feasibility analysis for implementing gpr scanning system in sawmills for productivity improvement

International Journal of Advances in Management Science, Vol. 4 No. 1, July 2015 1

2169‐7558/15/01 001‐15, © 2015 DEStech Publications, Inc.

doi: 10.12783/ijams.2015.0401.01

Feasibility Analysis for Implementing GPR

Scanning System in Sawmills for Productivity

Improvement Dayakar Devaru1, Nishit Banuri2, Bhaskaran Gopalakrishnan3, Udaya Halabe4, Hasan H Latif*5

1,2,3,5 Department of Industrial Management & Systems Engineering, West Virginia University, WV, USA

4Department of Civil & Environmental Engineering, West Virginia University, WV, USA

[email protected]; [email protected]; [email protected]; [email protected], *[email protected]

Abstract

Non‐destructive scanning technologies have been applied to detect internal defects of logs to optimize log scanning. Research

has been done using many non‐destructive scanning technologies with limited success for real‐time implementation. The main

objective of this research is to identify the issues related to implementation of the Ground Penetrating Radar (GPR) scanning

system in sawmills for purposes of yield improvement.

The paper discusses the logistics on integration of the scanning system into the existing sawmill environment. The durability of

a scanning system in continuously operating sawmills and integrating the scanning system into existing sawmill operations

were identified as major research aspects. The paper also evaluates the economic feasibility of implementing the GPR scanning

system for three different size sawmill classes. Based on conservative estimates of revenue and initial costs, results indicate that

the investment would be feasible for all the three sawmill sizes.

Keywords

Sawmills, Wooden Logs, Cants, Ground Penetrating Radar (GPR), Productivity Improvement, Quality Improvement, Feasibility Study,

Sawmill Economics, Lumber value yield, Scanning, Lumber Grades, Non‐Destructive Testing

Introduction

Productivity is often defined as the ratio of output to input where output means the amount produced or the

number of items produced and input consists of various resources employed such as labor, material, capital and

services. For industries where the cost of raw material constitutes larger part of the cost of finished goods, higher

productivity can be achieved through efficient use of raw material [Khanna 2004]. The process of lumber

manufacturing has not changed over the years; a cylindrical log (raw material) goes in at one end of the mill and

gets transformed into lumber. The sawmill industry includes facilities engaged producing dimension lumber,

boards, timber, ties and woodchips from logs. The purpose of the research is to set boundary conditions for the

design of the commercialized GPR technology for identifying defects in logs for improving manufacturing yield

and to use sensitivity to analyze its economic feasibility. The objective of every organization is to maximize profits

and to continuously improve productivity.

The typical sequence of operations for processing logs into lumber in sawmills include sorting and stacking logs,

metal detection, debarking, head saw operation, re‐saw operation, edging, trimming, inspection and grading,

sorting and stacking lumber, shipping. Figure 1 shows the process flow diagram of a typical sawmill.

Sawmills are becoming more similar to process industries where the production process runs continuously. This

makes it vital for sawmills to utilize raw material as effectively as possible, thus achieving better volume and value

yields. Figure 2 shows the breakdown of sawmill operating costs. Logs account for up to 75% of the total operating

costs [US DOC 2005]. The income in sawmills is from high value lumber boards and from the low value by‐

products such as chips, saw dust and bark. Research is needed to improve the utilization of raw materials and

yield from processes. This research is trying to recognize and address all the latent concerns for executing GPR

scanning system in sawmills.

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Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 3

Pyakurel 2009].

Sarigul et al. [2006] presented a paper describing the importance of detecting internal hardwood defects in order to

improve the lumber grade. CT images were analyzed to locate several internal defects on different hardwood

species. The paper also considers defect dependent post‐processing methods, based on mathematical morphology.

Schad et al. [1996] investigated three non‐destructive techniques such as Sound wave transmission, x‐ray

computed tomography, and impulse radar for locating internal wood defects. The paper discusses the

implementation of each of these techniques and difficulties in interpreting radar scanned data. All three techniques

were able to detect the presence of knots but were not able to detect small areas which are of decay. Of these

techniques, Computed Tomography provided highest resolution for voids, knots and high moisture contents.

Muller [2002] performed tests on timber girders of diameter varying between 0.35m to 0.40m from an existing and

demolished bridge using various non‐destructive techniques such as GPR, gamma ray transmission, and

ultrasound techniques. Of these techniques, GPR was found to be the most reliable method for locating internal

defects. Kabir and Araman [2002] successfully conducted ultrasonic scanning experiments for detecting defects in

wood pallet parts using rolling transducers. The paper describes the importance of characterization of defects for

grading pallet parts. Decay has the greatest effect on the amplitude of the ultrasound signals. Bhandarkar et al.

[1998] presented a paper describing the design and implementation of computer vision based system for lumber

production planning. This paper discusses about identification and classification of internal log defects from

analyzing computer axial tomography images. The mathematical algorithm was capable of providing cutting

pattern that improved yield and grade of resulting lumber significantly. Bhandarkar et al. [2002] described a

prototype computer vision system for identification and localization of defects. The tests performed on CT images

of hardwood logs from White Ash, Hard Maple, Red Oak, and Black Walnut showed 23‐63% gain in value yield

recovery when compared to sawing strategy that did not use internal log defect information. In a related research,

Bhandarkar et al. [2008] described the design and implementation of an automated system for planning and

optimization of lumber production using Machine Vision and Computed Tomography. Optimal log sawing

algorithms were designed and implemented for live sawing, grade sawing and cant sawing. The algorithms

showed significant increase in value yield recovery but the execution times were not suitable for real time

implementation.

Agrawal [2005] of Department of Civil and Environmental Engineering at West Virginia University successfully

performed tests on wooden logs using Ground Penetrating Radar (GPR), to detect the internal defects such as rots,

knots, and metals in logs. Subsurface Interface Radar (SIR)‐20 GPR system manufactured by Geophysical Survey

Systems, Inc. (GSSI) and a ground coupled dipole antenna with central frequency of 900 MHz was used for data

acquisition. The scanned data was processed using RADAN 5.0® software. The study was successful in detecting

the defects with a depth resolution of within 4 cm and a length resolution of within 2 cm. Pyakurel [2009] of

Department of Civil and Environmental Engineering at West Virginia University successfully performed tests on

wooden logs and cants using GPR and explored different interpretation methods (2‐D and 3‐D imaging) to

identify defects accurately. Automated Scanning System was developed to automate the movement of scanning

system with respect to canted logs and study the effects of motor vibrations on GPR scanned data. Devaru [2006,

2008] of Department of Industrial and Management Systems Engineering at West Virginia University developed a

MATLAB® algorithm to process the GPR scanned data. The study was successful in detecting the defects such as

rot, knots and embedded metals and was also able to represent the defect location in X‐Y (distance–depth)

coordinates. Jadeja [2007] of Department of Industrial and Management Systems Engineering at West Virginia

University used defect detection algorithm from previous study [Devaru 2006] to develop a process plan that

generated a cutting sequence for sawing cant at re‐saw machine. The generated process plan was represented as

series of numbers that corresponded to the different faces of the cant. This process plan algorithm showed an

increase of 13 to 21 % in lumber value yield compared to conventional sawing sequence.

Schmoldt et al. [2000] in their paper discussed about the six design and operational constraints for industrial CT

scanners to become a viable reality. Schmoldt et al. discussed about important constraints such as scanning speed,

scanner duty cycle, harsh environment and effective application software which needed to be satisfied before

being implemented in sawmill environment. They also discussed the value gain realized from prototype testing of

CT scanner at medium sized facility in Austria.

4 DAYAKAR DEVARU, NISHIT BANURI, BHASKARAN GOPALAKRISHNAN, UDAYA HALABE, HASAN H LATIF

Occena [1991] stated that the advent of non‐invasive technological developments without integrating the

information involved would result in data overload for the human saw mill operator. This paper discusses about

the issues related to computer integration for various technologies such as scanning, edging, trimming and

grading available to hardwood sawmills. Occena described a model for computer integrated hardwood sawmill to

resolve issues related to the integration of hardware and software such as scanning, edging, trimming and

grading. Bowe et al. [2000] found that nearly 73% of the hardwood sawmills did not have any type of scanning and

optimization technology. They stated the reason for reluctance to adopt new technology might be the low available

capital in the sawmill industry. In a related research, Bowe and Smith [2006] analyzed the data collected through

personal interviews with two hardwood sawmill groups including one that had adopted scanning and

optimization technology and the other had not. They examined decision factors for adopting scanning and

optimization technology such as production improvements, maintenance issues and barriers in hardwood

sawmills. The components such as improved raw material recovery, increased production levels, increased lumber

revenues, improved lumber quality and improved lumber consistency were classified into “production

improvements” factor. The components such as initial cost, new mill installation and existing mill layout

restriction were classified into “barriers” factor. The adopters of scanning technology rated production

improvements as the most important decision factor whereas the non‐adopters rated barriers as the most

important decision factor. Schmoldt [1992] presented a paper describing the importance of Computer Integrated

Manufacturing (CIM) software that can exercise processing decisions with the information provided by the

scanning systems. He stated that the difficult aspect to automate sawmill operations would be the natural

variability and complexity of wood such as different species and defect types such as visual surface defects (e.g.

knots, holes, and splits), board geometry defects (e.g. warp, crook, thickness variation),and internal defects (e.g.

honeycomb splits). He also attributed the high capital investment and poor payback on investment as primary

reasons for reluctance to automation improvements.

The above literature review indicates that significant effort has been devoted to developing various nondestructive

technologies for use with wood based products. Non‐destructive scanning technologies such as Computed

Tomography (CT), X‐ray, ultrasonic, nuclear magnetic resonance, sound wave transmission, gamma ray

transmission and GPR were able to detect the defects. Among these scanning technologies CT was found to be

most widely investigated technology and give better resolution of defects. The execution speeds of various data

processing algorithms and cost involved were the major drawbacks for the CT scanning technology. The literature

review also discusses about the concerns of hardwood sawmill industries such as high initial cost, production and

maintenance issues to implement the scanning systems. Significant research has been done on using GPR scanning

systems to identify the defect location and to generate cutting sequence to obtain better grade lumber. GPR

scanning system also provided better resolution of defects and was found to be less expensive compared to CT

scanners. There is the scope for research to evaluate the technical requirements and economic feasibility for

implementing GPR scanning system in sawmills.

GPR Scanning System

The laboratory setup for GPR scanning was assembled by Agrawal [2005] (Figure 3a) who utilized the Subsurface

Interface Radar (SIR)‐20 system manufactured by Geophysical Survey System, Inc. (GSSI). The GPR system

consisted of a mainframe unit connected to a portable laptop computer. The laptop controls the GPR data

acquisition. The mainframe unit was connected to the antennas for transmitting and receiving the signals. GPR

scans were acquired using survey wheel mode. The survey wheel includes an optical encoder which transmits

distance information to the GPR data acquisition system using a connecting cable. The data collection parameters

such as scans/second and scans/meter were user‐specified inputs that respectively affected how many scans of

GPR data are collected in a second and how many scans are collected based on distance traveled. Other user

defined parameters include time range (ns), samples/scan and bits/sample that affected the sample depth and

resolution of the data, and the signal quality as well. The header file that contains input values must be entered by

the user every time before performing the scanning process.

Ground Coupled Antennas

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sawmills to introduce new sawing techniques so that they will get best possible grades from the logs. Table 1

shows the average lumber prices by species, grade and thickness acquired from one of sawmills during this

research.

TABLE 1 LUMBERPRICES (USD) BYGRADE ANDTHICKNESS ($/1000 BOARDFT)

Lumber Grade Cherry Hard Maple Red Oak White Oak

4/4ʺ thickness boards

FAS / FAS – 1F / Selects $2,200 $2,000 $1,450 $1,400

No. 1 Common $1,380 $1,020 $960 $800

No. 2 Common $845 $620 $630 $550

6/4ʺ thickness boards

FAS / FAS – 1F / Selects $3,100 $2,950 $2,400 $2,300

No. 1 Common $1,850 $1,580 $1,700 $1,550

The fact that the raw material costs up to 75% of the total operating costs in sawmills makes it an economic

necessity to implement some type of NDT to improve lumber value yield. Based on a survey conducted [Smith

2004] in couple of mills in West Virginia and Pennsylvania which were two major hardwood lumber producers,

the production rate of high grade lumber (FAS/Selects) was less than 50%. This is due to the fact that the wood

processors are not able to identify the location of defects before actually sawing. And as the steps in sawing

process are irreversible, the loss involved in producing low grade lumber is also irrevocable. As a consequence

many mills are operating less than optimal profits. According to various surveys conducted to determine

hardwood research technology priorities [Milauskas 2005], one of the primary priorities was introduction of

automated defect detection and grading systems in sawmills.

Significant research has already been performed to address the scanning, processing, and analysis of the data and

generating the process plan for break‐down of cants. All this research was performed in a lab environment but the

critical issue was to integrate this scanning system technology into existing sawmill environment. Concerning such

as data acquisition, processing speeds, locating the scanner into existing sawmill layout and operations, and

impact on production parameters have to be addressed before implementing the scanning system.

To implement the GPR scanning system in sawmills, it is important to do feasibility studies. This research focuses

on determining and analyzing the potential issues for implementing GPR scanning system in sawmills. Technical

issues are often seen as primary focus of a feasibility study, but it is also important to analyze whether the project

is economically viable. This analysis will help to determine whether it is feasible or not to develop GPR scanning

system prototype and implement it in sawmills.

Feasibility Analysis

The GPR scanning system in sawmills must be able to operate up to 3 shifts, 5 days per week continuously. For

bridge deck and concrete evaluations, GPR was used only for certain periods of time, but sawmills would saw

hundreds and thousands of cants every day and they should be scanned in nearly a continuously manner. The

environmental noise which may be from nearby power lines, interference from any electromagnetic devices or

conductive materials such as conveyor system may contribute to errors in recorded scan data. Though the GPR

scanning systems which are used for surveying have the ability to collect data continuously, the reliability and

durability of the system in harsh sawmill environment would be a major concern. Further, the design of non‐

conductive conveyor system at the GPR scanning location would add to the cost and complexity of the scanning

system installed at sawmills.

The defect detection algorithm does not specify the type of defect, but identify the type of defect is important

because lumber grades depend on the type and length of the defect. Further research needs to be done to

differentiate the defect types based on the signal intensities by scanning logs of various species in each of the three

different log grades (FAS, Select, and Common). A better cutting sequence could be obtained by improved

estimation of third dimension of the defect which would require scanning cant from four faces [Jadeja 2007]. The

GPR data processing software could be developed by integrating enhanced defect detection and process plan

algorithms. GPR scan data is processed by applying the following major steps to generate cutting sequence that


yield higher value lumber.

Converting radar data and processing individual antenna scan data by applying steps such as linear gain,

zero correction, signal clipping and filtering by using the defect detection algorithm.

Detection of internal defects in individual processed antenna scan data by applying threshold value concept

used by Devaru [2006]. Generating two‐dimensional coordinates for identified defects by using the defect

detection algorithm.

Integrating the coordinate matrix data obtained from processing scan data of individual antennas to

determine the defect dimensions. Boxing the defects using synthesize data module of process plan

algorithm.

Generating sawing sequence in terms of face numbers and thickness by integrating “compare face module”,

“grading module” developed by Jadeja [2007] with the cost module. A “cost module” should be used which

has the cost details of the lumber based on species, grade, and dimensions. This would allow the program

to generate the sequence based on the lumber value and not just only on the grade.

For real time implementation of a GPR scanning system, the processing time of algorithms would play an

important role. Therefore, the algorithm should have the capability to process the radar data from multiple

antennas simultaneously. This could be achieved by using Parallel Computing Toolbox™ in MATLAB® software.

The algorithm should take the data from sensors about the length, width and height of the cant to generate defect

location coordinates. Further research needs to be done to verify if the data acquisition toolbox can send the data

from GPR antennas directly into the algorithm. Figure 6shows the overview of the data acquisition system

components.

FIG.6 OVERVIEW OF DATA ACQUISITION SYSTEM COMPONENTS

The cants produced at the head saw will be conveyed to a buffer region before entering re‐saw conveyor loop. The

buffer region holds around 7 to 8 cants before they enter re‐saw conveyor system. These cants stay in the buffer

region while the cants which are already on the conveyor system are processed into boards. The re‐saw operator

flips the cants to the desired side every time when they arrive for re‐sawing. Therefore, the appropriate location

for the GPR scanning system would be the place before the cant enters the re‐saw conveyor system (Figure 7).

After scanning and processing, the process plan (or cutting sequence) would be generated for the cant, but it may

become difficult to track the cant and indicate the process plan to the re‐saw operator. It is proposed to have

equipment to tag the cant with barcode on one end and mark it with face numbers on the other end at this

location.

Advantages of having scanning system at this location:

Scanning process could be completed during the time that cants wait in the buffer region and would have

less impact on production output.

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FIG. 7 TYPICA

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st one face nu

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NING SYSTEM

d be identify

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tware such a

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en there are 7

round 512 se

arcode Reader

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2009]) along

d for GPR d

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as Visual Bas

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abeling syste

uld be design

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boards through re‐saw. Assuming the dimensions of all the 7 cants are same, it takes around 10 minutes to

complete the sawing process for a cycle.

During this time, another set of 7 to 8 cants would be waiting in the buffer region to enter re‐saw conveyor loop.

The GPR scanning and data processing of 7 to 8 cants should be completed within these 10 minutes or between 75

to 85 seconds for one cant to have no impact on the production output. The total time taken by the existing SIR‐20

GPR scanning system and the execution time of the algorithms is approximately around 175 seconds for each cant.

The distribution of 175 seconds for scanning a log and processing the data as per the tasks performed are shown

below.

1. Data Entry = 15 seconds

2. Scanning = 20 seconds

3. Data Conversion = 50 seconds (25 seconds x 2 scanned data files)

4. Defect Detection Algorithm = 60 seconds (30 seconds x 2 data files)

5. Process Plan Algorithm = 30 seconds

Assuming data entry and scanning are done in main frame computer and data conversion, defect detection and

process plan algorithm are run in individual personal computers, the cycle time will be around 60 seconds since

the defect detection algorithm takes the maximum time out of all the tasks. Programs can be written to transfer the

data from one computer to another and process it automatically. Programs can also be written to do parallel

processing of last 3 tasks in the same computer, but the computer might get overloaded due to parallel data

processing. While this cycle time of processing programs is going on, log scanning can be done with the log

sawing time and save some time. Also, currently data conversion and defect detection algorithms are processing

two files each and if this can be done in parallel, then cycle time can be further reduced. The GPR scanning speed

can further be increased by using new SIR‐30 control system.

Sensitivity Analysis

In this study, the feasibility of implementing GPR scanning system is evaluated for three different sawmill

capacities. Sawmill capacity of 4.5 Million Board Feet (MMBF) of lumber per year was the lowest among all the re‐

saw operation. Therefore, sawmills producing lumber of 5, 10 and 20 MMBF/yr are considered for this analysis.

In this research, GPR scanning system and process plan (cutting sequence) were based on scanning cants. Hence,

sawmill revenues generated through re‐saw operation is considered for analysis. For sawmills producing 5, 10 and

20 MMBF of lumber per year, approximately 3 MMBF/yr (60%), 5 MMBF/yr (50%) and 8 MMBF/yr (40%)

respectively were produced through re‐saw operation. The gross annual income through lumber produced at re‐

saw can be estimated by using the following equation.

Annual Income = 1 1 1

p q r

S T G GS T G

PL PL PL P TL

Where,

PLS = Percent of lumber species ‘S’

PLT = Percent of lumber thickness ‘T’ of species ‘S’

PLG = Percent of lumber grade ‘G’ of lumber thickness ‘T’ of species ‘S’

PG = Price of lumber grade ‘G’ of lumber thickness ‘T’ of species ‘S’

TL =Total Lumber produced at re‐saw

A sensitivity analysis was performed to study the potential impact of uncertainty in the initial assumptions. The

sensitivity of Net Present Value (NPV)was evaluated with respect to the changes in key factors such as initial

investments, lumber value yield, revenue, operating costs and scanner downtime for different sawmill

configurations. This was done by varying values of one assumption at a time while holding all the other


assumptions constant. Table 2 shows the initial assumptions made for different sawmill configurations. The

important assumption of implementation of GPR scanning system in sawmill will have 0% impact on production

output and will increase the lumber value yield by 10%. The initial investment for scanning system was estimated

as $300,000 for 5 MMBF size sawmill using estimated values for GSSI SIR‐30 control unit with four 1,600 MHz

antennas as $60,000 [GSSI 2011], installation of non‐conductive conveyor system, scanning set up, tagging and

tracking system and any additional modifications as $150,000, data processing software as $50,000and $40,000 for

any other additional equipment and training. For the sawmills producing 10 and 20 MMBF lumber, it was

assumed that they would need additional SIR control unit and antennas as the reliability of these systems is not

known in continuous operation sawmills. Hence, for these larger sized sawmills, initial cost was estimated to be

$400,000.

Fixed costs include management and administrative costs, maintenance expenses and insurance costs and these

costs are categorized as overhead expenses and it accounted for only 4% of total operating costs in a typical

sawmill in 2005 [US DOC 2005]. For this analysis, it was estimated that overhead expenses were 10% of the

installed GPR scanning equipment cost in sawmill producing 5 MMBF of lumber per year and 15% for sawmills

producing more than 5 MMBF/yr. Variable costs include the labor cost for GPR scanning operator and the

electrical costs for operating equipment related to the scanning process. The average hourly wage for production

worker was $15.74 in 2008 [US DOL 2010]. The scanning operator is considered to be skilled labor and the hourly

wage is assumed to be $30 including fringe benefits. The labor cost is estimated to be around $60,000 (8‐hr shift for

scanning operator for 250 working days a year) for a sawmill producing 5 MMBF of lumber per year. The electrical

costs are estimated to be no more than $1,500 per month, which translates to $18,000 per annum. The operating

cost of barcode tagging and process plan marking on the cant was estimated to be $2,500. Therefore, the total

operating costs are estimated to be around $110,500 for sawmill producing 5 MMBF of lumber per year. Similarly,

the variable operating costs for sawmills producing more than 5 MMBF of lumber (operating more than 1 shift) are

calculated and shown in Table 2.

TABLE 2 ASSUMPTIONSMADE FORDIFFERENTSAWMILLSIZES

Sawmill Size

(MMBF/yr)

Re‐saw Output

(MMBF/yr)

Revenue through Re‐saw

($)*

Initial Investment

($)*

Operating Costs

($)*

Increase in Annual

Income ($/yr)*

5 3 2,652,450 300,000 110,500 265,245

10 5 4,420,750 400,000 208,500 442,075

20 8 7,073,200 400,000 251,500 707,320

Results and Discussion

Based on the assumptions and estimations made in this analysis about lumber species and grades, lumber price,

initial costs, operating costs, increase in lumber value yield and impact on production output, there seems to be a

direct relationship between sawmill size (re‐saw output) and economic performance of the GPR scanning system

(Figure 9). Net present worth of largest sawmill turns positive before the other sawmills as expected. Here the

discounted rate is estimated as 15% to determine the present value of each annual increase. The taxes are

calculated by reducing taxable income by 40% (effective tax rate).

Figure 10 shows the sensitivity of NPV results to variation in revenue. The revenue generated depends on the

species, volume and the thickness of the lumber processed and the lumber grade distribution. The investment

would be profitable even with 20% decrease in the revenues for all the three sawmill configurations. For sawmill

producing 8 MMBF per year at re‐saw, the investment would be profitable even with decrease as high as 45% in

revenues.

Figure 11 shows the sensitivity of NPV results to increase in lumber value yield. The GPR scanning system would

be feasible for sawmills producing 8 MMBF at re‐saw even with value yield increase of less than 6%. In contrast,

the increase in value yield should be at least 8% for sawmills producing 3 MMBF and 5 MMBF of lumber per year

at re‐saw. The economic potential of the investment increases at different rates for different sawmill configurations

for value yield above threshold values. Increase in cash flows would be substantially more for larger sawmills with

increase in value yield. This also indicates that the decrease in value yield has the most significant negative impact

on NPV for sawmills producing 5 MMBF or less at re‐saw.

12

Fig

do

of

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Fig

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DAYAKA

FIG. 9 D

FIG. 11 NP

FIG

gure 12 show

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GPR scannin

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gure 13 show

% downtime

oducing 8 M

erating days

gure 14 show

es not have s

50,000 for 3 M

mong all th

mplementation

AR DEVARU,

DISCOUNTED A

PV VS INCREAS

G. 13 NPV VS SC

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ws the sensiti

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MMBF and $

he factors, d

n of GPR sca

, NISHIT BAN

AFTER TAX CA

SEIN LUMBER

CANNER DOW

tivity of NPV

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less than $4

ely to be prof

ivity of NPV

erating days

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ivity of NPV

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$300,000 for 5

decrease in

anning system

NURI, BHASKA

ASH FLOWS

VALUE YIELD

WNTIME

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) for sawmil

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ARAN GOPA

D

changes in in

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large sawm

$700,000 app

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canner down

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ssing 8 MMB

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lls. The initia

ALAKRISHNA

FIG.10 N

FIG. 12

FIG. 14 NPV

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ills even wit

proximately f

n value yield.

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aw output o

be feasible ev

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BF at re‐saw.

essed at re‐sa

has significa

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AN, UDAYA H

NPV VS VARIA

2 NPV VS INITI

VS INCREASE

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per year at re

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for sawmills

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f 3 and 5 MM

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The operatin

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t also affects

HALABE, HAS

ATION IN REVE

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E IN OPERATIN

ncrease in ini

re‐saw. The im

investment

s producing

would be fea

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wntime as hi

ease of130%

ng costs shou

vestment to

on the fea

s the econom

SAN H LATIF

ENUE

ENT

NG COSTS

itial investme

mplementati

of $1.4 millio

3 MMBF and

sible even w

ar. For sawm

igh as 40% (1

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be feasible.

asibility of t

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with

mill

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osts

han

the

nce


of the scanning system for sawmills producing 3 MMBF and 5 MMBF of lumber per year at re‐saw. The results

indicate that sawmills produce 8 MMBF or more at re‐saw would have least amount of risk and highest possible

return on investment (ROI) from installation of GPR scanning system.

Conclusions

This study investigated the feasibility of implementing GPR scanning system in sawmills. The technical

requirements for GPR scanning system and the data processing software were analyzed. The durability of

scanning system in continuously operating sawmills, and integrating scanning system into existing sawmill

operations were identified as major technical concerns for implementing GPR scanning system. GSSI SIR‐30

control unit has the ability to collect radar data from four channels at higher speeds compared to SIR‐20 control

unit. The scan data from four antennas would enhance the internal defect information but processing time of the

algorithms might increase. The introduction of quad‐core processors which has the 8‐way multi‐tasking

capabilities and the parallel computing techniques in MATLAB® software would help in processing the data from

four antennas simultaneously which would significantly reduce the execution times of processing algorithms.

The location for GPR scanning setup has been identified to be the place just before the cants entering re‐saw

conveyor loop. A method to track the cants after scanning and indicate the process plan to re‐saw operator has

been proposed. The automated flipping of cant without the intervention of re‐saw operator has been investigated,

but it is not possible with the current barcode technologies and the major concern would be cants following one

another in quick succession at re‐saw. Time study with the proposed scanning location indicated that the scanning

and data processing time should be kept to approximately less than 75 seconds. The log scanning time and the

processing times of algorithms developed by Devaru [2006] and Jadeja [2007] were less than 75 seconds and hence

GPR scanning could be easily integrated into sawmill operation to improve lumber value yield. But identifying

defect types (i.e., rot, knot) would be crucial to obtain a better cutting sequence as these defects effect the grades

and value of the lumber produced.

This study also investigated the economic feasibility of implementing GPR scanning system for three different size

based sawmill configurations. Based on the conservative estimates of revenues and costs, the GPR scanning system

seems feasible for all the sawmill configurations. Sensitivity analysis indicates that the decrease in lumber value

yield has the greatest impact on economic potential. The initial cost is also a major factor for small and medium

sized sawmills. Scanning cants using GPR technology appears to be promising and warrants further research to

make it a viable reality.

Future work for this research would be to actually simulate the GPR scanning operation under sawmills

conditions such as continuous data collection on cants to test its durability. Further research needs to be done on

barcode readers to integrate it with flipping mechanism and re‐saw operation. Research should be done to test the

signal quality at different sampling rate and at higher data acquisition speeds using SIR‐30 control unit. Scanning

should be performed on cants of different species and grades to know how it increases in value yield vary by

species and log grades.

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radar”. Nondestructive Testing and Evaluation, 24(4):329–346

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GPR‐based defect detection system”, Int J AdvManufTechnol (2011) 56:649–661

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Ground Penetrating Radar (GPR) system”, M.S. Thesis, Department of Industrial and Management Systems Engineering,

West Virginia University, Morgantown, WV, 2007.

[14] Kabir, M.F., Araman, P.A., “Nondestructive Evaluation of Defects in Wooden Pallet Parts by Ultrasonic Scanning”,

Proceedings of 13th International Symposium on Nondestructive Testing of Wood, August 19‐21, 2002.

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DayakarDevaruis a Ph.D candidate at Industrial and Management Systems Engineering department ofWest Virginia

University. He received his Bachelor’s in Mechanical Engineering from University of Mysore in 1998 and Master’s in Industrial

Engineering from West Virginia University in 2006. He is a ‘Certified Energy Manager’ from ‘Association of Energy Engineers’

and a qualified specialist of ‘Process Heating Assessment and Survey Tool’ from US Department of Energy. He has received

‘Outstanding Industrial Assessment Center Engineering Student’ award for the year 2014 from US Department of Energy. He

worked on developing an Algorithm to detect defects in wooden logs using ground penetrating radar for his Master’s thesis

and currently working on Predicting Energy Intensity in Wood Processing Sawmills based on Analysis of Product, Process and

System parameters for his Ph.D dissertation. He is also an ‘Engineering Intern’ from ‘West Virginia Board of Registration for

Professional Engineers’.

NishitBanuri is working as a project engineer at Casco, USA. He was a graduate student from the Department of Industrial and

Management Systems Engineering (IMSE) at West Virginia University (WVU). During his Master’s program, he was also a part

of the research assistant team at WVU’s Industrial Assessment Center (IAC) funded by the US DOE. Mr. Banuri obtained his

M.S. degree in Industrial Engineering from the IMSE department at West Virginia University.

BhaskaranGopalakrishnan is a Professor of Industrial and Management Systems Engineering (IMSE) at West Virginia

University (WVU) and Director of the Industrial Assessment Center (IAC) funded by the US DOE. Through the IAC and as a

private consultant, he has conducted numerous industrial and energy assessments as well as plant wide energy assessments for

manufacturing facilities and has published widely in this field. Dr. Gopalakrishnan obtained his B.E. (Hons) degree in

Production Engineering from the College of Engineering at Guindy, University of Madras, India, M.S. degree in Operations

Research from Southern Methodist University, and Ph.D degree in Industrial Engineering and Operations Research from

Virginia Tech. He is a Certified Energy Manager (CEM) certified by the Association of Energy Engineers, Atlanta, Certified

Practitioner in Energy Management Systems (CPEnMS), and a US DOE Qualified AirMaster+ Specialist, SSAT Qualified

Specialist, FSAT Qualified Specialist, PSAT Qualified Specialist, and a PHAST Qualified Specialist. He is a registered

Professional Engineer (PE) in the State of West Virginia. His areas of research interest are industrial energy conservation, waste

reduction, and productivity improvement.

Udaya B. Halabe is a Professor of Civil and Environmental Engineering at West Virginia University, Morgantown, WV. He

received his B.E. degree in Civil Engineering from University of Roorkee, India in 1984, and M. Tech. degree in Civil/Structural

Engineering from the Indian Institute of Technology (IIT) Kanpur, India in 1985. He received M.S. and Ph.D. degrees in

Civil/Structural Engineering from the Massachusetts Institute of Technology (MIT) in 1988 and 1990, respectively. He also

received M.S. degree in Management from MIT in 1990. He is a Fellow of the American Society of Civil Engineers (ASCE), the

Structural Engineering Institute (SEI) and the American Society for Nondestructive Testing (ASNT), and member of the

American Concrete Institute (ACI) and the American Society for Engineering Education (ASEE). He has received the Statler

College Outstanding Teacher Award for 2009‐2010 and numerous teaching awards from the CEE Department. In 2012, he

received the James M. Robbins National Excellence in Teaching Award presented by Chi Epsilon, the National Civil

Engineering Honor Society. He is a member of two honorary societies, Chi Epsilon and Tau Beta Pi. Dr. Halabe has over

twenty‐five years of research and field testing experience in the area of nondestructive testing and evaluation of civil

infrastructure. He has published over 100 technical papers and over 40 research reports in the area of nondestructive evaluation

of timber, steel, concrete, and composite structural components. His expertise includes the use of several nondestructive testing

techniques such as ground penetrating radar, infrared thermography, ultrasonics, and vibration based technologies.

Hasan H. Latifis a graduate student in Industrial and Management Systems Engineering Department (IMSE) of West Virginia

University (WVU). He is playing a significant role as a research assistant in the Industrial Assessment Center (IAC) funded by

US DOE. His research interest includes energy efficiency, sustainable manufacturing, and operations management. He achieved

BSc. in Industrial and Production Engineering (IPE) from Bangladesh University of Engineering and Technology (BUET) where

he served as a lecturer also. He is a certified six sigma green belt holder from American Society of Quality (ASQ). He is affiliated

with Institute of Industrial Engineers (IIE), Society of Manufacturing Engineer (SME), Alpha Pi Mu, Project Management

Institute (PMI), and American Society of Quality (ASQ).

feasibility analysis for implementing gpr scanning system in sawmills for productivity improvement

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