Sawing is an important manufacturing operation used in many industries to cut raw materials to a specific length prior to secondary precision manufacturing processes. There are three basic types of sawing process, namely band sawing, circular sawing, and hack sawing, where the choice between them depends primarily on the particular needs of the sawing task. Compared to circular sawing and hack sawing, band sawing achieves a lower kerf width, a higher metal removal rate, and an improved surface finish. As a result, it is frequently the method of choice for high-productivity sawing operations. However, such benefits are dependent on achieving a trouble-free operation of the sawing machine and its components. As for any machine tool, maintaining the cutting performance of band saw machines relies on a proper estimation of the tool life and working conditions . Thus, monitoring the tool wear and surface quality of the sawed components, and adjusting the working conditions accordingly, is an important concern.
The literature contains many studies on tool condition monitoring (TCM) and life prediction methods. For example, Jemielniak  compared the signals obtained from laboratory and industrial cutting force sensors and concluded that cross-talk between the channels had a significant effect on the accuracy of the cutting force measurements in both cases. Choudhury and Rath  proposed a method for estimating the tool wear in the milling process based on the relationship between the flank wear and average cutting force coefficients produced under different cutting speeds, depths of cut, and feed rates. Gao et al.  introduced a data-driven model framework for TCM based on a statistical analysis of the cutting force. The validity of the proposed method was demonstrated experimentally through the lathe turning of Inconel 718 workpieces. Freyer et al.  compared the effectiveness of two TCM strategies based on orthogonal and unidirectional cutting force measurements, respectively, and found that the probability of a difference of less than 5 percentage points between the flank wear estimation errors of the two methods was more than 95%. Kaya et al.  proposed an online TCM system for milling machines based on an analysis of the measured cutting force and torque by an artificial neural network (ANN). The proposed system was shown to achieve a high correlation rate and low error ratio between the actual and predicted values of the flank wear in the machining of Inconel 718. Garshelis et al.  developed a method for monitoring the cutting tool condition and operating parameters in a general machining process through an inspection of the magnetoelastic rate of change of a torque sensor signal. Ahmad et al.  used a three-component piezoelectric transducer to examine the effects of the machining parameters (i.e., the cutting speed and feed rate) and workpiece shape on the cutting performance of a band sawing machine with a variable pitch combination blade. Andersson et al.  detected the variation in the cutting force between the individual teeth of a band saw using a multi-sensor technique, and proposed a method for quantifying these variations using a cutting force model based on positional errors of the cutting edge, changes in the tool dynamics during machining, and edge wear of the cutting tool. Thaler et al.  presented a method for characterizing the band sawing process based on an analysis of the cutting force signals. It was shown that the force signals provided useful insights into not only the blade geometry, but also the homogeneity of the cut workpiece.
It is well-known that the surface texture plays an important role in determining how a real workpiece will interact with its cutting condition using in-process monitoring. The surface texture of a finished geometrically-defined component essentially represents the fingerprint of all the previous processing stages, and is generally quantified by the surface roughness [11-12]. For machining processes, the surface roughness not only provides an important indication of the part quality, but also yields valuable insights into the state of the manufacturing process and cutting tool. Consequently, monitoring and quantifying the surface roughness provides an effective approach for controlling the manufacturing process in such a way as to achieve the required degree of accuracy of the workpiece surface [13-15]. One of the most important factors affecting the surface roughness and machinability of the workpiece is the tool wear. In practice, the tool wear determines not only the surface roughness of the final part, but also the cutting force and tool life. Hence, by monitoring the cutting force, it is possible to both evaluate the evolution of the tool wear throughout the manufacturing process and to estimate the effect of this wear on the surface finish.
Various techniques based on multiple sensors have been proposed for monitoring the process variables during machining in order to estimate the tool wear. Bhogal et al.  showed that the cutting speed was one of the most important factors affecting tool vibration, and therefore had a critical effect on the surface finish. Amin et al.  investigated the effect of the chatter amplitude on the surface roughness under various cutting conditions, and found that the correlation between the chatter amplitude and the surface roughness increased with an increasing cutting speed. Arizmendi et al.  proposed a method for predicting the topography, surface roughness and form errors produced in the peripheral milling process based on an analysis of the tool vibration. David et al.  showed that, in the end-milling process, a higher cutting depth and feed rate lead to an increased cutting force and vibrational amplitude, which resulted in turn in a higher surface roughness. Zahoor  evaluated the effects of the feed rate, axial depth of cut and spindle vibration on the surface roughness and tool wear in the vertical milling of AISI P20 steel workpieces using a solid carbide cutter. The results showed that the surface roughness depended mainly on the vibration amplitude and depth of cut, respectively, while the tool wear was governed principally by the vibration amplitude and feed rate.
In general, the accuracy of the prognostic estimation for the tool life of machine tools and surface quality of machined components is significantly dependent on the method used to collect and process the measurement data. However, while the tool condition can be accurately assessed through the deployment of multiple sensors on the machine tool system, such an approach is costly and applicable only to laboratory settings. Accordingly, taking the case of a band saw machine for illustration purposes, the present study proposes the use of the embedded load cell sensor, strategically located within the machine tool, to predict the wear of the band saw blade and estimate the surface quality of the machined component based on the measured value of the thrust force acting on the blade. Experimental trials are performed to investigate the effects of the cutting force, feed rate and machining time on the tool wear and surface roughness of the machined workpiece. It is shown that the thrust force, tool wear and surface quality of the machined workpiece are strongly correlated. As a result, the thrust force measurements provide a viable approach for evaluating the tool condition and predicting the tool life. Based on the experimental results, a feedback control system is developed for maintaining a stable thrust force on the band saw during cutting. The feasibility of the proposed approach is demonstrated experimentally through the machining of medium- and high-carbon steel workpieces with various cross-sections.