Layout Optimization by Solving Cell Formation ...

Layout Optimization by Solving Cell Formation Problem in Piston Ring Manufacturing Process Industry K.Arun Prasath

S.Anbarasan

Assistant Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering, Karur Tamilnadu, India [email protected]

U.G Scholar Department of Mechanical Engineering M.Kumarasamy College of Engineering, Karur Tamilnadu, India [email protected]

A.Sivamani U.G Scholar Department of Mechanical Engineering M.Kumarasamy College of Engineering, Karur Tamilnadu, India [email protected]

S.Suriyapradheep U.G Scholar Department of Mechanical Engineering M.Kumarasamy College of Engineering, Karur Tamilnadu, India [email protected]

R.Vigneshwaran U.G Scholar Department of Mechanical Engineering M.Kumarasamy College of Engineering, Karur Tamilnadu, India [email protected]

Abstract—Cellular Manufacturing is a popular methodology used in mid-volume and variety of mixed production systems to achieve the benefits of mass production in a batch environment. The main emphasis is on creating machine cells and product/component families such that each cell is independent of the other resulting in ownership and responsibility for the cell to make the products. The benefits are improved quality; less lead-time for production reduced set-up time reduced material travel times, reduced expediting and improved human relations. Software provides an opportunity to appropriately apply theoretical algorithms to meet practical requirements in cell formation problems. The implementation of Group technology (GT) to the manufacturing system is a manufacturing philosophy, which determines and divides the parts into various families and the machines into cells by taking advantage of part similarity. The fundamental problem in CM is to identify and create machine cells and associate part families. To solve this problem usually known as the cell-formation problem, different approaches have been developed to identify part families and their associated machine cells. This paper addresses various issues in cell formation in process industry to be considered and presents experiences on the development, testing and implementation of the software by solving the cell formation problems during the optimization of layout in the selected industry using ZODIAC algorithm and ARENA simulation software.

Keywords— Cellular Manufacturing, Mass production, Group technology (GT), Part families, Cell-formation problem.

I.INTRODUCTION This paper deals with the development of an algorithm for concurrent formation of part-families and machine-cells in group technology. The acronym ZODIAC stands for (zero-one data: ideal seed algorithm for clustering). The present algorithm is an expanded and improved version of the earlier ideal seed method[1].The formation of part-families and machine-cells has been treated as a problem of block diagonalization of the zero-one matrix. Different methods of choosing seeds have been developed and tested. A new concept called ‗relative efficiency‘ has been developed and used as a stopping rule for the iterations. The ZODIAC procedure and its theory are given in detail. Test results with a 40 × 100 matrix are shown the distinguished variables. ZODIAC ALGORITHMS developed by Chandrasekaran and Rajagopalan is a seed clustering cell formation technique. In ZODIAC algorithms parts and machine types are treated independently in the initial phase. Rows of the machine parts incidence matrix represent machine type in binary vector format. Similarly, binary vector for a specific part can be obtained from the corresponding column. Parts and machine types are clustered separately by means of seeds where seed represents a binary vector. Parts and machine clusters are then assigned to each other by the use of similarity coefficient. Consequently each assignment produces a cell.

Authors argued that non-hierarchical methods should be preferred over hierarchical methods, because in case of hierarchical method of clustering, when two points (row vectors or column vectors) are grouped together at some stage of the algorithm there is no way retrace the step even if it leads to suboptimal clustering at the end [2].

II. CONFIGURATION OF CELL FORMATION DILEMMA USING ZODIAC ALGORITHM The ZODIAC Algorithm chooses an arbitrary representative seeds for each group which may fail to represent the corresponding cluster. The densest binary vector in each cluster is offered as the first representative seed. The remaining representative seeds can be determined in such a way that they will be distant from the distant seeds. First the candidates with the maximum distance from all the seeds become representative seeds. The maximum distance is controlled by machine difference factor and this factor is decreased by a threshold percentage for the next representative seeds. A.Non-Hierarchical Clustering of columns and rows. (Classifies the machines into cells and the components into families) STEP1.Compute limiting number R for clusters using the equations. R≤1+[(α+β-1)-√({(α+β-1)^2-4(αβ-ϕ)})/2

Where, α=Number of rows (machines). β=Number of column (components). φ=Number of operation (1‘s). STEP 2.Choose R seeds for columns. STEP 3.Cluster column. STEP 4.Choose representative seeds. STEP 5.Cluster columns. STEP 6.Find number of non-null clusters Rc. STEP 7.Find numbers of null clusters Modify Rc. STEP 8.Modify R as Rc. STEP 9.repeat steps 2 and 5 for rows. STEP 10.Find non-clusters of RR. STEP 11.Modify R-Min(Rc,RT). STEP 12.If (Rc#RT) go to step 2. STEP 13.Re-order rows and columns in the order of cluster membership. III. LITERATURE SURVEY ON CELL FORMATION PROBLEMS FOR LAST DECAYS Cellular manufacturing is the physical division of the large job shop into numerous small production cells. Each cell is designed to efficiently produce common types or shapes of parts having similar machine, operation, and fixture requirements[3]. The objective of this work is to find the best layout designs with an intention of minimize the

material movement and cost to improve the over efficiency of a diligence and such extension of their work is they have planned to implement this optimized layout design in any of the industry to achieve some good results in their production and in this paper they have proposed to make a deliberate case study in a process industry to study the existing layout design[4] and layout design is executed using through ARENA simulation software with different form of machine layout. This research work inquires the applications of designed experiments aided by multiple regression analysis for tuning of this emerging novel optimization algorithm parameters to the cell formation (CF) problem considering operation sequence and[5] in this paper, an attempt is made to tuning chemo tactic and swarming steps parameters meanwhile taking into consideration bacteria foraging optimization algorithm convergence speed and performance so, the factorial designed experiment is suggested to create treatments of experiment and the adequacy of the proposed model is analyzed based on some commonly statistical criteria[6]. Machines are classified based on the operations and order of process. The machine aisles are arranged using cellular manufacturing concept of process layout and the material handling system is studied and evaluated [7]. The layout of straining section is optimized by Using Kaizen concept a methodology is suggested to effectively utilize the tube storage area so in production work stations, the equipments are arranged in a sequence that supports a smooth flow of materials with minimal transport or delay [8]. Cellular manufacturing system is a submission of group technology wherein are different machines or processes have been combined into cells, each of which is devoted to the fabrication of a part, product family, or limited group of families. Cell formation is necessary for implementation of cellular manufacturing. Many of methods exist for cell formation problem solving [9]. Some of these methods are applying in traditional cell design, in fixed routines and others are applying in dynamic cells environment. In this review article, critical assessment of various metaheuristic techniques which utilized in cell formation problem solving is made through extensive literature review [10]. Various existing models for cell formation are argued consequently and directions for future work are presented. Cellular manufacturing system has been proved a vital approach for batch and job shop production systems [11]. Group technology has been an essential tool for developing a cellular manufacturing system. The paper aims to discuss various cell formation techniques and highlights the significant research work done in past over the years and attempts to points out the gap in research [12]. To find good solutions to the stochastic case whenever good solutions can be found for the corresponding deterministic problem with incapacitated machines of the same dimension. Hence, this problem represents a case where a specific deterministic model, even when solved heuristically, produces a very good solution to a stochastic model[13]. Layout design and the flow of materials have a significant impact on performance of manufacturing system and these can help to increase productivity, reduce work in process and inventory, short production lead time,

streamlines the flow of materials, cost and reduce non value added activities from the production process of waiting and transportation, which make the factory meet customers requirement quickly and the conclusions of the research highlight the key lessons for successful design and implementation of cellular manufacturing in a sewing floor[14]. Lean manufacturing has been widely adopted by many production companies. Apart from the operational difficulty associated with conversion from a traditional, functional based operation, adoption of Lean manufacturing involves significant organizational transformations. It requires formation of work teams, comprised of multi-skilled workers [15]. This paper reports the results of investigations on manufacturing cycle times for special-purpose products. The company performs serial production characterized by complex and diverse technologies, alternative solutions and combined modes of work piece movement in the manufacturing process. Because of various approaches to this problem, an analysis of previous investigations has been carried out, and a theoretical base is provided for the technological cycle and factors affecting the manufacturing cycle time. The technological and production documentation of the company has been analyzed to establish the technological and real manufacturing cycle times, New environmental characteristics and requirements have challenged manufacturing enterprises to improve the efficiency and productivity of their production activities and cellular manufacturing as one of the solutions to this problem[15], as implementation of Group technology was proposed and Performance of cellular manufacturing systems depends heavily on the cell structure so in this paper they present and evaluate a novel approach for solving the cellformation problem based on implementation of a GA, called modified genetic algorithm in all of the industry applications. IV.PROBLEM FORMULATION AND SOLVING METHODOLOGY The selected industry was located in Industrial Estate Mariamalai Nagar, Kancheepuram District and they are Wholesale Suppliers, Producers and Exporters of Piston ring manufacturing industry and some small automobile components like oil rings, differentials, pinions and other transmission components with metal components, metal products, for various Automobile industries around tamilnadu and other states in India. we observed here is the arrangement of machines and its layout design its is shown as a model and some of the data‘s given by the industry from that we executed using ARENA software the results and the software model is shown below the problem here Fig:1 is the machines are arranged in their own order with different specifications of ring components in that SPEC-1 indicates the Large bore 2stroke diesel engines fitted with PVA cylinder liners as its application, similarly some SPEC‘s are used relevant to their applications this we only execute the solution for SPEC-1 and we show the optimum layout design with minimization of material travelling cost and increase in profit also discussed below with correct form of layout arrangements.

Fig 1. Default working vicinity of Piston ring Manufacturing Industry TABLE 1. Software Layout Design for their Working Area

Total Raw Material cost = 0.40

Scrap cost = 0.10

Forging operator = 0.10

Over head Cost= 0.07

Tensioning of material cost = 0.05

Inspection = 0.05

Material travelling cost = 0.25 Profit per piece= 0.12 Prime cost= 0.20 Administrative OD Cost = 0.11 Tool cost= 0.12 Final Cost for making single piece of Piston Ring is = Rs 1.20

Eg: 1)SPEC 1– [Ring thick-0.050 - 0.075mm, Application Large bore 2-stroke diesel engines fitted with PVA cylinder liners, with the chemical combination of carbon3.30%,silicon1.30%,sulphur0.08%,manganese0.090%,chromi um-0.05%,copper-0.10],is used as raw material for orbital forging with succession of operations as Tensioning, coating, quality checking, Packaging, Dispatched similarly this layout for all type we follow same procedure in our consideration we used only Nickel-graphite coating for coated our ring the key application of this type of coating was to help them bed-in and it proved useful when used on plasma sprayed coatings for collectively to produce Piston rings in all type of automobile component manufacturing industries.

Fig 2. Working vicinity of Linear Shape ARENA Model

TABLE 2. Software Linear Layout Design and Cost Calculation TABLE: 3 Software L-Shape Layout Design and Cost Calculation Total Raw Material cost = 0.40

Scrap cost = 0.10

Forging operator = 0.10

Over head Cost= 0.07

Tensioning of material cost = 0.05

Inspection = 0.05

Total Raw Material cost = 0.40

Scrap cost = 0.10

Forging operator = 0.10

Over head Cost= 0.07

Tensioning of material cost = 0.05

Inspection = 0.05

Material travelling cost = 0.37

Material travelling cost = 0.05

Profit per piece= 0.08

Profit per piece= 0.32 Prime cost= 0.20

Prime cost= 0.20

Administrative OD Cost = 0.11

Administrative OD Cost = 0.11

Tool cost= 0.12

Tool cost= 0.12

Final Cost for making single piece of Piston Ring is = Rs 0.95 paise

Final Cost for making single piece of Piston Ring is = Rs 1.10

Similarly we have made comparisons of different types of layout to find the optimum machines arrangements to reduce the cost and material movements depends on product volume and variety at one intense, the factory will manufacture a wide variety of personalized products in small volumes, each of which is different at the other extreme it will produce a continuous stream of identical products in large volumes between the extremes, the industrial unit might produce various sized batches a collection of different products. Eg: 2) SPEC 1– [Ring thick-0.050 - 0.075mm, Application Large bore 2-stroke diesel engines fitted with PVA cylinder liners, with the chemical combination of carbon3.30%,silicon,1.30%,sulphur0.08%,manganese0.090%,chromi um-0.05%,copper-0.10],is used as raw material for orbital forging with sequence of operations as Tensioning, coating, quality checking, Packaging, Dispatched similarly this layout for all type of Piston ring manufacturing process through this we have achieve around Rs0.95 by minimum material travel distance by using this linear arrangement of machines. The same example is used to study the cost and same process involved but in different layout conditions now we select the L shape layout design for other analysis of layout with machine provisions.

Fig 3. Working vicinity of L-Shape ARENA Model

Eg: 3) SPEC 1– [Ring thick-0.050 - 0.075mm, Application Large bore 2-stroke diesel engines fitted with PVA cylinder liners, with the chemical combination of carbon3.30%,silicon,1.30%,sulphur0.08%,manganese0.090%,chromi um-0.05%,copper-0.10] also the same to demonstrate U shape layout and the used raw material for orbital forging with sequence of operations as Tensioning, coating, quality checking, Packaging, Dispatched which was seen earlier in all type of layout while comparing this U shape layout may not provide most favorable result. From the corollary which we got previous but for the trial and error this will be executed and the consequences are shown here with the result of software simulation model to identify the difference in production. In this L type machine arrangement design the travelling distance of material is reduced and the production rate is increased with 123(parts/hr) it is superior layout design contrast with their default design Fig: 1.The distance between each material is condensed while. We implement this machine layout for the process of making the Piston Ring in this layout is about (569mm) also this type of layout requires only one overseer and with two helpers this optimum designs to make the product with requisite time and typical shape with suitable size in less time.

Fig 4. Working vicinity of U-Shape ARENA Model

TABLE: 4 Software U-Shape Layout Design and Cost Calculation

Total Raw Material cost = 0.40

Scrap cost = 0.10

Forging operator = 0.10

Over head Cost= 0.07

Tensioning of material cost = 0.05

Inspection = 0.05

Material travelling cost = 0.37 Profit per piece= 0.08 Prime cost= 0.20 Administrative OD Cost = 0.11 Tool cost= 0.12

Total Raw Material cost = 0.40

Scrap cost = 0.10

Forging operator = 0.10

Over head Cost= 0.07

Tensioning of material cost = 0.05

Inspection = 0.05

Material travelling cost = 0.43 Prime cost= 0.20 Profit per piece= 0.02 Administrative OD Cost = 0.11 Tool cost= 0.12 Final Cost for making single piece of Piston Ring is

= Rs 1.38

Final Cost for making single piece of Piston Ring is = Rs 1.32

This U and S shaped machine arrangement the design for travelling distance of material is abridged and the more production rate as 234(parts/hr) the distance between each material is reduced while we grasp this machine layout for the process of creating of piston ring in this layout is about (685mm) in above design. But this machine arrangement requires more distance as more than (900mm) deals with the placement of machine this expanse extra area. It is not suitable for this kind of industry which produces different sized products within their working area the arena model with calculations are shown below, for future references.

V.RESULT AND DISCUSSION In this investigation of the results we are getting from software and manually the investigation of various layout designs in both single-row and multi-row layout the main intend of this study is to categorize the exact layout for proper Process industry. And moreover the major dilemma in this layout design is the idol time of machines, cost and the material movement between the each machine, sequence of operation, layout model. The linear arrangements of machine gives the good result in the machine arrangements, operation sequences and the main advantage of this kind of layout rather than others was less production time as 120(parts/hr) the distance between each material is reduced while we seize this machine layout for the process of creating the piston ring in this layout is about (190mm) this will reduce the material travelling cost of 0.05 and the profit is 0.12paise more than the regular amount with their evade arrangement. The selection of optimum calculation is shown with software model given below as the ending of this examines those good results for production [16].

Fig 5. Working vicinity of S-Shape ARENA Model

TABLE 5. Software S-Shape Layout Design and Cost Calculation

Fig 6. Working vicinity of optimized ARENA Model

REFERENCES TABLE 2. Software Linear Layout Design and Cost Calculation

Total Raw Material cost = 0.40

Scrap cost = 0.10

Forging operator = 0.10

Over head Cost= 0.07

Tensioning of material cost = 0.05

Inspection = 0.05

Material travelling cost = 0.04 Prime cost= 0.20

Profit per piece= 0.32

Administrative OD Cost = 0.11 Tool cost= 0.12 Final Cost for making single piece of Piston Ring is = Rs 0.95 paise

VI. CONCLUSION AND FUTURE WORK The proposed model determines the cell configuration with the aim of minimizing the cell formation problem and the some of advantages in cell formation simultaneously. An efficient algorithm such as ZODIAC was designed to solve the mathematical model. In order to verify the performance of this approach, we solved using the details which got through the selected process industry from the literature and our idea here we are clearly explained with the help of software ARENA and the executed results are compared using any of the quality tools here we used scattered chart to made the evaluation between the layouts through this graphical representation we can easily explain about the layout comparison and their position, here the X-axis meant for various layout designs we discussed in this chapter an Y-axis was marginal regions of product cost, finally the selection of optimum layout design and by identifying minimum material travelling distance will be one of the important task to shows the objective of cellular manufacturing in various process industry.

Fig 7.Statistical Comparisons of Various layout design

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