Development of a framework to automate process planning functions and to determine machining parameters

A methodology is developed to perform the machining parameter selection function in an automated process planning environment. At the same time, a framework is developed to perform the machine and tool selection function of process planning. The purpose of the machining parameter selection function, as envisioned in this study, is to determine the number of roughing and finishing passes needed, depth of cut, cutting speed, and feed for each pass depending on the part geometry, tolerance, the available machine types and appropriate grades of cutting tools. A new stable cutting region constraint is proposed. A machining-related cost based on tolerance, called measurement and adjustment cost, is derived and included in the formulation. A way to determine maximum roughing depth of cut is indicated. Finally, a part example is used to demonstrate the concepts for a turning operation.     

Joint optimisation of production rate and preventive maintenance in machining systems

This paper studies an integrated control strategy of production and maintenance for a machining system which produces a single type of product to meet the constant demand. Different from previous research, we assume in this study that during the production, the production rate not only influences the life of cutting tool, but also the reliability of the machine. Both the replacement of cutting tool and the preventive maintenance (PM) of machine are considered in this paper. The machine is preventively maintained at the Nth tool replacement or correctively repaired at the machine failure, whichever occurs first. PM and corrective repair may cause shortage which can be reduced by controlling inventory. There are two decision variables p and N, where p denotes the production rate and N denotes the number of cutting tool replacement before the PM is performed. An integrated model is developed to simultaneously determine the optimal production rate and PM policy that minimise the total expected cost per unit item produced. Finally, an illustrative example and sensitivity analysis are given to demonstrate the proposed model.     

Optimal selection of cutting parameters in multi-tool milling operations using a genetic algorithm

In the milling of large monolithic structural components for aircraft, 70–80% of the total cut volume is removed using high-speed roughing operations. In order to achieve the economic objective (i.e. optimal part quality in minimal machining time) of this process, it is necessary to determine the optimal cutting conditions while respecting the multiple constraints (functional and technological) imposed by the machine, the tool and the part geometry. This work presents a physical model called GA-MPO (genetic algorithm based milling parameter optimisation system) for the prediction of the optimal cutting parameters (namely, axial depth of cut (a _p), radial immersion (a _e), feed rate (f _t) and spindle speed (n)) in the multi-tool milling of prismatic parts. By submitting a preliminary milling process plan (i.e. CL data file) generated by CAM (computer-aided manufacturing) software, the developed system provides an optimal combination of process parameters (for each machining feature), respecting the machine–tool–part functional/technological constraints. The obtained prediction accuracy and enhanced functional capabilities of the developed system demonstrate its improved performance over other models available in the literature.     

Optimization of ultrasonic machining for fabricating square holes using the Taguchi methods

Abstract

One of the solutions for making quality molded plastic products is to adopt post-machining to avoid the warpage problem. Considering many traditional and non-traditional machining techniques, we have suggested that ultrasonic machining may be suitable for fabricating square holes in plastics. Because many factors are involved in ultrasonic machining, the objective of this research was to find the optimal conditions for ultrasonic machining by the Taguchi methods. The plastic material utilized in the study was PMMA, and there were two quality characteristics adopted for the experiment. One was the area deviation percentage (ADP) of square holes created by ultrasonic machining, and the other was the average thrust force during the machining process. Four factors, which were tool material, tool type, abrasive, and feed rate were considered in the experiment. The optimal conditions for ultrasonic machining to minimize ADPs and to reduce the average thrust force, respectively, are reported in the paper. These conditions can be applied to machine PMMA using ultrasonic machining in future applications.     

Optimization of Dry Machining Parameters for High-Purity Graphite in End-Milling Process

The machining factors affecting the tool wear and surface finish produced in the end milling process are generally the cutting speed, the feed rate, the depth of cut, etc. This paper describes a study that identifies the influence of the machining parameters on the groove width and the surface roughness average for the end-milling of high-purity graphite under dry machining conditions. The experiments are based on an orthogonal arrays and grey relational analysis method is then applied to determine an optimal machining parameter setting. The dimensional accuracy of the groove width and the surface roughness average are selected as the quality targets. In this study, the feed rate is the most significant controlled factors for the machining process according to the weighted sum grade of the Δ and the R _a .     

Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods

Nowadays machining of materials in their hardened state, also called hard machining, is a challenge in production of tools and molds. It has some advantages such as lower process time and lower manufacturing cost when compared to conventional machining. In machining of hard workpiece materials, however, very high stresses act on the tool holder through the cutting tool. These stresses necessitate the tool holder to have some specific properties. Especially in hard milling, the tool holder should have high stiffness and should be able to dissipate the energy generated during interrupted cutting. Material cost of the tool holder is also important since lower costs provide a competitive advantage for manufacturers. The material selection for the tool holder should be conducted considering aforementioned requirements. To tackle the difficulty of the material selection with specific properties from a large number of alternatives, multi-criteria decision-making (MCDM) methods have been used. In this paper a decision model including extended PROMETHEE II (EXPROM2) (preference ranking organization method for enrichment evaluation), TOPSIS (technique for order performance by similarity to ideal solution) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje) methods were used for the selection of the best material for the tool holder used in hard milling. The criteria weighting was performed by compromised weighting method composed of AHP (analytic hierarchy process) and Entropy methods. The candidate materials were ranked by using these methods and the results obtained by each method were compared. It was confirmed that MCDM methods can be used for the solution of real time material selection problems. Tungsten carbide–cobalt and Fe–5Cr–Mo–V aircraft steel were found as the best materials for the tool holder production. The obtained results are found to be rather satisfactory and can be used in design stage of hard machining operations.     

The machine loading and tool allocation problem in a flexible manufacturing system

In this paper the machine loading and tool allocation problem of an FMS is discussed, A mathematical model is developed to determine the routings of parts through the machines and to allocate appropriate cutting tools to each machine to achieve minimum overall machining cost. Computational experience with this model is presented under various system and operation parameter values. Computational refinements based on lagrangean relaxation are also discussed.     

Optimal machining conditions and buffer space size for the two-stage case

This paper presents a model for calculating optimal cutting speeds and tool replacement policies for both operations of a two-stage machining problem when the unit cost is minimized or the profit rate is maximized. The tool life is assumed to be a stochastic variable and penalty costs are imposed for tool failures during production. The optimal size of buffer space between the two machines is also calculated analytically. It is shown that the unit cost increases as the tool variability and/or the penalty cost increase. The cutting speeds and tool replacement policies on both operations depend strongly on the tool variability and the penalty cost. The cutting speeds differ from those determined independently for each operation. Finally, the optimal buffer space size is the one necessary to keep the critical machine running when there is a tool change on the non-critical machine, and its optimal size can be calculated analytically.     

Optimization of multipass turning operations with genetic algorithms

The paper proposes a new optimization technique based on genetic algorithms for the determination of the cutting parameters in multipass machining operations. The cutting process simultaneously considers multipass rough machining and finish machining. The optimum machining parameters are determined by minimizing the unit production cost subject to practical machining constraints. The cutting model formulated is a non-linear-constrained programming (NCP) problem with 20 machining parameter constraints. Experimental results show that the proposed genetic algorithm-based procedure for solving the NCP problem is both effective and efficient, and can be integrated into an intelligent manufacturing system for solving complex machining optimization problems.     

Optimisation of machining parameters by criterion of maximum productivity

The optimisation of machining parameters for machine tools using the criterion of maximum productivity rate is not a new problem – many unresolved issues remain. The intensification of machining processes leads to changes in the productivity rate; analytically defining optimal machining parameters for the maximum productivity rate in real-world manufacturing processes is, therefore, an important problem that needs to be addressed. This paper aims to formulate a mathematical model for the optimisation of cutting processes on machine tools based on the criterion of maximum productivity rate. The mathematical model is based on technological data, machining and reliability parameters of the machine tool units. Several practical applications are discussed.     

Cost of cutting tools and total machining costs as a function of the cutting-tool reliability in automatic flow lines

This paper explains an approach to establishing the total reliability of cutting tools on feeding automatic machine lines as a function of time, and based on this the establishment of cutting-tool costs and total machining costs as a function of cutting-tool reliability.

Based on observations, made over a long period, cutting-tool reliability is established for certain operations (Weibull distribution), and the total reliability for the whole line established as the product of the individual operations.

Cutting-tool costs are shown as the sum of tool replacement, sharpening and depreciation costs, and total machining costs as the sum of machining values, machine tools and cutting tools.

By variation of the cutting-tool replacement time, the tabular change dependencies are obtained as a function of cutting-tool replacement time. By elimination of replacement time from the above dependencies, the cutting-tool cost changes and machining costs are obtained as a function of the cutting-tool reliability. These could prove valuable in the choice of optimal cutting-tool replacement times.     

Optimal machining conditions with costs of quality and tool maintenance for turning

Although there is a voluminous literature on machine tool economics, the cost of machining quality has received little attention. In this paper, we develop a timedynamic economic model for single-pass turning. The model incorporates considerations on the stochastic nature of tool-life and such tool maintenance activities as tool replacement and tool regrinding. We model the quality cost of tool-cutting in terms of deviation from target roughness and deviation from target dimension. The cost of deviation is either the Taguchi type under continuous assumption or in terms of the cost to the entire workpiece under discrete assumption. The connection between quality cost and tool maintenance cost is explicitly addressed. Essentially, quality cost as well as machining cost is a function of two sets of decisions: machining conditions as defined by the choice of cutting speed and feed rate (depth of cut is a constant in single-pass operations), and the condition of the cutting tool as defined by the tool retirement and regrinding policy. The cost of tool failure is also incorporated.     

A simulated annealing-based optimization algorithm for process planning

Computer-aided process planning (CAPP) in the past typically employed knowledge-based approaches, which are only capable of generating a feasible plan for a given part based on invariable machining resources. In the field of concurrent engineering, there is a great need for process planning optimization. This paper describes an approach that models the constraints of process planning problems in a concurrent manner. It is able to generate the entire solution space by considering multiple planning tasks, i.e. operations (machine, tool and tool approach direction), selection and operations sequencing simultaneously. Precedence relationships among all the operations required for a given part are used as the constraints for the solution space. The relationship between an actual sequence and the feasibility of applying an operation is also considered. An algorithm based on simulated annealing (SA) has been developed to search for the optimal solution. Several cost factors including machine cost, tool cost, machine change cost, tool change cost and set-up change cost can be used flexibly as the objective function. The case study shows that the algorithm can generate highly satisfying results.     

An analytical tim6 domain evaluation of the cutting forces in BTA deep hole machining using the thin shear plane model

This paper presents an analytical approach to describe the cutting forces in 1ST A deep hole machining processes in the time domain. The method takes into account the effect of different machining conditions. Since the cutting velocities employed in BTA deep hole machining process are relatively high, and since small chips are produced due to the presence of tool chip breakers, the analysis is developed on the basis of the thin shear plane model.

The cutting velocity is a linear function of radius and the rake angle. Cutting is different in the two regions of the cutting tool, so the total cutting force acting on the cutting tool is determined by integrating the force on a small incremental thickness of the cutting tool. This approach, to predict the value of the cutting forces without resorting to any empirical techniques, clearly illustrates the effect of various system parameters on the machining process.

The resultant force system on a new BTA cutting tool consists of an axial force and torque. But with the increase in the number of holes bored, not only does the cutting profile deteriorate, but the wear pads do too. The resultant force system will then consist of three force components and a torque, due to the fact that the forces are not balanced at the wear pads. Under such conditions, the cutting force equations derived in the latter half of the paper, coupled with the properties of the randomly varying component, can be used as the forcing function on the machine tool to evaluate not only the response but also the regions of stability and instability during the machining.     

Assessment of the effectiveness of a spindle power signal for tool condition monitoring in machining processes

Less expensive and ‘readily available’ process monitoring techniques are needed to be effective in industrial machining processes. Spindle motors on modern computer numerical control machine tools allow easy access to the monitoring of spindle power. Whilst a spindle power signal fulfils the requirements for simple process monitoring, such a signal can trigger ‘machine alarms’ when process malfunctions occur. Little analysis has been done to assess the sensitivity of a spindle power signal relative to interrupted/continuous cutting processes. This paper aims to assess the effectiveness of a spindle power signal for tool condition monitoring in three machining processes: milling, drilling and turning. Based on cutting force/torque, the cutting power was calculated and a comparison between the theoretical cutting power and the spindle power signal was performed. Tool condition monitoring using spindle power could be successful in continuous machining processes (turning and drilling), while for discontinuous machining operations (milling), the spindle power signal showed reduced sensitivity to detect small uneven events such as chipping of one tooth. The results were used to define the sensitivity limitations when using a spindle power signal for tool condition monitoring on different computer numerical control machining centres where continuous and discontinuous machining operations are performed.     

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.     

An experimental investigation for the stochastic modelling of the resultant force system in BTA deep hole machining

This paper presents the measurement and a statistical analysis of the resultant force system, consisting of an axial force and torque, in BTA deep hole machining. The measurements were performed using a specially designed two-component piezoelectric dynamometer and adopting the rotating cutting tool-stationary workpiece procedure. The dynamometer was calibrated for static and dynamic outputs and techniques were employed for increasing the measuring accuracy and reducing the cross-interference by obtaining the elements of the system transfer function. Experiments were carried out to measure the mean values and the dynamic fluctuations of the axial force and torque. The recorded data was processed and analysed to establish all major statistical properties of the axial force and torque. Results show that the dynamic fluctuations of the axial force and torque in BTA deep hole machining can be represented by a stationary wideband process with a gaussian density distribution function. Such a mathematical model is essential for evaluating the dynamic response of the machine-workpiece system as well as the true motion of the cutting tool tip, and to establish the reliability of the machining process.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Performance-based optimal selection of cutting conditions and cutting tools in multipass turning operations using genetic algorithms

The paper presents a new methodology involving the use of genetic algorithms for the selection of optimum cutting conditions and cutting tools in multipass turning operations based on a comprehensive optimization criterion. The optimization objective includes the contributing effects of all major machining performance measures. A hybrid process model, based on metal-cutting theories and numerical interpolation from an experimental database, predicts major machining performance measures. Presented case studies demonstrate the application of the new methodology for the determination of optimum cutting conditions and the selection of cutting tool inserts.     

高速激光切割机床数控加工态势识别系统

当前方法设计的系统对机床数控加工态势进行识别时,没有对噪声进行回放,对切割平面度、切割定位精度、机床平稳性进行识别时,识别结果不准确,存在识别准确率低的问题。该文提出高速激光切割机床数控加工态势识别系统设计方法,首先,通过运动控制卡,伺服控制模块及加工态势识别模块构成高速激光切割机床数控加工态势识别系统的架构;其次,在加工态势识别模块中设计系统初始化、态势识别、自动诊断报警、运动控制、图形转码和G代码编译等子模块,在此基础上,通过噪音采集、噪音回放和信号分析等方法提高加工态势识别的准确率;最后,采用HMM算法构建高速激光切割机床数控加工态势识别模型,实现加工态势识别。实验结果表明,加工态势设计方法设计的系统可有效识别机床的切割平面度、切割定位精度和机床平稳性,识别准确率较高。