Showing papers in "Journal for Manufacturing Science and Production in 2008"
TL;DR: A356 is a high strength Aluminium-Silicon cast alloy used in food, chemical, marine, electrical and automotive industries as mentioned in this paper, and the experiments were conducted based on three factors, three-level, and central composite face centered design with full replications technique.
Abstract: A356 is a high strength Aluminium-Silicon cast alloy used in food, chemical, marine, electrical and automotive industries. Fusion welding of these cast alloys will lead to many problems such as porosity, micro-fissuring, hot cracking etc. However, Friction Stir Welding (FSW) can be used to weld these cast alloys without the above-mentioned defects. The FSW process parameters such as tool rotational speed, welding speed, axial force etc., play a major role in deciding the weld quality. The experiments were conducted based on three factors, three-level, and central composite face centered design with full replications technique and models were developed to predict the tensile strength of A356 alloy using, response surface methodology and artificial neural network (ANN). The results obtained through RSM were compared with ANN. It is found that the error rate predicted by the artificial network is smaller than predicted by the response surface methodology.
33 citations
8 citations
TL;DR: In this article, a robust static weight setting in a tobacco fabric is presented as an application on a real workshop, using real production data, and a reactive control, facing the variations in input quality, provides a significant improvement in the quality of the final production.
Abstract: In many industrial sectors, such as the chemical industry, weight measures are used in order to control the quantity of consumed raw material. This parameter also takes a fundamental part in the product quality, as the correct transformation process is based upon a given percentage of each essence. Thus, weight regulation increases the global productivity of the system by decreasing the quantity of rejected products. This paper presents an approach based on a new modelling tool: the Interval Constrained Petri Nets. This tool is introduced in order to extend some properties of p-time PN to nontemporal constraints. A robust static weight setting in a tobacco fabric is presented as an application on a real workshop, using real production data. Then, a reactive control, facing the variations in input quality, provides a significant improvement in the quality of the final production.
7 citations
5 citations
Journal Article•
TL;DR: This paper presents an approach based on a new modelling tool: the Interval Constrained Petri Nets, introduced in order to extend some properties of p-time PN to nontemporal constraints.
4 citations
TL;DR: In this paper, an experimental investigation of the influence of machining parameters, viz., pulse current, pulse on time and pulse off time on fractal dimension of surface profile produced in electrical discharge machining (EDM), was conducted for three different workpiece materials to consider the effect of material variation.
Abstract: The present study considers an experimental investigation of the influence of machining parameters, viz., pulse current, pulse on time and pulse off time on fractal dimension of surface profile produced in electrical discharge machining (EDM). Experiments are conducted for three different workpiece materials to consider the effect of material variation in this respect. The second order mathematical models, in terms of the machining parameters, have been developed for fractal dimension prediction using response surface methodology. The fractal dimension models as well as the significance of the machining parameters have been validated with analysis of variance. It is found that the response surface model for fractal dimension is specific to workpiece materials. An attempt has also been made to obtain optimum cutting conditions with respect to fractal dimension with the help of response optimization technique.
3 citations
TL;DR: In this article, the effects of minimum quantity lubrication (MQL) on the cutting performance of AISI 1040 steel, in terms of chip formation mode, tool wear, and machined surface roughness as compared to completely dry and wet machining were investigated.
Abstract: This research work was carried out with the view of investigating the effects of minimum quantity lubrication (MQL) on the cutting performance of AISI 1040 steel, in terms of chip formation mode, tool wear, and machined surface roughness as compared to completely dry and wet machining. In this study, the minimum quantity lubrication was provided with a spray of air and cutting oil. However, compared to the dry and wet machining, M Q L machining performed much better, mainly due to substantial reduction in cutting zone temperature enabling favorable chip formation and chip-tool interaction. It also provides substantial reduction in tool wear, which enhanced the tool life, and surface roughness. Furthermore, it provides environment friendliness and improves the machinability characteristics. K e y w o r d s : MQL; Turning; Tool wear; Surface roughness; Steel; Carbide tools.
3 citations
TL;DR: In this article, an optimization of work roll grinding parameters to obtain desired surface roughness in the work rolls using Neural Network and Taguchi approach has been discussed, where six factors, namely wheel speed, work speed, traverse speed, infeed, dress depth and dressing lead were considered to obtain an optimum grinding condition using Taguchi techniques with L27 orthogonal array.
Abstract: In an industrial manufacturing, grinding processes are used as finishing operation. The work rolls used in finishing operation will undergo considerable wear and changes in surface quality. The objective in roll grinding is to restore the roll to its required surface roughness with minimum stock removal, without visible feed and chatter marks and to remove surface irregularities. To obtain a desired roughness in work rolls consistently, the process has to be optimized and the optimal level of the factors has to be determined. This paper deals with optimization of work roll grinding parameters to obtain desired surface roughness in the work rolls using Neural Network and Taguchi approach. In this study, six factors, namely wheel speed, work speed, traverse speed, infeed, dress depth and dressing lead were considered to obtain an optimum grinding condition using Taguchi techniques with L27 orthogonal array. An attempt was made to minimize the number of experimental runs and increase the reliability of experimental results. Artificial Neural Network (ANN) model was developed to enhance the prediction accuracy. The confirmation test was conducted to verify the results obtained from Taguchi technique and Neural Network model. Analysis of Variance (ANOVA) was subsequently conducted to identify the significant parameters during grinding process.
2 citations
TL;DR: In this paper, the main aim of the research is to reduce lead time, reduce work in process inventory, reduce non-value added activities and to increase production in manufacturing S-Cam Shafts by implementing the Lean Manufacturing System principles.
Abstract: Post liberalization and globalization, there is a need for industry to produce products of world class quality and market them in a highly competitive environment. In today's manufacturing environment, however, the anticipation of high stable demand is not always realistic. Any manufacturing activity requires resource input in terms of men, materials, capital and machines. For maximum effectiveness, this must be done in such a way that customer demands and satisfaction, but at the same time production activities, are carried on in an economic manner. Customer demands are likely to differ in quantities and delivery schedules and this will lead to large fluctuations in the production levels. So to meet any demand, it is desirable to have planning for production in future time periods for inventories of finished goods and meet part of market demands from such finished goods inventories. Further the lead times involved in procurement of manufacturing inputs warrant planning for production in advance. Also requirements of skilled manpower necessitate such planning where time factors are involved. Also the socio-political structure makes it quite difficult for an organization to have varying manpower levels. This again necessitates production planning in order to smooth out the needs for manpower. Long-run changes in demand are taken care of by changes in overall capacity by expansion and / or new facilities. However, in the short run, these will have to be taken care of by such factors as subcontracting, using overtime and building up inventories. Moreover, production operations are subjected to a variety of uncertainties such as non value added steps, material shortage, lead time, quality and various other contingencies. Lean manufacturing is a manufacturing process that productively adds value to materials by capturing proprietary production processes in manufacturing cells supplied by sole-source vendors. Lean manufacturing addresses material, administration and labor costs including the costs of storing and handling materials within the factory. One of the primary lean manufacturing tools that can provide a different dimension in identifying and analyzing non-value added activities is Value Stream Mapping (VSM). The main aim of this research is to reduce lead time, reduce work in process inventory, reduce non-value added activities and to increase production in manufacturing S-Cam Shafts by implementing the Lean Manufacturing System principles.
2 citations
TL;DR: This paper proposes the use of Pertinet based approach aided by particle swarm optimization technique for scheduling activities dynamically for a project, within the available resources and adhering to precedence relationships.
Abstract: The problem of scheduling activities dynamically for a project, within the available resources and adhering to precedence relationships, is NP hard combinatorial. Traditional approaches and their extensions are limited in application for simultaneous scheduling of resources with leveling. Considering the need for improved modeling and scheduling techniques, this paper proposes the use of Pertinet based approach aided by particle swarm optimization technique. Manufacturing projects involving either a single or multi resources can be optimally leveled and scheduled. The effectiveness of this approach is demonstrated with case studies.
1 citations
TL;DR: In this article, the use of frictional vibration for joining of plastic plates by butt welding was studied using the Taguchi methodology and Artificial Neural Network was used to establish the relationship between input and output parameters.
Abstract: The use of frictional vibration for joining of plastic plates by butt welding was studied using the Taguchi methodology. The effectiveness of the Taguchi method lies in clarifying the factor that dominates the complex interactions in frictional vibration welding. The factors are (i) Frequency of tool vibrating across the work piece, (ii) Feed of the work piece against the tool and (iii) Clamping force at the joint interface. This study describes an innovative method of selection of process parameters for obtaining optimal weld tensile strength. Artificial Neural Network was used to establish the relationship between input and output parameters. Genetic Algorithm (GA), Particle Swarm Optimization(PSO) and Taguchi method were used to optimize the parameters for the process.
TL;DR: In this paper, the effects of process control parameters (voltage (OCV), wire feed rate, traverse speed, electrode stick out and slag-mix%) on features of bead geometry, as well as HAZ through mathematical models representing quadratic relationships between process control variables and characteristics of the weld quality obtained by non-conventional submerged arc welding.
Abstract: Slag formed during SAW process has been collected and processed for further use in subsequent runs of Submerged Arc Welding of mild steel material. The mixture of fused flux (slag) and fresh flux has been used as an alternative to fresh flux. The percentage (by volume) of slag in the mixture of fused flux and fresh flux has been termed as slag-mix%. Welding has been done using various percentages of slag-mix; the percentage has been treated as a process variable of extreme importance. An attempt has been made to assess the effects of process control parameters (voltage (OCV), wire feed rate, traverse speed, electrode stick out and slag-mix%) on features of bead geometry, as well as HAZ through mathematical models representing quadratic relationships between process control variables and characteristics of the weld quality obtained by non-conventional Submerged Arc Welding. The level of significance of process parameters on various responses associated with submerged arc weldment has been checked statistically and calculated quantitatively. Further, the direct effect of process parameters and their interaction effects (if they exist) on selected output features of the weldment have been shown graphically. The present investigation leads to the idea that reconsumption of slag in SAW process does not impose any alarming adverse effect on parameters of the desired weld quality. Keyw. rds: SAW, slag, HAZ, quadratic relationship
TL;DR: Tittagudi et al. as mentioned in this paper applied the Response Surface Methodology (RSM) to the Electrochemical Machining (ECM) of AISI 1035 medium carbon steel specimens, and three independent parameters: voltages, feed rate and Inter Electrode Gap (IEG) were chosen as the process variables, whilst the metal removal rate and surface roughness of the finished specimens were the response variables.
Abstract: Response Surface Methodology (RSM) was applied to the Electrochemical Machining (ECM) of AISI 1035 medium carbon steel specimens. Three independent parameters: voltages, feed rate and Inter Electrode Gap (IEG) were chosen as the process variables, whilst the Metal Removal Rate (MRR) and surface roughness of the finished specimens were the response variables. A first-order experiment was performed to determine the magnitudes of the relative changes to the process variables that would result in a better MRR and surface roughness. A second-order central composite design was used subsequently to locate the optimum conditions for achieving the best MRR and surface roughness. A maximum value of MRR of 266 mm /min and a minimum value of surface roughness Ra of 1.5 pm were predicted at an ECM applied voltage of 12 V, tool feed rate of 0.24 mm/min and IEG of 0.05 mm. Confirmatory tests showed that the actual performance at the optimum conditions were 251 mm/min and 1.59 μηι, a deviation from the predicted performance of only 6 %. 1 Corresponding Author Tel: +91-427-2346157 Fax: +91-427-2346458 sekar_tittagudi@yahoo.com K e y w o r d s : ECM, Optimization, Surface Response Methodology, AISI 1035, MRR, surface roughness
TL;DR: Applied aggregate production plan (APP) is generated using simulated annealing (SA) for a multi-factory model, where the same family of products is manufactured at different localities, for the objective of cost minimization.
Abstract: Aggregate production plan (APP) aims to attain the highest profit level by utilizing all the resources efficiently. Most of the industries today are generating the APP separately for each plant even though they produce the same family of products in all the plants. APP devised for the plants after allocating the demand for the corresponding plants based on gross production and distribution costs is not optimum. The industry must work towards meeting the forecasted demand by creating a medium-term plan considering all its plants simultaneously. In this paper, APP is generated using simulated annealing (SA) for a multi-factory model, where the same family of products is manufactured at different localities. Data collected from a bearing manufacturing company is fitted to the mathematical model developed in this case. The parameters of the APP, viz., period-wise workforce level, quantity to be produced in regular production, overtime hours, outsourcing, finished goods inventory status and number of products sent from each plant to the demand centers are all normalized towards the objective of cost minimization. * Corresponding Author
TL;DR: In this paper, the experimental investigations of surface roughness and material removal rate in electrical discharge machining (EDM) of austenic stainless steel are reported. But the machining factors used are current intensity (I), pulse on-time (ti), pulse off-time(to) and gap voltage (v).
Abstract: This paper reports on the experimental investigations of surface roughness and material removal rate in electrical discharge machining (EDM) of austenic stainless steel. The importance of this study is that it establishes imperical relations regarding machining parameters and the responses in analyzing the machinability of the austenitic stainless steel. The machining factors used are current intensity (I), pulse on-time (ti), pulse off-time (to) and gap voltage (v), over the important factors of surface roughness (Ra) and material removal rate (MRR). The investigation and the analysis of the influences of machining parameters on the response variables were performed by using the technique of design of experiments (DOE). The developed models show that current intensity and pulse on-time are the most significant machining parameters influencing Ra and MRR while increase in off-time results in decrease MRR response. These models can be used to get the desired responses within the experimental range.
TL;DR: In this article, an optimization of flux cored arc welding process parameters, which are used for deposition of duplex stainless steel on low carbon structural steel plates, has been presented, where experiments were conducted based on central composite rotatable design and mathematical models were developed using multiple regression method.
Abstract: In weld cladding applications, most of the engineers are often facing the problem of optimum selection of input process parameters to achieve the required dilution. This problem can be solved by optimizing the process parameters. This paper focuses on an optimization of flux cored arc welding process parameters, which are used for deposition of duplex stainless steel on low carbon structural steel plates. Experiments were conducted based on central composite rotatable design and mathematical models were developed using multiple regression method. Further, an optimization of percentage dilution was carried out using particle swarm optimization technique. A computer program was developed using C language for computation of algorithm. It is highly reliable, adoptable, very user friendly and it can be extended to other welding processes such as GMAW, GTAW, Robotic Welding,etc.
TL;DR: In this paper, a Computer Aided Process Planning (CAPP) system for the prismatic parts having different features, these are recognized by Manufacturing View Technology is presented, the parts which have been modeled using any CAD software is imported into manufacturing view software, where an Automatic Feature Recognition (AFR) module is used to recognize the features automatically.
Abstract: This paper presents the development of a Computer Aided Process Planning (CAPP) system for the prismatic parts having different features, these are recognized by Manufacturing View Technology. The parts which have been modeled using any CAD software is imported into manufacturing view software, where an Automatic Feature Recognition (AFR) module is used to recognize the features automatically. This paper also presents GA based CAPP, which deals with the process planning problem in a concurrent manner in generating entire solution space by considering multiple decision making activities, i.e. operation selection, machine selection, setup selection, tool selection and operation sequence simultaneously. Precedence relations among all the operations required for a given part are used as constraints for the solution space. By applying GA, the CAPP system can generate optimal or near optimal process plan based criteria chosen. Minimum processing time is the plan evaluation criteria chosen for optimization.
TL;DR: This paper tries to identify the steps involved in the Reverse Engineering process and 3D data acquisition technique, which will be useful for generating CAD models or to have a CAD database.
Abstract: The objective of Reverse Engineering is to develop a new product based on the shape recovery of the existing one. Digitizing is the process of duplicating an object functionally and dimensionally by measuring the existing parts to develop the technical data required for competitive procurement or for design and manufacturing. In the current industrial scenario 3D shape recovery is done by measuring the component physically with the gauges or by building up of physical models or copying with the help of CNC machines. This paper tries to identify the steps involved in the Reverse Engineering process and 3D data acquisition technique, which will be useful for generating CAD models or to have a CAD database. The 3D digitizing technique is proved to be an important tool for the 3D-shape recovery with good cost effectiveness and accuracy. Efforts are made to identify a cost-effective approach to recover 3D shape of objects by using a Coordinate Measuring Machine (CMM). The Intake and Exhaust Port Cores of a Cylinder Head have been digitized by using a CMM. Scanned raw data is saved in any one of standard point file format and it is transferred to CAD software to generate cloud points. With the help of these cloud points NURBS curves are generated by using any commercial CAD software like Pro\\Engineer. Smooth parametric surface are obtained by the fitting of Bsplines curves into surfaces from boundary options. Thus complete 3D models of the port cores are modeled by using various surface options. The accuracy of the CAD model is generated by these limits. Hence this data can be saved as a standard data format for further CAD/CAM applications.
TL;DR: In this work, the problem of scheduling of FMS, considering jobs integrated with Automated Guided Vehicles (AGVs) and Automated Storage/Retrieval System (AS/RS) with the multiple objective function of minimizing penalty cost, minimizing machine idle time and minimizing the distance traveled by the storage machine is considered.
Abstract: Flexible Manufacturing System (FMS) is a highly automated system in the field of manufacturing to achieve high flexibility and productivity in mid-variety and mid-range of products. The success of any FMS therefore lies in the design of an appropriate scheduling procedure to optimize the required performance measures of the manufacturing systems. In this work, the problem of scheduling of FMS, considering jobs integrated with Automated Guided Vehicles (AGVs) and Automated Storage/Retrieval System (AS/RS) (16 machines and 43 parts) with the multiple objective function of minimizing penalty cost, minimizing machine idle time and minimizing the distance traveled by the Storage/Retrieval(S/R) machine is considered. The non-traditional techniques are implemented to solve the above FMS scheduling problem. The results of Genetic Algorithm (GA), Adaptive Genetic Algorithm (AGA), Artificial Immune System (AIS), Sheep Flocks Heredity Algorithm (SFHA) and Particle Swarm Optimization (PSO) are compared. The results show that PSO outperforms all other algorithms.