Tool wear monitoring in bandsawing using neural

Int J Adv Manuf Technol (2011) 55:969–982 DOI 10.1007/s00170-010-3133-1

ORIGINAL ARTICLE

Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments Haci Saglam

Received: 15 July 2010 / Accepted: 29 December 2010 / Published online: 15 January 2011 # Springer-Verlag London Limited 2011

Abstract The bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6 ×9×1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned as L27. The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimen-

H. Saglam (*) Department of Mechanical Technologies, Technical Science College, Selcuk University, 42031 Konya, Turkey e-mail: [email protected]

tation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly. Keywords Bandsawing . Cutting force measurement . Tool wear . Artificial neural network . Taguchi design of experiment . Specific cutting pressure

1 Introduction Bandsawing is the primary process for the preparation of raw materials to be machined. Parting off raw materials by bandsaw machines have become more popular since the sawing equipment started to be used reducing both the cutting time and cost of cutting operation. Most of workpieces must be cut off from raw materials by saw before not to pass to the next process. Bandsawing can be simulated to milling process from the viewpoint of multi-toothed cutting process that provides many advantageous like continuous cuts with endless bandsaw, minimum material loss with small thickness of saw band and variable feed rate, making parting off parts in large dimensions possible. Although milling has been studied by many researchers, there are not many studies on sawing especially on bandsawing. Instead of bandsawing, most of the studies on multi-point cutting have focused on face milling processes. Since the shape of the workpiece and the length of cut vary during in-feed of the band saw and the load on the teeth increases along with the radial engagement of the cut, this variation of load induces vibration in the machine although feed rate is constant. The vibration affects the kerfs of the saw cut that leads to severe tooth wear and tool breakage. Therefore, it is not easy matter to predict the cutting force in bandsawing since it is multi-point cutting as

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in milling process not a single-point cutting. Henderer et al. [1] suggested an analytical method for predicting the cutting force of a sawblade in 2D cutting. However, these models are based on 2D cutting and require prior knowledge of the shear angle; they only give the static mean cutting forces. In bandsawing, cutting force measurements are generally presented as the total cutting force for a group of teeth working together. No more studies have been carried out dealing with the cutting force of each tooth. The multi-point cutting process has been investigated extensively by Martelloti [2, 3] who developed a mathematical model to predict cutting forces in milling using instantaneous undeformed chip thickness and specific cutting pressure. In addition to that, he also introduced an idea that the average undeformed chip thickness could be used in establishing a relation between cutting conditions and specific cutting pressure. Carlin et al. [4] analysed the buckling and vibration of a circular sawblade subjected to a combination of loading conditions. The vibrations occurring according to the nature of band sawblade have a great importance. Ulsoy et al. [5] carried out a literature survey on vibrations and made suggestions about appropriate action to reduce vibrations during bandsawing. Chandrasekaran et al. [6, 7] researched tooth chipping during bandsawing of steel. Recent work undertaken by Sarwar et al. [8–11] and Anderson et al. [12, 13] has made effort to a better understanding of bandsawing, and they investigated cutting forces and friction characteristics, cutting forces and stress generated in the bandsaw teeth, chip formation mechanism, wear modes and mechanisms and the cutting parameters affecting the performance of a saw tooth blade. Therefore, the investigations on evaluation of bandsawing efficiency would be great assessing for bandsaw user and suppliers for further development of band materials and tooth design. The effect of cutting speed, feed rate and workpiece geometry in bandsawing was investigated by Ahmad et al. [14, 15]. In the experimental studies, reduction in the thrust force and cutting force per teeth for unit thickness was observed, as the cutting speed increased. The developed mathematical models are too far to represent the real condition of process at any time in terms of considering the tool wear. Since the tool wear mechanisms are very complicated, it is very difficult to calculate its value by means of analytical formulas. Also, various theoretical models that have been proposed are not accurate enough and can only be applied to a limited range of processes. In a traditional environment, the condition of a tool is monitored by skilled operators who use experience and frequent visual inspection to the tool surface to assess the tool condition. Operator-assisted tool monitoring has the disadvantage of being both expensive and at times erroneous [16]. A reliable method for automated tool wear

Int J Adv Manuf Technol (2011) 55:969–982

monitoring is desirable not only for optimization of tool changing times but also as an essential part of unmanned machining. In part machining, cutting force is considered to be the important characteristic variable that best describes the cutting process [17]. Hence, cutting force monitoring is frequently used to diagnose/predict both tool condition [18, 19] and part accuracy [20]. Tool wear that plays a major role in the economic aspects of metal-cutting operations can be detected easily as it occurs, and the increase in cutting force due to tool wear is dependent strongly on other cutting conditions together with the type of wear, tool work material etc. The prediction of cutting force and tool life in machining is a demanding task, but due to the complexity and uncertainty of the machining process in modelling and optimisation of bandsawing, it is difficult to provide an accurate model with traditional identification methods. Instead of them, artificial intelligence (AI) process models such as neural networks, fuzzy sets, genetic algorithms etc. are used for optimising, predicting or controlling machining processes. To predict and monitor cutting forces, various models using AI techniques were proposed. Many researchers have been using these AI techniques as individual and combined [21–23]. Nevertheless, tool wear is a natural fact, abnormal cutting forces generated during machining process excite it and causes early tool failure or tool breakage. Therefore, several tool wear monitoring schemes have been proposed that employ cutting force, torque, power, vibration, acoustic emission, velocity, temperature and sensor fusion. These parameters are monitored indirectly by related sensors and are correlated with tool wear. The sensors are used to extract features from cutting zone and tool wear is estimated. Mostly neural network, fuzzy logic and genetic algorithm are used to estimate wear and monitoring online machining processes. Although indirect methods are not as accurate as direct methods, they are generally suitable for online tool condition monitoring. Sick [24] has reviewed a number of research papers dealing with online tool wear monitoring in turning using artificial neural networks for obtaining the feedback for indirect estimation of tool wear. Based on a continuous acquisition of signals with multisensor systems, it is possible to estimate or to classify certain wear parameters by means of neural networks. Das et al. [25] developed a back-propagation neural network model for the reliable online tool condition monitoring based on cutting force measurement. They conclude that the ratio of cutting force components is a good indicator of the tool wear. The supervision of tool wear is the most difficult task in the context of tool condition monitoring in metal-cutting processes. Saglam and Unuvar [26] have introduced the application of multi-layered neural network to estimate flank wear and surface roughness indirectly in


face milling based on cutting force data. In the cutting tests, a good correlation was obtained between tool wear and cutting forces. With the development in artificial neural network (ANN), the researchers have made an effort to the solution of non-linear problems. The ANN has the ability to approximate any complex relationships between process performance variables and process variables in machining accurately, hence is well suited for the prediction of surface roughness and tool flank wear, cutting tool stage diagnosis and for use in reliable model development of highly nonlinear processes. The ANN has the advantages of learning ability as well as generalization, thus can capture non-linear and complex input–output relationships [27, 28]. Ozel and Nadgir [29] developed a neural network model to predict tool wear for CBN cutting tools during hardened H-13 steel workpieces for a range of cutting conditions. In this manner, Sanjay et al. [30] studied on the ANN model to predict the tool wear considering some cutting conditions and thrust force as process parameters in drilling using twist drills. Khanchustambham and Zhang [31] used neural network to predict cutting force as well as surface finish during machining of ceramic material. Feed rate, depth of cut and spindle speeds were used as input parameters for the network for online monitoring of turning process. Asilturk and Unuvar [32] established a neural-fuzzybased force model for controlling bandsawing process. The results show that the neural fuzzy system developed performs reliable adaptive controlling in real time in controlling cutting speed and feed rate and created a material identification system by using the measured cutting forces. Online method of achieving optimal settings of a fuzzy neural network has been developed by Sandak and Tanaka [33]. The results of the cutting experiments using several wood species show that the fuzzy neural system developed performs well in online feed rate optimization during bandsawing, while maintaining saw deviation within specified limits.

Fig. 1 Setting parameters and tool geometry of sawblade– raker set, standard type [12]

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When the experimentation is costly and time-consuming, it is important that an effective proper planning of experimentation is essential to reduce them. Therefore, a design of experiment (DoE) based on Taguchi orthogonal array (OA) was chosen. Taguchi method is used to optimise the performance characteristics of process parameters, which is proved to be a powerful tool [34]. In this paper, a tool condition monitoring system that predicts the average tool flank wear based on thrust force and cutting force using a multi-layer feed forward ANN trained with error BB training algorithm was employed in bandsawing. The experiments were planned as per L27 orthogonal array and carried out using six factors having two cutting parameters, two cutting condition and two cutting force components with three levels. The ANN architecture developed having different number of inputs and neurons in the hidden layer were tested in order to predict tool wear as accurate as possible. Then the experimental results were analysed using statistical tool in order to define significant factors affecting sawblade tooth wear. The specific cutting energy/pressure was also calculated and modelled for evaluating sawing efficiency.

2 Sawing process 2.1 Geometry and mechanic of sawblade The sawblade has been formed by so many teeth arranged on the pitch of sawblade which is the number of defined teeth per inch. The geometric shape of the sawblade cutting edge varies due to the offset on each side to provide clearance for the back of the blade [35]. The undeformed chip width (bc) and undeformed chip thickness (tcu) in the feed direction vary during sawing. The geometry of sawblade having required terminology is shown in Fig. 1. In bandsawing, since the setting angle χs of the tooth is very small, the feed per tooth is approximated to the equal size of the actual depth of cut or undeformed chip thickness

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(tcu) when they are measured in the same direction. Undeformed chip thickness is affected by cutting edge position, tool dynamics and tool wear. For a given tooth pitch (p), tcu can be expressed as depends on cutting speed (v) and feed rate (f). tcu ¼

f p v

ð1Þ

In this case, the undeformed chip area Ac is: Ac ¼ bc tcu

ð2Þ

Cutting force measurements are generally presented as the total cutting force for a group of teeth cutting together. The undeformed chip area together with specific cutting energy/ pressure (Esc/ks) determine the magnitude of the individual cutting forces, and so a variation in these parameters during cutting also causes a variation in cutting forces values. Esc, defined as the energy to remove a specific volume of material, is calculated by dividing the cutting energy (Ec) with the volume of material removed (Vc). Esc ¼ Ec =Vc ¼ Ec =Ac Lc

ð3Þ

The cutting energy can be written as with power (Pc) and time (Tc) required to make the cut: Esc ¼ Pc Tc ¼ ðFc vc þ Ft vf ÞTc Esc ¼ FAcc þ TcVFct vf

ð4Þ

where Fc is the cutting force (act as vertical) and Ft is the thrust force. If vertical Esc is neglected (as vertical Esc is normally