Manufacturing Technology Today

Susanta Kumar Gauri ¹, Shankar Chakraborty ²

Affiliations
1 SQC & OR Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata, IN
2 Dept. of Prod. Engg., Jadavpur University, Kolkata, IN

Abstract

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.

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Susanta Kumar Gauri ¹, Shankar Chakraborty ²

Affiliations
1 SQC and OR Unit, Indian Statistical Institute, Kolkata, IN
2 Dept. of Production Engg., Jadavpur University, Kolkata, IN

Abstract

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.

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Susanta Kumar Gauri ¹, Shankar Chakraborty ²

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

Abstract

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.

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Vijay Manikrao Athawale ¹, Prasenjit Chatterjee ², Shankar Chakraborty ²

Affiliations
1 Dept. of Mechanical Engg., Govt. Polytechnic, Amravati, Maharashtra, IN
2 Dept. of Prod. Engg., Jadavpur University, Kolkata, IN

Abstract

From the engineering perspective, material selection is a process aimed at identification of the suitable material which after manufacturing will have the desired dimensions, shape and properties necessary for the product to demonstrate its required function at the lowest cost. While selecting the best material for a product, the design engineer has to critically analyze the functional requirements of that product. To cut down the cost of the finished product, many new materials are now available in the market, but the design engineer has to check the properties and feasibilities of those materials in order to fulfill the desired objective. The large number of materials with their diverse properties makes the job of the design engineer more complex than before. The design engineer has to take into account a large number of material selection criteria before arriving at the final decision, otherwise, there may be premature failure of the product during its operation. In this paper, considering the material selection as a multi-criteria decision-making (MCDM) problem, the best material for a flywheel is selected using five most widely used and computationally easy MCDM methods. The rankings of the alternative materials as obtained using these MCDM methods almost match with that derived by the past researchers.

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Shankar Chakraborty ¹

Affiliations
1 Department of Production Engg., Jadavpur University, Kolkata, IN

Abstract

Machine and machining process selection play an important role in the modern day manufacturing environment. Selection of the most suitable machining process for generating a desired feature on a given material requires the consideration of several factors among which the type of the workpiece material and shape to be machined are the most significant ones. Selection of a machining process is, therefore, observed to be a multi-criteria decision-making problem with different conflicting objectives. Various methods have now been available for automatic selection of machines and machining processes to generate a desired shape feature on a given material. This paper presents a brief review of those methods developed by the previous researchers while selecting various machines and machining processes under specific manufacturing environment. This paper also helps to offer new possibilities for automation of machining process selection and many other areas within manufacturing.

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Shankar Chakraborty ¹, Basab Samanta ¹

Affiliations
1 Dept. of Production Engg., Jadavpur University, Kolkata, IN

Abstract

In this paper, an effort is made to give a brief overview on Simulated Annealing (SA) technique based on the review of past research works. The review is primarily done to search out the applications of this heuristic technique in various optimization related problems of Production Engineering. For this, more than hundred research papers published in different reputed international journals during the year 2000-2005 are selected to critically analyze the role of Simulated Annealing as an improved optimization tool in present day manufacturing environment. Moreover, the published papers are categorized according to various application areas of Production Engineering, to highlight the specific area where this non-conventional optimization technique has been widely used to provide optimal solutions. Lastly, it is shown that this hybrid optimization technique is rapidly gaining its popularity with respect to time as well as geographical boundaries.

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Shankar Chakraborty , Debabrata Banik ¹

Affiliations
1 Dept. of Production Engg., Jadavpur University, Kolkata-700032, IN

Abstract

This paper attempts to describe the key features o f inventory control system and report a study that utilizes the analytic hierarchy process (AHP) technique to evaluate the best policy o f inventory control. The study reviews the most important criteria and sub-criteria affecting the selection of the optimal inventory control policy and determines their respective priority values imposing the AHP methodology. Five most popularly used inventory control policies are considered based on knowledge and experience and then subsequently ranked using the AHP under a multi-criteria environment. These considerations together with pair-wise comparison matrices and evaluating methodology lead to prioritize the conflicting criteria for selecting the optimal policy. Sensitivity analysis is also performed to search out the most critical and robust criteria in the inventory control policy selection problem.

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Shankar Chakraborty ¹, Debabrata Banik ¹

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata-700 032, IN

Abstract

In any manufacturing organization, the traditional ABC analysis for selective inventory control represents the relationship between the volume and annual consumption value of the inventory items. The procedure of classifying inventory items into ‘A’, ‘B’ and ‘C’ categories needs to be constantly and carefully attentive to how well it will interpret the relevant data with multiple criteria and conflicting objectives. Perhaps the most creative and difficult task in making a decision is to choose the dominant criteria along with the other related sub-criteria and search out the fact that how those are important deciding factors in selecting the ‘A’, ‘B ’ and ‘C’ class of items. This paper highlights a distinct methodology to analyze the ‘A' ‘B' and ‘C ’ items using the pair-wise comparison matrices of the analytic hierarchy process (AHP) technique, which ensures the consistency of the decision maker’s judgments regarding the importance of one criteria over another to find out the weightage value for each of the considered criteria and sub-criteria. The developed approach is illustrated using real time data and validated with the theoretical results.

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Shankar Chakraborty ¹, Srijib Kumar Dhara ¹

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata, IN

Abstract

This article presents a literature review on the applications of genetic algorithm (GA) in the field of production engineering. Genetic algorithm is a multi-criteria decision-making tool that is used in almost all areas of production engineering. Out of many different applications of GA, this article covers a selective few that will be of wide Interest to the researchers and practitioners. This article critically analyzes some of the papers published in the international journals of high repute and gives a brief idea about many of the referred publications. Published papers are categorized on the basis of the areas of applications related to production engineering. The references are also grouped region-wise and year-wise in order to track the growth of GA applications. A total of 150 application papers are referred in this article. It Is hoped that this work will provide a ready reference on genetic algorithm and act as an informative summary kit for the researchers and practitioners for their future work.

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Susanta Kumar Gauri ¹, Shankar Chakraborty ²

Affiliations
1 SQC & OR Unit, Indian Statistical Institute, 203, Kolkata-700 108, IN
2 Department of Production Engineering, Jadavpur University, Kolkata-700 032, IN

Abstract

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.

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Shankar Chakraborty ¹, Sammilan Dey ¹

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata - 700 032, West Bengal, IN

Abstract

The intricacy involved in selecting an optimal non-traditional machining (NTM) process for machining a desired feature on a given material demands automating the decision making process. This paper employs a Quality Function Deployment (QFD) based expert system to quantify various factors involved in the complex decision making problem of selecting the optimal NTM process. The expert system not only automates the decision making process but also uses Its Inbuilt graphical user interfaces and visual aids to enable proper interpretation of the results. The approach includes the use of a simplified House of Quality (HOQ) matrix to compare the desired product characteristics with the technical characteristics of the NTM processes to develop individual scores for the technical characteristics of each of the NTM processes for generating a specific shape feature on a given material. The Individual scores are added to obtain the overall suitability scores for the NTM processes. Ultimately, among the NTM processes which satisfy some critical criteria, the process with the highest overall score is accepted as the optimal choice for a particular material and shape feature combination.

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Susanta Kumar Gauri ¹, Shankar Chakraborty ²

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

Abstract

Accurate monitoring and control of the manufacturing processes has become very important In today's dynamic world due to rapid Increase In demand for highly precise end products. For every process, there is a target and the goal of statistical process control (SPC) is to ensure that the process mean lies nearer to the target value without inflation o f process variability. Control charts are widely used to identify the situations when control actions will be needed for the manufacturing processes. Control charts usually exhibit one of the eight basic types of patterns. Identification of these patterns leads to more focused diagnosis and significantly minimizes the effort towards troubleshooting. Pham and Wani presented a feature-based heuristic approach for control chart pattern (CCP) recognition, which has been very appealing to the shop-floor people, because In this approach, the practitioners can clearly understand how a particular pattern has been identified by the use o f relevant features. The heuristics proposed by Pham and Wani can only differentiate six types of CCPs based on extraction of nine features. In this paper, a new set of nine features is proposed and the heuristics for CCP recognition based on these features is also presented that can efficiently differentiate all the eight basic types of CCPs. The features are chosen such that the need for human intervention for their extraction is eliminated and thus the CCP recognition is totally automatic. The proposed feature-based CCP recognition approach can be applicable to any process operating on the known target value and will be considerably robust against the variation of true process standard deviation from its estimated value. Thus it has enough potential for use in real-time process control applications.

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Shankar Chakraborty ¹, Sammilan Dey ¹

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata-700032, IN

Abstract

Selection of a non-traditional machining process is always deemed to be a challenging problem due to the involvement of various conflicting criteria in the decision-making process. This paper strives to use the multi-criteria decision-making approach towards the selection of an optimal non-traditional machining process under constrained material and machining conditions through the design of an expert system. The expert system relies on the analytic hierarchy process to evaluate the priority index values for different non-traditional machining processes, based on several criteria and sub-criteria involved in the selection process. The expert system also takes the help of an inbuilt logical database to demarcate the non-traditional processes that are actually acceptable for a specific condition. Acceptability Index values are determined for the processes lying in the acceptability zone and the process with the highest acceptability index value is selected as the optimal choice.

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Shankar Chakraborty ¹, Diganta Tah ²

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata - 700 032, IN
2 Cognizant Technology Solutions, Salt Lake, Kolkata, IN

Abstract

In present-day manufacturing environment, there is an ardent need to have effective and smooth flow of information through all the departments of an organization. In this paper, a maiden venture is taken to design and develop a Supply Chain Management (SCM) module for streamlining all the procurement and inventory related activities of a manufacturing organization. With the help of the developed module, the order to delivery cycle time for the products can be reduced considerably while improving the customer responsiveness that is found to be the crucial success factor in today's competitive market. The developed module is successfully implemented in a simulated manufacturing environment to test its utility.

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Shankar Chakraborty ¹, Diganta Tah ²

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata-700032, IN
2 Cognizant Technology Solution, Salt Lake, Kolkata-700091, IN

Abstract

This paper deals with the development of a hybrid prototype system, which would act as an integrated real time decision support system for resource planning and scheduling. The hybrid system would be the application of continuous improvement, with elements of Total Quality Management and Close Loop Enterprise Resource Planning (ERP) modules. A combination of the above two capabilities would help in deploying a hybrid approach of managing the manufacturing activities through 'Advanced Planning and Scheduling' (APS) mechanism. The optimised quantities of products to be manufactured have to be decided after passing through a decision support model. This model has been primarily based on linear programming (LP) method, which would consider the finite capacity of resources.

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Shankar Chakraborty ¹, Diganta Tah ²

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata - 700 032, IN
2 Cognizant Technology Solutions, Salt Lake, Kolkata, IN

Abstract

A real time maintenance management system is observed to be the way to meet the dynamic workloads in present day manufacturing environment. The paper focuses on the development of such a system for integrated functioning of a manufacturing organization. The developed computerized system will essentially replicate the processes of a traditional ERP system, collect the real time data, explode the data against the timeline, help in balancing the use of limited resources against the timeline and finally give output in the form of preventive maintenance and corrective maintenance work orders The system is tested in simulated manufacturing environment and the flexibility of the developed system is also proved.

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Shankar Chakraborty ¹, Diganta Tah ²

Affiliations
1 Department of Production Engineering, Jadavpur University, Kolkata-700032, IN
2 Cognizant Technology Solutions, Salt Lake, Kolkata-700091, IN

Abstract

Continuous increase in the competition and customer expectations has directed the manufacturing operations in an organization to focus on quality related matters and statistical techniques so as to interpret the data as captured at various stages of manufacturing. The developed advisory decision support system uses generic methods to address various problems of quality control and would analyze the enormous volume of data as generated and aid in making quality related decision. In conventional statistical process control applications, the analyzed results have to be interpreted by very experienced operators or quality control engineers. In automated unmanned manufacturing environments, based on Manufacturing Execution Systems (MES), this has been a major drawback. This inherent bottleneck has been overcome by incorporating interpretation expertise in the system. The developed system would help in configuring various rule sets, on violation of which various exceptions would be generated by the system and the expert system would advice for appropriated remedial actions, hence, would act as an advisory support system. The concerned quality control engineers could interactively change these rule definitions. Subsequently, the possible causes of the violations of various rules would be configured as Violation Causes. The set of causes of the rule violation would be associated with a certain type of variable.

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