Genetic-algorithm-based multi-objective optimization of the build orientation in stereolithography

Build orientation is an important fabrication parameter in layer manufacturing (LM) since it affects the part fabrication accuracy, cost, and time. Despite its importance, orientation selection relies quite heavily on the experience and skill of the operator of the LM system, which does not guarantee optimality of the decision. In the present work, a decision support system that automates the orientation selection task is proposed. The proposed system utilizes genetic algorithms and multi-criteria optimization techniques for the definition of (near) optimum build orientation for parts fabricated with stereolithography. Build time, surface roughness, and post-processing time are considered as the main optimization criteria.     

A study on microinjection moulding using moulding blocks by additive micromanufacturing

Microinjection moulding is one of the most efficient replication methods for polymeric components in microsystems. The manufacturing of moulding blocks for complex geometries is resorting increasingly to the techniques of rapid prototyping. This development on the use of additive microtechnologies can promote the massification of microsystems within a shorter tooling development cycle time. However, the microinjection moulding process itself has mechanical and thermal demands that must be addressed and require specific consideration of the selection of the tool material. This constrains the selection of the best-suited additive manufacturing process. The current state of the art of additive manufacturing technologies at the micrometric scale favours laser sources to process layer by layer the media contained in a vat. The media type, the laser power and the laser spot size are parameters that can influence the replication tool tolerances and physical properties. This work explores the possibilities of two additive technology tooling approaches for microinjection moulding, using different materials. The research parameters included replication detailing onto the plastic part, surface roughness, microtool integrity and wear. The evaluation of these parameters was carried out using both optical and hybrid microscopy, a laser perthometer as a non-contact solution for surface roughness evaluation, scanning electron microscopy and X-ray spectroscopy. The results of this research work showed that the processed material and technology play an important role both on surface quality and tool life, enabling criteria definition for technology selection.     

Determining Build Orientation for Layer-Based Machining

Some inherent limitations exist in current layered manufacturing (LM) technologies (e.g. little choice of material, small part size, and poor surface quality) and traditional NC machining (e.g. the restriction of tool accessibility to internal features and small working area). In order to overcome these limitations, a rapid manufacturing method called robot-based layered manufacturing (RoLM) is developed. A robot with a milling cutter mounted on the end-effector is used to build a part layer by layer. Given a part model, the determination of build orientation is the first step in the manufacturing cycle and has a large effect on the surface quality and build time. In this paper, a method is proposed to determine the build orientation for RoLM by considering part accuracy and build time. Algorithms are developed to calculate tool accessibility, part stability, and the number of required support for overhangs.     

Determination of process parameters in stereolithography using neural network

For stereolithography process, accuracy of prototypes is related to laser power, scan speed, scan width, scan pattern, layer thickness, resin characteristics and etc. An accurate prototype is obtained by using appropriate process parameters. In order to determine these parameters, the stereolithography (SLA) machine using neural network was developed and efficiency of the developed SLA machine was compared with that of the traditional SLA. Optimum values for scan speed, hatching spacing and layer thickness improved the surface roughness and build time for the developed SLA.     

Surface roughness prediction in fused deposition modelling by neural networks

Fused deposition modelling is a proven technology for the fabrication of both aesthetic and functional prototypes. The obtainable roughness is the most limiting aspect for its application. The prediction of surface quality is an essential tool for the diffusion of this technology, in fact at product development stage, it allows to comply with design specifications and in process planning it is useful to determine manufacturing strategies. The existing models are not robust enough in predicting roughness parameters for all deposition angles, in particular for near horizontal walls. The aim of this work is to determine roughness parameters models reliable over the entire part surface. This purpose is pursued using a feed-forward neural network to fit experimental data. By the utilisation of an evaluation function, the best solution has been found. This has been obtained using a feed-forward neural network for fitting the experimental data. The best solution has been founded by using an evaluation function that we constructed. The validation proved the robustness of the model found to process parameters’ variation and its applicability to different FDM machines and materials.     

Research on rotary surface topography by orthogonal turn-milling

As a new technology in manufacturing, turn-milling broadens the application ranges of mechanical processing, wherein both cutting tool and workpiece are given a rotary motion simultaneously. The objective of the present work is to study workpiece surface topography during orthogonal turn-milling process. This study begins with two mathematical models, which describe theoretical surface roughness and topography of rotationally symmetrical workpiece. The models are built with the establishment of locus function according to orthogonal turn-milling principle. Then based on these models, the influence law of surface topography affected by various cutting parameters is found by some simulation methods. The law also matches with orthogonal turn-milling surface roughness and topography experiments. By analyzing the experimental results, some parameter selection criteria during orthogonal turn-milling processing are also proposed qualitatively and quantitatively. The comparison between the simulation and experimental results shows that a better surface quality and tiny oil storage structure can be obtained if the cutting parameters are chosen in reason. This conclusion provides a theoretical foundation and reference for the orthogonal turn-milling mechanism research.     

Model-based identification of tool runout in end milling and estimation of surface roughness from measured cutting forces

This paper presents a model-based approach for the identification of tool runout and the estimation of surface roughness from measured cutting forces. In the first part of the paper, the effect of tool runout on variations in the cutting forces and the effect on surface roughness generation are studied. Thereby, several influencing parameters are identified and examined systematically. Based on theoretical considerations, systematic relationships between tool runout, resultant process force variations, and surface roughness characteristics are deduced. The sensitivity of process force variation is investigated for varying runout parameters by experimental tests. In the next part, the model-based runout identification method is developed, which identifies runout parameters accurately from the measured process forces. The approach has been tested extensively and was verified by measured runout parameters and the correlation of surface roughness characteristics of the machined workpiece. The performance of the developed approach is demonstrated in the final part by comparing the result of the model-based surface reconstruction with the measured surface topography.     

Optimization and graphical representation of machining conditions in multi-pass turning operations

This paper describes a procedure to calculate the machining conditions, such as the cutting speed, feed rate and depth of cut for turning operations with minimum production cost or the maximum production rate as the objective function. The optimum number of machining passes and the depth of cut for each pass is obtained through the dynamic programming technique and optimum values of machining conditions for each pass are determined based on the objective function criteria by search method application to the feasible region. Production cost and production time values are determined for different workpiece and tool material for the same input data. In the optimization procedure, the objective functions are subject to constraints of maximum and minimum feed rates and speeds available, cutting power, tool life, deflection of work piece, axial pre-load and surface roughness. By graphical representation of the objective function and the constraints in the developed software, the effects of constraints on the objective function can be evaluated. The parameters that are assumed to be most effective in determining the optimum point can easily be changed and the revised graph can be inspected for possible improvements in the optimum value.     

Use of NC kernel data for surface roughness monitoring in milling operations

This work focuses on developing an application based on the information contained in the numerical control (NC) kernel for surface roughness monitoring of the part in process. A human?Cmachine interface (HMI) was developed in order to facilitate the interaction between the operator and the NC kernel with a graphical user interface working in the computer numerically controlled (CNC) screen. Experimentation was carried out in order to obtain the data to be modeled with artificial neural networks for surface roughness average parameter (Ra) predictions. Finally, a compact solution was implemented through global user data (GUD). Data from the HMI and from the kernel are collected in the GUD and analyzed with the artificial neural network. The application provides the surface roughness average parameter of the part in process and gives optimized parameters to the operator. Verification tests were carried out, showing accurate results. The use of the application developed in this research ensures the surface roughness Ra requirement, improves cutting parameters, reduces manual finishing operations and unacceptable parts at the end of the manufacturing process, and provides a solution implemented in the machine tool CNC screen without the need of any other external sensors.     

The development of a CAD environment to determine the preferred build-up direction for layered manufacturing

Stereolithography is a process used to produce polymer components rapidly directly from a computer representation of the part. There are several factors to be considered in order to make efficient use of the process. In particular, the build-up orientation of the part critically affects the part accuracy, the total build time and the volume of the support structures. The purpose of this study is to determine the optimal build-up part orientation for the StereoLithography Apparatus (SLA) process to improve part accuracy, and minimise the total build time and the volume of the support structure. Additionally, an algorithm is developed to calculate the staircase area, quantifying the process errors by the volume of materials supposed to be removed or added to the part, and the optimal layer thickness for the SLA system which can handle the variable layer thickness. Thus, the optimal part orientation is determined based on the user's selections of primary criteria and the optimal thickness of layers is calculated for any part orientation.     

Prediction of cutting force, tool wear and surface roughness of Al6061/SiC composite for end milling operations using RSM

The results of mathematical modeling and the experimental investigation on the machinability of aluminium (Al6061) silicon carbide particulate (SiCp) metal matrix composite (MMC) during end milling process is analyzed. The machining was difficult to cut the material because of its hardness and wear resistance due to its abrasive nature of reinforcement element. The influence of machining parameters such as spindle speed, feed rate, depth of cut and nose radius on the cutting force has been investigated. The influence of the length of machining on the tool wear and the machining parameters on the surface finish criteria have been determined through the response surface methodology (RSM) prediction model. The prediction model is also used to determine the combined effect of machining parameters on the cutting force, tool wear and surface roughness. The results of the model were compared with the experimental results and found to be good agreement with them. The results of prediction model help in the selection of process parameters to reduce the cutting force, tool wear and surface roughness, which ensures quality of milling processes.     

Comparative study of conventional and micro WEDM based on machining of meso/micro Sized Spur Gear

This paper discusses the comparison of micro machining process using conventional and micro wire electrical discharge machining (WEDM) for fabrication of miniaturized components. Seventeen toothed miniaturized spur gear of 3.5 and 1.2 mm outside diameter were fabricated by conventional and micro WEDM respectively. The process parameters for both conventional and micro WEDM were optimized by preliminary experiments and analysis. The gears were investigated for the quality of surface finish and dimensional accuracy which were used as the criteria for the process evaluation. An average surface roughness (R_a) of 50 nm and dimensional accuracy of 0.1–1 μm were achieved in micro WEDM. Whenever applied conventional WEDM for meso/micro fabrication, a R_a surface roughness of 1.8 μm and dimensional accuracy of 2–3 μm were achieved. However, this level of surface roughness and dimensional accuracy are acceptable in many applications of micro engineering. A window of conventional WEDM consisting of low energy discharge parameters is identified for micromachining.     

A study on the characteristic of parameters by the response surface method in final wafer polishing

As the level of Si-wafer surface directly affects device line-width capability, process latitude, yield, and throughput in fabrication of microchips, it needs to have ultra precision surface and flatness. Polishing is one of the important processing having influence on the surface roughness in manufacturing of Si-wafers. The surface roughness in final wafer polishing is mainly affected by the many process parameters. For decreasing the surface, the control of polishing parameters is very important. In this paper, the optimum condition selection of ultra precision wafer polishing and the effect of polishing parameters on the surface roughness were evaluated by using central composite designs such as the Box-Behnken method. Moreover, in accordance with variation of process variables, there is a temperature change on pad surface. And so, this paper also researches that this temperature variation affects surface roughness of Si-wafer.     

Signal analysis and real-time monitoring for wafer polishing processes using the ch computing environment

There are several processes used in the silicon wafer fabrication industry to achieve the planarity necessary for photolithography requirements. Polishing is one of the important processes which influence surface roughness in the manufacturing of silicon wafers. As the level of a silicon wafer surface directly affects device line-width capability, process latitude, yield, and throughput in the fabrication of microchips, it is necessary for it to have an ultra precision surface and flatness. The surface roughness in wafer polishing is affected by many process parameters. To decrease the surface roughness of the wafer, controlling the polishing parameters is very important. Above all, a real-time monitoring technology of the polishing parameters is necessary for the control. In this study, parameters affecting the surface roughness of the silicon wafer are measured in real-time. In addition comparing the predicted value is done according to the process parameters using the artificial neural network. Through these results, we conduct research on the efficient parameters of silicon wafer polishing. Required programs are developed using the Ch computing environment.     

Bayesian regularization-based Levenberg–Marquardt neural model combined with BFOA for improving surface finish of FDM processed part

Fused deposition modeling has a complex part building mechanism making it difficult to obtain reasonably good functional relationship between responses and process parameters. To solve this problem, present study proposes use of artificial neural network (ANN) model to determine the relationship between five input parameters such as layer thickness, orientation, raster angle, raster width, and air gap with three output responses viz., roughness in top, bottom, and side surface of the built part. Bayesian regularization is adopted for selection of optimum network architecture because of its ability to fix number of network parameters irrespective of network size. ANN model is trained using Levenberg–Marquardt algorithm, and the resulting network has good generalization capability that eliminates the chance of over fitting. Finally, bacterial foraging optimization algorithm which attempts to model the individual and group behavior of Escherichia coli bacteria as a distributed optimization process is used to suggest theoretical combination of parameter settings to improve overall roughness of part. This paper also investigates use of chaotic time series sequence known as logistic function and demonstrates its superiority in terms of convergence and solution quality.     

基于RSM的SiC单晶片表面粗糙度预测及参数优化

通过响应面分析法(RSM)对超声振动辅助金刚石线锯切割SiC单晶体的工艺参数进行分析和优化。采用中心组合设计实验,考察线锯速度、工件进给速度、工件转速和超声波振幅这4个因素对SiC单晶片表面粗糙度值的影响,建立了SiC单晶片表面粗糙度的响应模型,进行响应面分析,采用满意度函数(DFM)确定了切割SiC单晶体的最佳工艺参数,验证试验表明该模型能实现相应的硬脆材料切割过程的表面粗糙度预测。     

用高分辨率快速成形系统制作细微结构

针对制作参数对制作分辨率的影响,利用高分辨率激光快速成形系统进行了试验研究。研究表明,制作时激光功率与光束扫描速度的比值越小,则制作的单壁结构厚度越小。采用准确的线宽补偿、较小的激光功率及具有较高临界曝光量的光敏树脂,可制作出具有较小槽宽尺寸的细微槽状结构。制作出了壁厚为0.011 mm的单壁结构及槽宽为0.034 mm的槽状结构。试验结果表明,该快速成形系统有很高的制作分辨率,具有制作扫描平面内细微结构的能力。     

Machinability study of high speed steel for focused ion beam (FIB) milling process – An experimental investigation at micron/nano scale

Nowadays the attention is focused on machining of non-silicon materials for miniaturized devices. High speed steel (HSS) is a non-silicon tool material, which is used in metal cutting applications as well as in micro-medical applications. Focused ion beam (FIB) milling process is highly suited for the fabrication of micro tools and other micro devices manufactured from HSS material. This study investigates the machinability aspects of HSS for FIB milling process. Beam current, extraction voltage, angle of incidence, dwell time and percentage overlap between beam diameters are the FIB process parameters, which have been analyzed experimentally to optimize FIB milling process for maximum material removal rate and minimum surface roughness. Beam current is found as the most significant parameter for controlling the material removal rate and surface roughness.     

Precision surface characterization for finish cylindrical milling with dynamic tool displacements model

In this work a new approach to surface roughness parameters estimation during finish cylindrical end milling is presented. The proposed model includes the influence of cutting parameters, the tool’s static run out and dynamic phenomena related to instantaneous tool deflections. The modeling procedure consists of two parts. In the first stage, tool working part instantaneous displacements are estimated using an analytical model which considers tool dynamic deflections and static errors of the machine – tool-holder – tool system. The obtained height of the tool’s displacement envelope is then applied in the second stage to the calculation of surface roughness parameters. These calculations assume that in the cylindrical milling process, two different mechanisms of surface profile formation exist. Which mechanism is present is dependent on the feed per tooth and the maximum height of the tool’s displacement envelope. The developed model is validated during cylindrical milling of hardened hot-work tool steel 55NiCrMoV6 using a stylus profiler and scanning laser vibrometer over a range of cutting parameters. The surface roughness values predicted by the developed model are in good agreement with measured values. It is found that the employment of a model which includes only the effect of static displacements gives an inferior estimation of surface roughness compared to the model incorporating dynamic tool deflections.     

A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method

As a new technology that fabricates a three-dimensional (3D) physical model from computer-aided design (CAD) data using an additive process, rapid prototyping (RP) has been developed to reduce product development time and cost. Recently, many newly emerging techniques of RP have been commercialized worldwide. This paper deals with the selection of an optimal RP system that best suits the end use of a part by using multiple-attribute decision making and the test part designed with conjoint analysis to reflect users’ preference. Evaluation factors include only the major attributes that significantly affect the performance of an RP system such as accuracy, roughness, strength, elongation, part cost and build time. Crisp values such as accuracy and surface roughness are obtained with a new test part developed in this study. The part cost and build time are identified as falling within approximate ranges due to varying costs and many variable parameters. They are presented as linguistic values that can be described with triangular fuzzy numbers. Based on the evaluation values obtained, an appropriate RP process for a specific part application can be selected using a modified technique of order preference by a similarity to ideal solution (TOPIS) method given crisp data and linguistic variables as the alternatives of attributes. Finally, each attribute’s weight is assigned using a pairwise comparison matrix. Determined using these weights, the final ranking order aids in the selection of the RP system.