Sawing Machine Research Articles

A fuzzy control based strategy for adjusting band blade feed rate in constant power sawing

Abstract The variable cross-sectional shape of the blank reduces the efficiency of the sawing machine. Additionally, the wear of the band saw blade causes a mismatch between the feed rate and the load, further exacerbating the wear. This study studied a feed rate control strategy considering sawtooth wear to balance the relationship between cutting efficiency and band life. Initially, speed parameters for the cut-in and cut-out areas, along with acceleration and deceleration control algorithms, were established to ensure the smooth transition of the saw blade during the machining process. Additionally, a constant power sawing feed rate model that considers the influence of wear was developed, enabling dynamic adjustments to the feed rate based on the saw blade's wear state. This study also proposes a fuzzy control-based target current correction method, which dynamically adjusts the target current according to the current wear state of the saw blade, ensuring that sawing requirements were met under varying levels of wear. The accuracy of the sawing model and control strategy was verified through experiments. The results demonstrated that this strategy improved efficiency by 44% compared to the traditional uniform feed rate method. This study introduces a novel feed rate control strategy for sawing and offers an effective solution to the problem of saw tooth wear.

180 years of Innovative Sawing, storage & software technology

German manufacturer Kasto is one of the leading sawing machine manufacturers and advanced storage system developers. Machinery attended a press event to mark its 180th anniversary year in business along with other journalists from across the globe

Noise as a physical risk factor in furniture industry machines

This study aimed to determine the risk level of noise, which is an important physical risk, in small and medium-sized furniture industry enterprises. The noise levels of the circular sawing machines, edge banding machines, and mitre cutting machines, which are among the main processing machines of the sector, were measured. The study was carried out in 32 furniture businesses. The possible risks of noise on the operators of the machines in question and other employees were evaluated. Noise level measurements were made with the help of TESTO 815 measuring device. Dunnett’s T3 test was used to detect differences in noise levels for machine operators and other employees. It was determined that the edge banding machine does not pose an occupational health and safety risk in terms of noise risk factors. However, the mitre cutting machine and the circular sawing machine pose a risk for the machine operator in active production by creating noise above the established exposure limit value. The mitre cutting machine carries the same risk for the machine operator when it is in operation but in passive production. The results revealed the need for personal protective equipment for machine operators for mitre cutting and circular sawing machine.

Réselő adapter rezgéstani vizsgálata kőzetvágás során

This paper deals with a dimensional rock sawing machine to optimize the production of small-volume dimension stone products in limestone. After cutting around the block stone, the rock material can be moved with the help of the machine. The finite element (FE) analysis of the adapter with simplified geometry treated as a solid model is to investigate the natural frequen-cies of the adapter to identify any potentially dan-gerous frequencies. In this study, the eigenvalue extraction used to calculate the natural frequen-cies is based on the Lanczos iteration methods. The natural frequencies of the concept design, es-timated by FE-modal analysis, provide an oppor-tunity to give sufficient stability against the low frequencies associated with the normal operating speed of the cutting at the final version.

Stone sawing machine at the Temple of Artemis in Jerash, Jordan

Stone sawing machine at the Temple of Artemis in Jerash, Jordan

Research on High-Speed Vibration and Structure Optimization of Multiwire Sawing Machine

To improve the sawing stability and slicing quality of an existing multi-wire saw under severe vibrations using a high wire speed, its structure is optimized to improve its structural stiffness, so that it can still maintain good operation stability under higher wire speed. First, a finite element simulation model is established based on the shape, structural and material characteristics of the multi-wire saw, and then a transient dynamic simulation calculation is carried out using finite element software to obtain the vibration characteristics of the machine, which is verified with the measured data. The results show that the error between the calculated and the measured value is less than 11.46%, which verifies the accuracy of the simulation. On this basis, a multi-objective optimization design method based on the response surface is used to optimize the structure of the whole machine, and the optimized model is simulated again to evaluate its optimization effect. The results show that the amplitude of the optimized machine at a 2400 m/min wire speed is equivalent to that of the original machine at a 1500 m/min wire speed, and the wire speed is increased by 60% year-on-year with the same operation stability, which can guide research on high-speed multi-wire saws and machine tool vibration control strategies.

Machine-vision-based intelligent robotic replacement system of grinding-wheel-saws for steel round bar sawing machine

In the factory, the steel round bar sawing machine is applied to finish the sawing mission. Due to the process of the sawing steel is operating in high speed, the grinding-wheel-saw (GWS) is quickly worn by the friction and replaced frequently. The automatic replacement system of GWSs is seldom built yet. To improve the efficiency of the replacement for GWSs, the machine-vision-based intelligent robotic replacement system (MIRRS) for GWSs is developed and reported in the study. The MIRRS is mainly consisted of the six degrees-of-freedom (DOF) manipulator and the six-axes mechanical arm system to successfully complete the task of the replacement. The finite element method (FEM) is applied to analyze and design the subsystem of the MIRRS. Besides, the machine-vision positioning system associated with the digital camera are also setup to provide the required feedback detections for the MIRRS. The hardware system architecture and the control flow chart are described in detail and the hundreds of realistic testing results are collected to verify the reliability and offer the well working efficiency of the designed MIRRS.

Increasing the performance of the raw material wheel and fiber quality when processed by the sawing machine

The saw gin, the primary technical device used in the cotton ginning process, is responsible for separating the cotton fiber from the cotton seed. After being cleansed of khaschops and dried to the desired humidity in the drying, cleaning, and cleaning shops, seeded cotton is transferred to the main building of the business for ginning (separation of fiber). Up to now, 4DP-130, 5DP-130, and DPZ saw gins have been employed in cotton ginning operations to spin medium fiber seeded cotton. The ginning machine's operation, which separates the fiber from the seed in cotton gins, is largely responsible for the creation of high-quality fiber. Maintaining natural quality indicators during the technical process of first processing of cotton, i.e. the appearance, length, and presence of minute impurities in the fiber, is key to ensuring the competitiveness of cotton fiber produced in our nation on the global market. One of the most crucial processes is the ginning of seeded cotton with the appropriate amount of moisture and dirt, which involves removing the fiber from the seed. The length of the fiber, the impurities in the content, the absence of damage, and the hairiness of the seed all play a significant role in this process.

Design and Mechanical Behavior of a Custom Adapter for Dimensional Stone Mining

The paper deals with the development of a dimensional rock sawing machine to optimize the production of small-volume dimensional stone products in limestone quarries by re-thinking the use of production and transport equipment. A low-volume mining production and transportation adapter to be developed for both production and transport can reduce the number of mining machines to be used in the mine. After cutting around the block stone, the rock material can be moved with the help of the machine. Cutting tests were performed to determine the winning properties of the limestone. The first objective of the paper was to calculate the cutting forces and cutting powers and eliminate the failure possibilities and vibrations. In this study, the eigenvalue extraction used to calculate the natural frequencies and mode shapes was based on the Lanczos iteration methods using the Abaqus software to identify any potentially dangerous frequencies. By taking the load cases from modelling of the extraction of limestone and the transport of block stones, the deformation and strength properties of the adapter were also examined using FE analysis for stress/displacement analysis. Drawing the conclusion from finite element analysis, the construction of the adapter can be developed and checked. The results can be used with great confidence, so the main dimensions of the concept serve as the basis for the dimensions of the adapter.

An Empirical Analysis of Barriers to Building Information Modelling (BIM) Implementation in Wood Construction Projects: Evidence from the Swedish Context

Building information modelling is gradually being recognised by the architecture, engineering, construction, and operation industry as a valuable opportunity to increase the efficiency of the built environment. Focusing on the wood construction industry, BIM is becoming a necessity; this is due to its high level of prefabrication and complex digital procedures using wood sawing machines and sophisticated cuttings. However, the full implementation of BIM is still far from reality. The main objective of this paper is to explore the barriers affecting BIM implementation in the Swedish construction industry. An extensive literature review was conducted to extract barriers hindering the implementation of BIM in the construction industry. Secondly, barriers to the implementation of BIM in the wood construction industry in Sweden were extracted using the grounded theory methodology to analyse expert input on the phenomenon of low BIM implementation in the wood construction industry in Sweden. Thirty-four barriers were identified. The analysis of this study also led to the development of a conceptual model that recommended solutions to overcome the barriers identified to help maximise BIM implementation within the wood construction industry. Identifying the main barriers affecting BIM implementation is essential to guide organisational decisions and drive policy, particularly for governments that are considering articulating regulations to expand BIM implementation.

Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines

Monitoring the operational performance of the sawmilling industry has become important for many applications including strategic and tactical planning. Small-scale sawmilling facilities do not hold automatic production management capabilities mainly due to using obsolete technology which is an effect of low financial capacity and focus their strategy on increasing value recovery and saving resources and energy. Based on triaxial acceleration data collected over five days at a sampling rate of 1 Hz, a robust machine learning model was developed with the purpose of using it to infer the operational events based on lower sampling rates adopted as a strategy to collect long-term data. Among its performance metrics, the model was characterized in its training phase by a very high overall classification accuracy (CA = 98.7%), F1 score (98.4%) and a very low error rate (LOG LOSS = 5.6%). For a three-class problem, it worked very well in classifying the main events related to the operation of the machine, with active work being characterized by an F1 score of 99.6% and an error of 3.6%. By accounting for the same metrics, the model was proven to be invariant to the sampling rates of up to 0.05 Hz (20 s) and produced even better results in the testing phase (CA = 98.9%, F1 = 98.6%, LOG LOSS = 5.5%, for a testing sample extracted at 0.05 Hz), while there were no differences in the share of class data irrespective of the sampling rate. The developed model not only preserves a high classification performance in the training and testing phases but it also seems to be invariant to lower sampling rates, making it useful for prediction over data collected at low sampling rates. In turn, this would enable the use of cheap data collectors to be operated for extended periods of time in various locations and will save human resources and money associated with data collection. Further tests would be required only for validation and they could be supported by collecting and feeding new data to the model to infer the long-term performance of similar sawmilling machines.

A robust embedded load cell sensor for tool life prognosis and smart sawing of medium carbon steel

An embedded load cell sensor is proposed for the tool life prognosis and thrust force control of a band saw machine. The sensor enables the tool life and surface quality of the machined workpiece to be effectively improved through the use of a single sensing device strategically located in the cutting machine. The feasibility of the proposed sensor is demonstrated experimentally using a double-column horizontal sawing machine with medium carbon steel bars as the workpiece material. An investigation is performed into the effects of the cutting force, feed rate, and machining time on the machined workpiece’s tool wear and surface roughness. It is shown that the machined workpiece’s thrust force, tool wear, and surface roughness are strongly correlated and increase over time. Based on the experimental results, a feedback control system is proposed for maintaining a constant thrust force on the band saw during cutting under even the most challenging conditions. Overall, the results confirm that a single embedded load cell sensor located in a key position can provide effective force monitoring. Such force monitoring enables a control methodology to maintain the optimal cutting conditions in the sawing of medium carbon steel and improve the tool life and machined part quality.

Timber Losses during Harvesting in Managed Shorea robusta Forests of Nepal

Logging and sawing of timber using conventional tools by unskilled workers causes enormous damage to the valuable timber, residual stand, regeneration, and forest soil in Nepal. The purpose of this study was to find out the volume reduction factor and identify major strategies to reduce timber losses in the tree harvesting process in the Terai Shorea robusta forest of Nepal. Field measurements and product flow analysis of 51 felled trees from felling coupes and randomly selected 167 sawed logs were examined to study harvesting losses. Responses from 116 forest experts were analyzed to explore strategies for reducing harvesting and processing losses. The results showed that timber losses in the felling and bucking stage with and without stem rot were 23% and 22%, respectively. Similarly, timber losses in the sawing stage with and without stem rot were 31% and 30%, respectively. Paired t-test at 5% level of significance revealed that there was significant loss in both tree felling and log sawing stages with present harvesting practice. The most leading factor contributing to timber loss in all of the three stages was the use of inappropriate equipment during tree harvesting. Use of synthetic ropes for directional felling and skidding as well as flexible and portable sawing machine with size adjustment options during sawing were mainly recommended as strategies to reduce timber losses. This study serves as a baseline study to identify and quantify timber losses in different stages of tree conversion and also formulate their reduction strategies in Nepal.

UTILIZATION OF MACHINE LEARNING APPROACHES FOR TOOL WEAR DETECTION AND PREDICTION IN THE CIRCULAR SAWING PROCESS OF METALLIC MATERIALS

Efficiency plays a major role in many areas of the mechanical processing of metals. Especially in basic provision of raw material, for example by cutting components to length during sawing, high penetration and thus efficiency as well as high robustness of the process is required. Here, the sawing tool and its durability is the decisive component in the process. In today's state of the art, at least in modern sawing machines, the condition is recorded via the sawing tool course or also via the motor current. This gives the operator an initial indication of the tool condition. In current machines an extensive sensory recording for e.g., accelerations as a characteristic for the tool condition is not used. The aim of the investigations described in the paper was to use measurement data from various internal and external sensors to record the state of the sawing tool wear and to analyse it using machine learning approaches. For this purpose, cutting tests were carried out on a modern sawing centre and the acquired measurement data were an-alysed using a convolutional neural network (CNN). During the tests, the internal and external sensors were compared to show which of the sensors used provides the best results in detecting wear on sawing tools. The investigations show that the CNN is suitable for detecting tool wear by means of the sensors used, which can be used for on-line monitoring. Finally, an outlook is given, and it is shown how the CNN can be used for active control in the sawing process.

Advanced imaging of dentin microstructure

This study aimed to demonstrate the feasibility of using optical coherence tomography (OCT) for locating the sectioning site of a specimen before characterizing the ultrastructural features of dentin surfaces as well as the inner wall of the dentinal tubules (DT) using a field emission scanning electron microscope (FESEM). Eight sound human molar teeth were extracted, examined via cross-polarization optical coherence tomography (CP-OCT), embedded, and hemisectioned using a low-speed diamond sawing machine. Next, each sectioned surface was further trimmed, polished, and examined under a confocal laser scanning microscope (CLSM) to locate the target area on the superficial dentin. Subsequently, each section was gold-coated and examined using FESEM. Backscattered reflection from the dentin layer was less than that from the enamel under CP-OCT. Distinct reflections from certain enamel and dentin microstructures were observed before sectioning the specimens. Areas with enamel cracks and dentin defects were identified and avoided during sectioning. At the micron level, the CLSM images exhibited a homogenous distribution of the DT orifices. Low magnification FESEM images showed intertubular dentin as a loosely condensed globular layer with shallow grooves in between, whereas peritubular dentin exhibited more organized condensation of apatite crystals surrounding the DT orifices. High magnification of the DT revealed a cross-linking layer of mineralized collagen network extending in the peri-intratubular lumen, with scattered globules of matrix vesicles. CP-OCT enabled the realization of rapid initial scanning and image acquisition with high contrast at the micron scale before profound insights into dentin ultrastructures at the nano scale were provided by FESEM. The variations in structural densities of the dental tissues significantly affected the image contrast and helped identify underlying structures.

Investigation on the stiffness of wire web of multi-wire sawing machine and its influence on machining accuracy

Diamond wire sawing has gradually applied as the dominant way of silicon sawing in the photovoltaic and semiconductor industry. In this paper, a model of stiffness of wire web was established, and the corresponding test measure, process, and evaluation standard were proposed. Lack of stiffness of wire web could easily cause wire bow and further reduce the machining accuracy during diamond wire sawing. And the machining accuracy due to wire bow was analyzed. The special significance of this study is considered the stiffness of wire web to be the key index of multi-wire sawing performance. Through this method, the wire bow and the lack of cutting accuracy can be reasonably evaluated. As a result, wire lag and wire bow due to the loss of the stiffness of wire web could be minimized, as well as its influence on machining accuracy. Static stiffness matrices of wire web were presented to analyze the influence of the feed position and the number of wires forming the wire web on the stiffness of wire web. Moreover, a FEM model was established to find the influence factors such as wheel span, wheel diamond, and preset tension on the stiffness of wire web.Graphical abstract

Assessing the System Vibration of Circular Sawing Machine in Carbonate Rock Sawing Process Using Experimental Study and Machine Learning

Predicting the vibration of the circular sawing machine is very important in examining the performance of the sawing process, as it shows the amount of energy consumption of the circular sawing machine. Also, this factor is directly related to maintenance cost, such that with a small increase in the level of vibration, the maintenance cost increases to a large extent. This paper presents new prediction models to assess the vibration of circular sawing machine. An evaluation model based on the imperialist competitive algorithm as one of the most efficient artificial intelligence techniques was used for estimation of sawability of the dimension stone in carbonate rocks. For this purpose, four main physical and mechanical properties of rock including Schimazek’s F-abrasivity, uniaxial compressive strength, mean Mohs hardness, and Young’s modulus as well as two operational parameters of circular sawing machine including depth of cut and feed rate, were investigated and measured. In the predicted model, the system vibration in stone sawing was considered as a dependent variable. The results showed that the system vibration can be investigated using the newly developed machine learning models. It is very suitable to assess the system vibration based on the mechanical properties of rock and operational properties.

Peculiarities of designing a multi-saw block in the form of a crank saw

The features of the development of sawing equipment with circular translational motion of blades are considered. The proposed solution provides an increase in the productivity and service life of the equipment, reduction of energy consumption and metal consumption of the sawing machine. Keywords: sawing machine, dynamic balancing, circular translational motion. hornet10@yandex.ru

A hybrid model for evaluating the sawability of stones through the performance of frame sawing machine

Modeling of stone processing, especially the sawability of stones (SS) has been studied extensively over the last decade. Artificial neural network (ANN) is an effective method to deal with complex engineering problems. However, ANN has some limitations such as trapped in local minima and cannot analyze the sensitivity of parameters. To fill the gaps, the primary objectives of this paper are to optimize ANN by genetic algorithm (GA) and introduce mean impact value (MIV) algorithm for parametric sensitivity analysis. Therefore, a hybrid model was proposed for predicting SS based on BP neural network (BPNN), MIV algorithm, and GA. Further, the main factors that affect SS were analyzed based on the grinding mechanism and fracture mechanics. The proposed model is not only meaningful to analyze SS, but provides a novel foundation for studying other stone procedures in future.

Tension analysis and fluctuation control of diamond multi-wire sawing machine

During the use of diamond multi-wire sawing machine, tension of diamond wire has an important impact on the quality of silicon wafer. In this paper, ADAMS is used to simulate the tension of diamond wire during the operation of machine, and results are verified by experiments. The general law of tension fluctuation of diamond wire in the operation of diamond multi-wire sawing machine is obtained, and the methods of controlling tension fluctuation are put forward. The proposed method is verified by simulation and experiments, and the conclusion of controlling tension fluctuation is obtained. It is helpful to reduce the broken probability of diamond wire, prolong the service life of diamond wire, and improve the use efficiency of diamond wire.