to train the ANN and the randomly
selected remain 10 values are used to test if the ANN is generating the correct
results. The data was exactly measured with time study in the non-ergonomic
area and under unsuitable conditions of the department of the company. The
train and test values of the products are given in Table1.Table 1
The premeasured train and the
test values of the ANNs.
VolumeWeightProcess numberCylinder hole numberArrival statusStandard time310000118144good1094.0265000110143good896.0………………………………………………1150003460good369.71200003760good389.7*1120003360good379.3*980004160good378.0*135002570good365.0*140002370good377.3*140002370good374.0*135003070bad380.3*120002870good368.0*170003470good354.0*110001880moderate430.0*180002260moderate354.0………………………………………………16500850good245.0480830bad83.03200950good293.040001470moderate418.040001870moderate425.0R*andomly selected 10
standard times are used as test values.The MLP ANNs was trained with the four learning
algorithms as explained earlier. The ANN parameters, weights, activation
functions, number of neurons and the levels of the input and output values were
achieved in a small software developed in C++. The training phase takes 15–20 secs
for each run on a Pentium IV 2.4 GHz PC with 512 MB of RAM memory.Several configurations
of ANNs are tested with different network configurations. For the proper training system, the model was
run several times for each different structure beginning with different random
numbers in order to reach the minimum error level. The input and output layers
have the linear activation function and the hidden layers have the hyperbolic
tangent sigmoid activation function. The epoch number is selected 800 for
training. After training, the calculation time is less
then a fewμs in real time calculation. The neural computation is very fast after
training phase. Therefore, ANN can be used effectively in real-time
applications.The result of the two
configurations (i.e., 6× 4 × 2 and 7 × 5 × 3) in order to obtain better
convergence with simpler structure to the pre-measured values in four
algorithms are stated below:Table2 summarizes the comparison between the estimation outputs of algorithms with
previously measured standard time for the structure 6 × 4 × 2 and Table 3
summarized for the 7 × 5 × 3. The required error level corresponds to the total
difference between the outputs of the network to trained inputs and exact
values of the outputs.Table 2
Comparison of outputs of the MLP ANN algorithms (for the hidden layers 6× 4 × 2).
No.PreGeneratedAbsoluteGeneratedAbsoluteGeneratedAbsoluteGeneratedAbsolutemeasur-values bydifferencevalues bydifferencevalues bydifferencevalues bydifferenceed valuesCGF(%)BFG(%)LM(%)BR(%)1389.7384.841.25375.043.76389.350.09401.693.082379.3368.242.92367.053.23379.250.01388.012.303378.0365.793.23364.323.62378.130.04369.812.174365.0304.5816.55314.2013.92364.670.09307.1415.855377.3303.0819.67312.4517.19377.010.08304.5119.296374.0303.0818.96312.4516.46373.650.09304.5118.587380.3507.7633.51473.4124.48380.380.02444.6516.928368.0306.3816.75316.0314.12368.040.01310.5815.609354.0315.6510.83325.977.92353.460.15321.769.1110430.0493.9014.86422.771.68429.730.06463.367.76Train
error2.24E−032.14E−037.77E−042.59E−03Test
error3.17E−031.84E−036.87E−031.85E−03Table 3
Comparison of outputs of the MLP ANN algorithms (for the hidden layers 7× 5 × 3).
No.PreGeneratedAbsoluteGeneratedAbsoluteGeneratedAbsoluteGeneratedAbsolutemeasur-values bydifferencevalues bydifferencevalues bydifferencevalues bydifferenceed valuesCGF(%)BFG(%)LM(%)BR(%)1389.7385.751.01446.0614.46350.3010.11402.123.192379.3368.122.95415.309.49385.631.67388.422.403378.0399.985.82436.4415.46449.6818.96370.511.984365.0315.4213.58278.5323.69187.7748.56306.9815.905377.3307.5518.49273.9527.39115.8969.29304.3619.336374.0307.5417.77273.9526.75115.8969.01304.3618.627380.3365.493.89615.8361.93407.087.04444.7416.948368.0326.8711.18285.5722.40278.5024.32310.3915.659354.0353.950.01314.6111.13395.4911.72321.629.1510430.0477.8111.12453.625.49420.702.16462.887.65Train
error1.85E−031.48E−033.00E−242.59E−03Test
error1.31E−037.96E−031.46E−021.85E−03As seen in Table2, the developed MLP ANN
methods can generate very accurate results with respect to standard time. It
was found that the most suitable network configuration is 6 × 4 × 2 with the LM
algorithm. The maximum difference observed was lower than 0.5 minutes. This
structure gives more accurate results than 7 × 5 × 3. These differences will
not affect the managerial and planning activities and could be used as standard
time easily. Additionally, it is obvious that when the cost of time study is
considered, the results are quite useful and important. In addition, the test
and the train errors are quite low.When Table3 is examined, it is seen that the best results are obtained from CGF
algorithm for the hidden layers 7 × 5 × 3. Although the train and the test
errors are quite low, the results are not satisfactory.The
regression analysis is one of the best alternative methods in the estimation of
the standard time. The regression model is constructed using this pre-measured
values and the standard time is predicted. TheR2 value is calculated as 0.91. In our sample, this
means that 91% of total variation of the values of Y (standard time) is accounted for by a linear relationship with
values of Xi’s   (i=1,2,…,5). The model says that all the
factors are effective on model. The comparatively results of the regression
model is stated in Table 4.Table 4
Comparison of outputs of the regression model.
No.PremeasuredPredicted valuesAbsolute difference (%)valuesby regression model1389.7412.285.792379.3402.166.023378.0374.690.874365.0289.7720.615377.3291.8622.646374.0291.8621.967380.3337.5111.258368.0285.4222.439354.0289.4618.2310430.0354.8517.47The absolute differences of values in Table4 show
that the results are not satisfactory. Although the results are better than
some algorithms of hidden layer 7 × 5 × 3, this method could not be used in the
planning activities.


## 5. Conclusion and Discussions

The proposed MLP ANN model with a very limited calculation time is very
easy to use in the estimation of standard time for any production department of
a company. The companies that do not know the exact standard time of their
products due to measurement difficulties can easily obtain these time values with the benefits of lower cost, shorter time,
and higher accuracy for use in
production planning.The developed method is based on the time affecting factors in similar
processes. After experimentation, the best results were obtained from LM
learning algorithm for the structure 6× 4 × 2 and CGF learning algorithm for 7
× 5 × 3. With the developed model, the total train and the test errors of
standard time estimation was very low.
This accuracy shows that the model is valid with its selected time
affecting factors. In other words, it was evident that the determining factors
are relevant for the proposed model. Moreover, more accurate results could be
obtained by increasing the premeasured data or by trying for several different
architectures. However the results are satisfactory consider the limited data
obtained from the non-ergonomic working conditions.In ANN-based time
estimation method, many factors effecting the time are taken into consideration
and it is possible to deduce the fractions of these factors during time
measurements. More accuracy is required to measure more premeasured values in
order to increase learning progress. The train errors indicate that sometimes these
algorithms are over-trained. When the method is compared with other measurement
methods, the following advantages and the benefits to the industry can be
listed:(i)
does not require long term experience with the product to estimate time,(ii)
minimizes the amount of time study to avoid time and money consumptions,(iii)
gives high accuracy results with a very limited error band,(iv)
considers all the factors that effect time with the pre-measured values
and gives more objective estimations,(v)
allows tracing of factors which have
more effect on the standard time.It is obvious that the method is applicable in many companies
economically. When the company develops one of the appropriate ANN methods,
they can easily calculate the standard time of their products family. The
disadvantages of the method are that it is not easy to apply for every product
or semiproduct whose manufacturing processes are quite complex and it is
difficult to determine the time effecting factors in every case, and
furthermore, determination of the MLP ANN methods, number of hidden layers or
parameter sets are required when using the software. In contrast, it is very
simple and efficient in the fields where applicable.



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*Source: 527452-2009-03-11.xml*