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Gauri, Susanta Kumar
- Multi-Response Optimization of WEDM Process Using the VIKOR Method
Abstract Views :190 |
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Authors
Affiliations
1 SQC & OR Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata, IN
2 Dept. of Prod. Engg., Jadavpur University, Kolkata, IN
1 SQC & OR Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata, IN
2 Dept. of Prod. Engg., Jadavpur University, Kolkata, IN
Source
Manufacturing Technology Today, Vol 7, No 9 (2008), Pagination: 11-16Abstract
Researchers have attempted several approaches for determination of the process settings that can optimize the multiple performance measures (responses) of wire electrical discharge machining (WEDM) operations. The VIKOR method, applied so far for multi-response optimization of chemical processes, can overcome the limitations of the multi-response signal-to-noise (MRSN) ratio based approach. In this paper, the VIKOR method is modified to make it more generalized and a set of experimental data on multiple responses of WEDM process is analyzed using the modified VIKOR method. The results demonstrate that the optimal factor-level combination determined using the VIKOR method can lead to significantly better overall quality level than the MRSN ratio based approach.- Selection of Optimal Control Parameters for Non-Traditional Machining Processes Using the Taguchi Method-A Literature Review
Abstract Views :160 |
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Authors
Affiliations
1 SQC and OR Unit, Indian Statistical Institute, Kolkata, IN
2 Dept. of Production Engg., Jadavpur University, Kolkata, IN
1 SQC and OR Unit, Indian Statistical Institute, Kolkata, IN
2 Dept. of Production Engg., Jadavpur University, Kolkata, IN
Source
Manufacturing Technology Today, Vol 7, No 7 (2008), Pagination: 33-41Abstract
With the introduction and increased use of newer and harder materials like titanium, stainless steel, high strength temperature resistant (HSTR) alloys, fiber-reinforced composites and ceramics in aerospace, nuclear, missile, turbine, automobile, tool and die making industries, a different class of machining processes has been emerged. Instead of employing the conventional tools, these non-traditional machining (NTM) processes use energy in its direct form to remove materials from the workpiece. To achieve the best performance of these NTM processes, it is necessary to select the machining parameters at their optimal levels. Taguchi method of robust design has been extensively used to choose the optimal parametric levels in various machining processes. This paper exclusively reviews the applications of the Taguchi method adopted to select the optimal factor level combinations in different NTM processes.- Recognition of Control Chart Patterns Using Feature-Based Artificial Neural Network Approach
Abstract Views :166 |
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Authors
Affiliations
1 SQC & OR Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata-700108, IN
2 Dept. of Production Engineering, Jadavpur University, Kolkata-700032, IN
1 SQC & OR Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata-700108, IN
2 Dept. of Production Engineering, Jadavpur University, Kolkata-700032, IN
Source
Manufacturing Technology Today, Vol 6, No 5 (2007), Pagination: 11-16Abstract
Control charts usually exhibit one of the eight types of patterns. These patterns can be classified as normal and abnormal. Recognition of abnormal patterns in control charts can provide clues to reveal potential quality problems in the manufacturing processes. Neural network approaches (with features extracted from the pattern data as input vector representation) have been successfully applied by the researchers in recent years for recognition of control chart patterns. Usage of features leads to smaller network size and results in faster training and generally more effective and efficient recognition of control chan patterns. The reported feature-based approaches can only recognize six principal control chart patterns (CCPs). In this paper a new set of features is proposed and a multilayered perceptron (MLP) neural network trained by back-propagation algorithm is presented that can recognize stratification and systematic patterns in addition to the other six patterns as mentioned above. Extensive performance evaluation of the developed pattern recognizer is carried out using simulated data. Numerical results indicate that the artificial neurai network based pattern recognizer developed using the proposed set of features can perform well in real time process control applications.- Determination of Optimal Product-Mix for a Foundry Using Goal Programming
Abstract Views :158 |
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Authors
Affiliations
1 SQC & OR Unit, Indian Statistical Institute, 203, Kolkata-700 108, IN
2 Department of Production Engineering, Jadavpur University, Kolkata-700 032, IN
1 SQC & OR Unit, Indian Statistical Institute, 203, Kolkata-700 108, IN
2 Department of Production Engineering, Jadavpur University, Kolkata-700 032, IN
Source
Manufacturing Technology Today, Vol 5, No 9 (2006), Pagination: 5-9Abstract
The time consumed from loading of ingredients into a furnace to the completion of pouring of melt in the molds is known as the heat length and a batch of production is called as heat. In a single heat, different types of castings are produced when the required raw material composition for all these castings are similar. If sufficient numbers of molds for different types of castings that can consume the entire melt quantity are not ready after packing, heat length increases resulting in lesser number of heats per day. However, deployment of too many resources for packing incurs unnecessary cost. In this context, determination of the optimal quantities of different types of castings to be produced in a heat (known as product-mix) and allocation of their molds for packing at different locations is very important. The optimal product-mix will ensure that the leftover quantity of melt in the furnace will be minimum and shortages in the delivered quantities as well as overproduction of different types of castings can be avoided. In this paper, a real time problem of optimal product-mix determination for an Indian foundry is discussed and Its solution Is presented. The expected annual tangible gain from the increased productivity achieved through the deployment of the optimal product-mix of molds for packing Is estimated to be around rupees 11.14 lakhs/annum.- Feature-Based Control Chart Pattern Recognition
Abstract Views :157 |
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Authors
Affiliations
1 SQC & OR Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata-700108, IN
2 Dept. of Production Engineering, Jadavpur University, Kolkata-700032, IN
1 SQC & OR Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata-700108, IN
2 Dept. of Production Engineering, Jadavpur University, Kolkata-700032, IN