Selection Of Optimal Process Parameters Research Articles

Study on tooth profile error of cylindrical gears manufactured by flexible free-form milling

The free-form milling of gears is a modern and flexible machining process that allows the manufacturing of external gears by using standard milling tools on universal machine center. In this paper, tooth profile error of cylindrical gears manufactured by flexible free-form milling is studied. Firstly, a mathematical model is established to investigate the tool path with respect to geometric characteristic of gear surface according to the theory of spatial form milling. Then, the influence of tooth profile error related to the processing parameters such as the type of tool, the feeding strategy of tool, and the basic parameters of gear is studied. Finally, for a validation, an experiment is performed using a universal multiaxis machine center with standard end mill. The experimental results show that free-form milling of gears is an effective and economical solution for manufacturing high-quality gears while a universal machine center is available. Furthermore, the study results support the engineers in the selection of optimal processing parameters by using free-form milling method.

A comprehensive review of micro-grinding: emphasis on toolings, performance analysis, modeling techniques, and future research directions

Micro-grinding is a tool based mechanical micromachining process which is mostly applied to create and finish 3D micro-features on hard and brittle materials such as glass, silicon, alumina, etc. Miniature-sized abrasive tool comes in physical contact with the workpiece and removes the unwanted material with mostly nanometric undeformed chip thickness and hence can achieve ductile mode cutting. Electroplated and metal bonded sintered abrasive tools are the most researched micro-grinding tools in terms of their fabrication techniques evolution, design modification, and processing of different materials. In the last decade, researchers have thoroughly investigated the micro-grinding process mechanics and identified different issues along with controlling strategies to improve the process performance. Through experimental and analytical studies, it was shown that process performance could be improved through proper tool modifications, optimum selection of process parameters, proper lubrication media, and most importantly by the development of dedicated machine tools. This paper describes the micro-grinding process mechanism considering tool-workpiece interaction. Thereafter, a comprehensive review on micro-grinding tool manufacturing technologies, issues and controlling strategies, proper machining parameter selection, modeling techniques, and micro-feature generation in various materials is presented. After critical examination of the state of the art of this process, challenges, and limitations in the process establishment and applications are derived. Future research scopes in different aspects of the said process are suggested so as to multiply the process utility.

Optimization of roller burnishing process using Kriging model to improve surface properties

The objective of this work is to investigate the influences of three machining factors (burnishing speed V, feed rate f, and depth of penetration a) on the improved rate of arithmetic average roughness Δ Ra, improved rate of maximum height roughness Δ Ry, and improved rates of surface hardness Δ SH. The internal roller burnishing experiments were conducted with the aid of the computer numerical control machining center and Box–Behnken experimental design. The Kriging models were used to render the highly nonlinear relationships between inputs and outputs. An integrative approach combining a Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by Similarity to Ideal Solution was adopted to generate a set of feasible optimal solutions and determine the best machining conditions. The scanning electron microscopy images were depicted to investigate the surface morphology at the different conditions. The X-ray diffraction was applied to measure the compressive stresses at the external surface. The results showed that the predicted values of the objectives have good agreement with the experimental ones. High surface quality is characterized by an improved average roughness of 95.80%, an enhancement in the maximum roughness of 91.98%, and an improvement in surface hardness of 45.44%, compared to pre-machined surfaces. The selection of optimum process parameters could help the burnishing operators to save the machining costs and time. The combination of Kriging model, Non-dominated Sorting Genetic Algorithm II, and Technique for Order Preference by Similarity to Ideal Solution is considered as an intelligent approach for modeling and optimization of burnishing processes.

Electrochemistry-enabled atomic layer deposition of copper: Investigation of the deposit growth rate and roughness

Electrochemical atomic layer deposition (e-ALD) presents a low-cost, environmentally friendly and scalable approach for the atomically precise fabrication of semiconductor interconnect materials. In the present work, growth rate and roughness evolution during e-ALD of copper (Cu) were analyzed using anodic stripping coulometry, atomic force microscopy, and diffusion-reaction modeling. Through considerations of the unsteady-state diffusional transport of species and their associated surface electrochemical reactions, a semianalytical e-ALD process model is developed. The model enables quantification of the e-ALD growth rate and the deposit surface roughness as a function of various e-ALD process parameters, i.e., electrolyte composition and deposition time. Model predictions are compared with experimental data and good agreement is noted. The model is used to develop guidelines for the selection of optimal process parameters that enable e-ALD of Cu with minimal roughness evolution and a deposit growth rate close to one monolayer-per-cycle.

Use of a design of experiments approach to optimise production of a recombinant antibody fragment in the periplasm of Escherichia coli: selection of signal peptide and optimal growth conditions

Production of recombinant proteins such as antibody fragments in the periplasm of the bacterium Escherichia coli has a number of advantages, including the ability to form disulphide bonds, aiding correct folding, and the relative ease of release and subsequent capture and purification. In this study, we employed two N-terminal signal peptides, PelB and DsbA, to direct a recombinant scFv antibody (single-chain variable fragment), 13R4, to the periplasm via the Sec and SRP pathways respectively. A design of experiments (DoE) approach was used to optimise process conditions (temperature, inducer concentration and induction point) influencing bacterial physiology and the productivity, solubility and location of scFv. The DoE study indicated that titre and subcellular location of the scFv depend on the temperature and inducer concentration employed, and also revealed the superiority of the PelB signal peptide over the DsbA signal peptide in terms of scFv solubility and cell physiology. Baffled shake flasks were subsequently used to optimise scFv production at higher biomass concentrations. Conditions that minimised stress (low temperature) were shown to be beneficial to production of periplasmic scFv. This study highlights the importance of signal peptide selection and process optimisation for the production of scFv antibodies, and demonstrates the utility of DoE for selection of optimal process parameters.

Optimization of resistance spot welding process for real unconstrained and constrained scenarios using cuckoo search algorithm

The optimum selection of process parameters is essential for a designer, as it incur high initial investment, power and; operating and maintenance costs. This article presents optimisation aspects of resistance spot welding process using cuckoo search algorithm. The parameters are studied critically to obtain the optimum settings satisfying one or more quality characteristics. Some constrained and unconstrained single as well as multi-objective optimisation problems related to the practical case studies are solved. The obtained results are compared with those derived by the past researchers. It is found that the present results are better than the previous results in all the cases. The solved examples illustrated a novel effectiveness of the presented algorithm and its application suitability in the field of resistance spot welding. [Submitted 18 November 2017; Accepted 30 June 2018]

Machining performance optimisation of MQL-assisted turning of Inconel-825 superalloy using GA for industrial applications

This work investigates machining performance of Inconel 825 using chemical vapour deposition (CVD) TiN coated inserts in minimum quantity lubrication (MQL) approach. Three important measures in relation to machining of hard-to-cut materials viz., surface roughness (Ra), tool wear (VB), and cutting temperature (CT) have been considered for investigation. The response surface methodology (RSM) modelling and relationship between input and output factors is studied. The machining parameters are optimised individually as well as simultaneously using genetic algorithm (GA). The minimum Ra of 0.39 μm, VB of 15.37 μm and CT of 56.47°C was obtained. The significant contribution of this research is that the values of Ra, VB and CT obtained are quite low when compared to those reported in literatures. For the application in manufacturing industries, a technology table is generated for selection of optimum process parameters having minimum VB or minimum CT satisfying the desired value of surface finish of components produced.

Optimisation of resistance spot welding process for real unconstrained and constrained scenarios using cuckoo search algorithm

The optimum selection of process parameters is essential for a designer, as it incur high initial investment, power and; operating and maintenance costs. This article presents optimisation aspects of resistance spot welding process using cuckoo search algorithm. The parameters are studied critically to obtain the optimum settings satisfying one or more quality characteristics. Some constrained and unconstrained single as well as multi-objective optimisation problems related to the practical case studies are solved. The obtained results are compared with those derived by the past researchers. It is found that the present results are better than the previous results in all the cases. The solved examples illustrated a novel effectiveness of the presented algorithm and its application suitability in the field of resistance spot welding. [Submitted 18 November 2017; Accepted 30 June 2018]

A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning

Selection of optimum process parameters is often regarded as an effective strategy for improving energy efficiency during computer numerical control (CNC) turning. Previous optimization methods are typically developed for specific machining configurations. To generalize the energy-aware parametric optimization for multiple machining configurations, we propose a two-stage knowledge-driven method by integrating data mining (DM) techniques and fuzzy logic theory. In the first stage, a modified association rule mining algorithm is developed to discover empirical knowledge, based on which a fuzzy inference engine is established to achieve preliminary optimization. In the second stage, with the knowledge obtained by investigating the effects of parameters on specific energy consumption covering a variety of configurations, an iterative fine-tuning is carried out to realize Pareto-optimization of turning parameters for minimizing specific energy consumption and processing time. The simulation results show that the method has a high potential for enhancing energy efficiency and time efficiency in turning system. Furthermore, compared with three heuristic optimization techniques, i.e. Genetic Algorithm, Ant Colony Algorithm and Particle Swarm Algorithm, the proposed method demonstrates certain superiority.

Sustainable machining: an experimental investigation and optimization of machining Inconel 825 with dry and MQL approach

Sustainability is a vital issue for present and future generation, and it aims to obtain overall efficiency in terms of economic, environmental and social aspects. Inconel 825 belongs to the family of nickel-based super alloy and is widely used in the chemical and marine industries. This work attempts to investigate machining performance of Inconel 825 using physical vapor deposition-titanium nitrate inserts, with a focus on sustainable machining. The effect of cutting parameters, viz. cutting speed (v), feed (f) and depth of cut (d) on three aspects of sustainability has been explored in two different machining environments, viz. dry and minimum quantity lubrication (MQL). The experimental results show a significant improvement in MQL machining and tool wear, and cutting power is reduced by 16.57 and 8.47%, respectively, and surface roughness is improved by 10.41%. The interacting effects of parameters on responses are studied using 3-D surface plots; it shows cutting speed and feed are found as dominating parameters on all the three responses. The novelty of this work is to optimize the process for the sustainable production of components by optimizing the process parameters with multiple and conflicting objectives. The sustainable optimization using genetic algorithm provides surface roughness (Ra) as 0.49 µm, tool flank wear (VB) as 110.68 µm and cutting power (P) as 5.44 kW with better convergent capability having 4% deviation. For the application of manufacturing industry, an optimization table is generated for selection of optimum process parameters for achieving desired surface roughness with minimum VB or minimum P.

Production of Single Tracks of Ti-6Al-4V by Directed Energy Deposition to Determine the Layer Thickness for Multilayer Deposition.

Directed Energy Deposition (DED), which is an additive manufacturing technique, involves the creation of a molten pool with a laser beam where metal powder is injected as particles. In general, this technique is employed to either fabricate or repair different components. In this technique, the final characteristics are affected by many factors. Indeed, one of the main tasks in building components by DED is the optimization of process parameters (such as laser power, laser speed, focus, etc.) which is usually carried out through an extensive experimental investigation. However, this sort of experiment is extremely lengthy and costly. Thus, in order to accelerate the optimization process, an investigation was conducted to develop a method based on the melt pool characterizations. In fact, in these experiments, single tracks of Ti-6Al-4V were deposited by a DED process with multiple combinations of laser power and laser speed. Surface morphology and dimensions of single tracks were analyzed, and geometrical characteristics of melt pools were evaluated after polishing and etching the cross-sections. Helpful information regarding the selection of optimal process parameters can be achieved by examining the melt pool features. These experiments are being extended to characterize the larger blocks with multiple layers. Indeed, this manuscript describes how it would be possible to quickly determine the layer thickness for the massive deposition, and avoid over or under-deposition according to the calculated energy density of the optimum parameters. Apart from the over or under-deposition, time and materials saving are the other great advantages of this approach in which the deposition of multilayer components can be started without any parameter optimization in terms of layer thickness.

Machining of AISI D2 Tool Steel with Multiple Hole Electrodes by EDM Process

In recent years, with the increasing of technology the demand for machining processes is increasing for the newly developed materials. The conventional machining processes are not adequate to meet the accuracy of the machining of these materials. The non-conventional machining processes of electrical discharge machining is one of the most efficient machining processes is being widely used to machining of high accuracy products of various industries. The optimum selection of process parameters is very important in machining processes as that of an electrical discharge machining as they determine surface quality and dimensional precision of the obtained parts, even though time consumption rate is higher for machining of large dimension features. In this work, D2 high carbon and chromium tool steel has been machined using electrical discharge machining with the multiple hole electrode technique. The D2 steel has several applications such as forming dies, extrusion dies and thread rolling. But the machining of this tool steel is very hard because of it shard alloyed elements of V, Cr and Mo which enhance its strength and wear properties. However, the machining is possible by using electrical discharge machining process and the present study implemented a new technique to reduce the machining time using a multiple hole copper electrode. In this technique, while machining with multiple holes electrode, fin like projections are obtained, which can be removed easily by chipping. Then the finishing is done by using solid electrode. The machining time is reduced to around 50% while using multiple hole electrode technique for electrical discharge machining.

DETERMINING THE OPTIMAL DIMENSIONS FOR A MECHANISM FROM A WHEELCHAIR USING LABVIEW

The optimum selection of process parameters plays a very important role to ensure quality of product, to reduce the machining cost and to increase the productivity. Customizing a product to improve performance requires a reliable method to measure performance, in order to evaluate the effects that design changes have on them. A product development process can be thought as a sequence of activities to be completed. This paper has the purpope to analyse the influence of the parameters for equipment from a wheelchair that will overcome obstacles. The fuzzy logic is used through specialized software to optimize several parameters of the proposed mechanism with the purpose to ensure the stability through maintaining horizontality of the equipment. Comparing experimental values with those obtained through the software model, is determined whether the model used is consistent.

Application of PSI Methods to Select FDM Process Parameter for Polylactic Acid

The aim of the present study is to apply preference selection index (PSI) method for selection of optimal process parameters of Fused Deposition Modelling (FDM) for Polylactic Acid Material. Experts or users related to FDM technology are always interested to select an appropriate optimal process parameters involve in manufacturing of component which directly affect the response process parameters such as tensile strength, tensile module, compressive strength, compressive module, surface roughness, etc. In present study one case study is considered from them literature to study the suitability of PSI method for selection of FDM process parameter for Polylactic Acid Material.

An optimization and experimental analysis of plasma arc cutting of Hardox-400 using Taguchi based desirability analysis

The plasma cutting process is widely used in manufacturing or fabrication units for cutting the metal plates. The optimum selection of process parameters is essential for smoother and faster cutting. In this research work, experimental investigation of plasma arc cutting has been carried out. The workpiece material was taken as Hardox 400 which is very high resistance steel. This material has good bendability, high toughness and better weldability. Hardox 400 variously used in manufacturing of Dump truck, Front loaders, Barges and Buckets. Argon is used as the inert gas and oxygen gas was taken for shielding. During the cutting process, the parameters for the operation are varied cutting current, supply gas pressure, cutting speed and Standoff distance. The material removal rate, surface roughness chamfer and kerf are considered as the output responses. The individual effect of cutting parameters related to the output responses also described. Taguchi based desirability analysis (TDA) was observed to find the optimal cutting conditions for improving the quality characteristics of the plasma arc cutting process. The goal of the experiment was to maximize the MRR and minimize the other output responses. The confirmation test was obtained according to the optimal setting of process parameter in achieving the improved efficiency of plasma arc cutting machine. The result of this analysis will offer a better database for the industries. The outcomes of the investigations clearly show that the specific range of input process parameter achieved the improved machinability.

GTA-based framework for evaluating the effectiveness of material handling equipment's in FMS environment

In this paper, a methodology to evaluate the effectiveness of material handling equipments variables using graph theoretic approach (GTA) is presented. Variables affecting the effectiveness of material handling equipments and their interactions are analysed by developing a mathematical model using GTA. Permanent function performance index is obtained from the matrix model developed from the digraph. This permanent function value ranks the equipment variables which help in selection of optimum process parameters during flexible and automated material handling system adoption.

Application of MCDM Method for the Selection of Optimum Process Parameters in Turning Process

Turning process is one of the fundamental machining operations and its parameters optimization leads to better machining performance. This study applies Additive Ratio Assessment (ARAS) method to determine the optimum process parameters and suitable coated tool in turning operation of AISI 4340 steel. The process parameters considered are cutting speed, feed rate, depth of cut and coated tools. The objective is to minimize surface roughness, micro hardness and maximize Material Removal Rate (MRR) simultaneously. The result revealed that this proposed method is appropriate for solving multi criteria optimization of process parameters.

Performance improvement of machining parameters in electrical discharge machining process of tool steel 2714

Electrical discharge machining (EDM) is an extremely prominent machining process among newly developed non-traditional machining techniques for difficult-to-cut materials. Though a lot of research has been done to improve the process performance, optimal selection of process parameters for the best performance measures still remains a challenge. In this work, the performance of electrical discharge machining process when cutting hot work tool steel 2714 with copper electrodes has been investigated. Orthogonal array of L27 (3*4) based on the Taguchi experimental design is utilised to plan the experiments. Raw data is assessed by the analysis of variance (ANOVA) to find optimal conditions for response parameters. The main machining parameters such as pulse-on time, pulse-off time, discharge current and average machining voltage are chosen to determine the EDM response parameters such as material removal rate, surface roughness and gap size. Response tables and graphs are used to find the optimal parameter level in the EDM process.

Performance improvement of machining parameters in electrical discharge machining process of tool steel 2714

Electrical discharge machining (EDM) is an extremely prominent machining process among newly developed non-traditional machining techniques for difficult-to-cut materials. Though a lot of research has been done to improve the process performance, optimal selection of process parameters for the best performance measures still remains a challenge. In this work, the performance of electrical discharge machining process when cutting hot work tool steel 2714 with copper electrodes has been investigated. Orthogonal array of L27 (3*4) based on the Taguchi experimental design is utilised to plan the experiments. Raw data is assessed by the analysis of variance (ANOVA) to find optimal conditions for response parameters. The main machining parameters such as pulse-on time, pulse-off time, discharge current and average machining voltage are chosen to determine the EDM response parameters such as material removal rate, surface roughness and gap size. Response tables and graphs are used to find the optimal parameter level in the EDM process.

PSI and TOPSIS Based Selection of Process Parameters in WEDM

Wire electric discharge machining (WEDM) is a nontraditional machining process for machining conductive materials with complex and intricate shapes with a high surface finish and dimensional accuracy. The decision making for the selection of the best set of combinations of input process parameters is a major challenge. Therefore a proper optimization tool should be used for the optimal selection of process parameters. The resent work deals with the comparative study of Preferential Selection Index (PSI) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for the selection of process parameters during machining of EN31 tool steel. Four input parameters- Pulse on Time (Ton ), Pulse off Time (Toff ), Servo Voltage (SV) and the Wire tension (WT) are considered. Surface roughness and material removal rate are the measured output responses. Taguchi L9 orthogonal array is used for developing the experimental design. Three levels of each control factor are considered. The results show that a single parameter alone does not have a significant influence on the output responses. Thequality of the output responses depends on the combination of the various set of input parameters. The best set of combination suggested from the current input parameters for machining of EN31 Tool Steel by Wire EDM Process is found to be Pulse on Time (Ton )= 15μs, Pulse Off Time (Toff )=35μs, Servo Voltage (SV)=40V and the Wire tension (WT)=5kgf from both PSI as well as TOPSIS techniques. Confirmation experiments are performed to validate the optimal results.