Simulation of turning operations

One of the most important factors affecting the quality and productivity of turning operations is the dynamics of the system. In this study a computer program is developed to simulate turning operations. The program represents turning operations with a tool geometry simulation module, and the discrete transfer functions of machining process and machine tool structure. The discrete transfer functions of the turning operation are determined by dynamic experiments. The developed program is run at different depth of cuts to study the development and finite amplitude vibrations of chatter. The characteristics of the simulated data agree with the results of previous experimental studies. The simulation program is very convenient to study the chatter susceptibility of machine tool, workpiece and tool geometries.     

The chaotic characteristics of three dimensional cutting

Chatter is an important problem in turning operations. However, there are very few studies on severe chatter because of the difficulties related to data collection and the complexities associated with non-linear cutting dynamics. In this study, cylindrical turning of long slender bars was simulated at three different cutting speeds by using experimentally identified cutting and structural dynamics transfer functions. The simulations were prepared at various depths of cuts, and variations of the cutting forces and displacements were investigated. Visual inspection and the Lyapunov spectrum of the cutting force signals suggested the chaotic nature of the cutting force signals. Although the frequency of the displacement signal remained constant, the amplitude of the signal appeared to have a chaotic nature. The same characteristics were also observed on the experimental data. During the design of a chatter detection or suppression system, the chaotic nature of the force and displacement signals must be considered. The unpredictable nature of the signals may cause prediction errors.     

Basic computational tools and mechanical hardware for torque-based diagnostic of machining operations

In the industry, only rotary dynamometers can be used for monitoring when multiple spindles are used in machining operations. The current commercial rotary dynamometers are bulky and expensive for most machining centers. The basic hardware and computational tools proposed are for a smaller, more cost effective Torque-based Machining Monitor (TbMM). The objective of the TbMM concept is to estimate the remaining tool life, detect chatter from the torque signal inside the proposed device, and communicate with the central computer only when problems arise. The remaining tool life estimation and chatter detection algorithms of the TbMM were developed by analyzing the experimental data collected by a commercial rotary dynamometer. The mechanical hardware of the TbMM was designed to generate voltage proportional to the cutting torque using a piezoelectric composite element. The remaining tool life was estimated from the standard deviation (or variance) of the torque signal. Teager-Kaiser algorithm (TKA) based procedure detected the chatter based on the frequency estimations only from four samples at a time. The accuracy and characteristics of the signal of the mechanical component of the TbMM were found satisfactory in the estimation of machining problems such as wear and chatter. The TbMM is a good choice particularly when multiple spindles work simultaneously on the same workpiece.     

Output feedback adaptive stabilization and command following for minimum phase dynamical systems with unmatched uncertainties and disturbances

In this article, we develop an output feedback adaptive control framework for continuous-time minimum phase multivariable dynamical systems for output stabilisation and command following. The approach is based on a nonminimal state-space realisation that generates an expanded set of states using the filtered inputs and filtered outputs and their derivatives of the original system. Specifically, a direct adaptive controller for the nonminimal state-space model is constructed using the expanded states of the nonminimal realisation and is shown to be effective for multi-input, multi-output linear dynamical systems with unmatched disturbances, unmatched uncertainties and unstable dynamics. The proposed adaptive control architecture requires only knowledge of the open-loop system's relative degree as well as a bound on the system's order. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.     

Development of a decision support system for machining center selection

The machining centers are key resources for manufacturing companies in their dealing with their fierce competitive market environments. However, although selecting the most appropriate machining center is a very important decision for manufacturing companies, the availability of wide-range of types and models makes the selection process a complex and difficult task. In this study, a decision support system (DSS), namely MACSEL, is developed to help the decision makers in their machining center selection decisions. Several issues and applicability of the MACSEL is illustrated with case problems in the paper.Within the developed DSS, to select the feasible set of machining centers fifteen questions are placed in the elimination (pre-selection) module. The developed DSS uses fuzzy analytical hierarchy process (FAHP) or fuzzy technique for order preference by similarity to ideal solution (FTOPSIS), which are extended versions of multi-criteria decision making approaches, to rank the feasible machining centers. In the DSS, FAHP is used if a detailed pair-wise weighting of the hierarchically structured criteria is wanted. On the other hand, when a simpler separate weighting of each criterion is be considered as enough, FTOPSIS is used.     

Fault diagnosis on bottle filling plant using genetic-based neural network

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA–ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA–NN environment. It may also be used for training for selection of ANN configurations at various applications.     

Kinetics of colour,chlorophyll, and ascorbic acid content in spinach baked in different types of oven

Spinach was baked in steam-assisted hybrid ovens, natural and forced convection ovens, and saturated steam ovens at different temperatures and baking times. The moisture content, water activity, peroxidise activity, colour, ascorbic acid, and chlorophyll content were determined for each baking time and kinetic analysis of thermal degradation of the colour, chlorophyll, and ascorbic acid were evaluated. Degradation of green colour, total chlorophyll, and ascorbic acid in spinach leaves during the baking process was considered as first order reaction kinetics and temperature dependency of degradation was described by the Arrhenius equation. Baking in steam-assisted hybrid ovens resulted in the highest rate of reaction for both colour change and chlorophyll degradation, followed by forced convection and then natural convection ovens. The existence of steam in the baking chamber resulted in an acceleration of the baking process and better ascorbic acid retention in spinach was determined by baking in steam-assisted hybrid ovens. Degradation kinetics could allow definition of optimum baking temperatures and times in different types of ovens and shorter baking times should be preferred in steam-assisted hybrid ovens if fresh appearance (high greenness) is desired.     

Effect of material properties on interaction of ceramic and glass flyer plates with hypervelocity jet

A study on the effect of material properties on the interaction of explosively driven ceramic and glass plates with shaped charge jets is performed in order to bolster future studies on the design of reactive cassettes with non-steel flyer plates. A symmetric steel reactive cassette construction is taken as a base, and front and back plates are selectively changed with ceramic or glass plates keeping the areal densities similar. Plates manufactured from two different grades of alumina, silicon carbide, boron carbide, and borosilicate glass are employed to assess the ballistic protection performance of reactive cassettes. It is seen that a high density and fracture toughness is desired for the front plate and high bulk impedance for the back plate, respectively. Moreover, replacing the front plate of a steel cassette with an alumina or silicon carbide plate increases the ballistic protection efficiency close to 50%. Finally, the effect of material selection is investigated by hydrocode numerical simulations. The early phases of the interaction process are presented to reveal the basics of initial hole closure on the flyer plate and the determination of the length of the jet precursor.     

The Effect of Iron Content on Microstructure and Mechanical Properties of A356 Cast Alloy

In the present study, microstructure and mechanical properties of A356 alloy including various amounts (0.2 to 1.2 wt pct) of iron were investigated. The alloys were produced by conventional gravity sand casting method. In order to determine the effect of iron addition to A356, optical and scanning electron microscopes (SEM/EDS) were used for microstructural examinations, and X-ray diffraction (XRD) analysis was carried out for phase characterization. Tensile tests were also conducted in order to determine effect of the Fe content on mechanical properties. It was found that as the Fe content of A356 was increased, the secondary dendrite arm spacing (SDAS) was decreased and the morphology of Al-Si eutectic became finer. From XRD examinations, different iron-based intermetallic compounds (β-Al₅FeSi and α-Al₈Fe₂Si) formations were observed. It was also observed that as iron content increased, α-Al₈Fe₂Si intermetallic was transformed into β-Al₅FeSi intermetallic. The tensile test results revealed that tensile strength and elongation values were reduced by increasing Fe content. It was also determined that β-Al₅FeSi intermetallics were more negatively effective on tensile strength than α-Al₈Fe₂Si intermetallics.     

Modeling micro-end-milling operations. Part I: analytical cutting force model

A new analytical cutting force model is proposed for micro-end-milling operations. The model calculates the chip thickness by considering the trajectory of the tool tip while the tool rotates and moves ahead continuously. The proposed approach allows the calculation of the cutting forces to be done accurately in typical micro-end-milling operations with very aggressively selected feed per tooth to tool radius (f_t/r) ratio. The difference of the simulated cutting forces between the proposed and conventional models can be experienced when f_t/r is larger than 0.1. The estimated cutting force profile of the proposed model had good agreement with the experimental data.