Advanced Technologies for Developing Countries
September 21-24, 2005
Slavonski Brod, Croatia /
VARIANTS OF PROCESS PLANNING – STEP TOWARD PRODUCTION PLANNING
P. Cosic, N. Volarevic, D. Lenac
Keywords: process variants, shape complexity, analysis of variance
1. Introduction
In the process production, the most important step is making a process plan. In spite of the importance of process planning in the manufacturing cycle, there is no formal methodology, which can be used, or can help to train personnel for this job. As different process planners have different experience, it is not wonder that for the same product, different process planners will create different processes and variants.
2. Variants of process planning
Process planning is a decision-making process. The most frequently approach is intuitive approach with the all results of this approach. The objective is to devise an economic process plan. Possible parameters to consider are: part geometry, part raw material, part dimensional accuracy, part surface finish, part geometric tolerances, part heat treatment, quantity required, etc.
The decisions to make are: select type of metal removal process, select machine for the job, select chucking type and location, select detail operations, select tooling for each operation, select path for each operation, select cutting conditions for each operation.
Wrong sequence of decisions may result in artificial constraints, because if the sequence of decisions were different, the constraints might not have existed.
There are few reasons to generate variants of process planning. These reasons can influence individual or as the combination some of them, often with different level of influence. We can mention some of the most important reasons:
Influence of non adequate competitiveness and bad response of the market (improvement of the process plan),
Development of the process plans for the new products
Capital investment, type and number of machines to needed,
Integration of process and production planning
2.1Response of market and non adequate competitiveness
Department of marketing/sales can observe in one period of selling decrease of profit, or number of sold products, or level of quality, competitiveness, etc.Managing of a company calls for many economic decisions such as the economics of manufacturing a certain product, capital investment and cash flow needs, type and number of machines to needed, number of employees, due date of delivery, layout, etc. A decision may be based on accurate data, on partially estimated data, or intuition. One of the major sources of data comes from process planning. Thus the one important role of process planning in management activities is to supply accurate data in order to arrive at good decision. As the result of this analysis management can discuss process planning optimization, part optimization, product optimization-scheduling, maximum profit process planning, etc.
2.2Development of the process plansfor the new products
In introducing a new product in the company, the finance department wants to know its manufacturing cost. To answer this question within reasonable accuracy, the bill of material (product structure) for the product has to be broken down giving a list of all required parts and their quantity for a single product. For each part on the list, a process plan will be made. The finance personnel will translate this data into costs.
Design has to meet product specifications as given by management or customer. The designer task is to translate the given specifications to engineering drawings. There is no single solution to a design problem, but rather a variety of possible solutions which surrounds a broad optimum. The designer has to consult first with a process planner. The process planner has role in design to assist the designer by introducing design features that will meet design objectives and at the same time reduce processing cost and lead time.
2.3Capital investment, type and number of machines to needed
For example, management would like to know what capital investment has to be made in manufacturing facilities. To answer this inquiry, data from process planning will be multiplied by the quantity of products to be manufactured per period. In the case of using the same facility for several operations, the total time required of each facility is summed up.
When the total time per period is known, the number of required facilities of each type can be computed. Knowing the cost of each working station, management can transform this data into total investment.
2.4Integration of process and production planning
Process planning is the first step toward the production planning. Usually, planner does not consider time and use of industrial equipment. Scheduling in production management is very important function in manufacturing and has to be well matched with process planning. In design of process planning planners usually make plans on the basis of intuition. Usually, at this moment they have not any information about occupation some machines, priorities in occupation some machines for individual parts, waiting, machine tools troubles, organisational problems, etc. The new trend in process planning can be described as integration of the two functions of process and production planning in order to get better productivity. There is no way or method to estimate which machine will be overloaded and which underloaded.
2.5Important and observed factors in variants
The possible important factors in the variants of machining part can be:
- Selection of the primary process,
- Shape and size of the product,
- Product dimensional accuracy,
- Dimensional and geometrical tolerances,
- Material, heat treatment, requested hardness, surface finish,
- Size of the batch and quantity required,
- Frequency of the repeating the batch size,
- Available machines, tools, fixtures, chucks, clamping devices.
In this case (Table 1, Figure 1, Figure 2) we have discussed some variants of machining the required product (Figure 2). We have observed the influence of primary process and the level of automation on the machining costs and times. Why? From passed experience and cases, the influence of shape complexity, primary process and level of automation can be recognized as the most significance and measurable factors through the values of times and costs.
Figure 1. Forging of the observed part [3]
Figure 2. Final shape of the part after machining [3]
2.5.1Influence of primary process
The following factors would be the basis for decision support selection of the manufacturing process as the primary process : a) quantity, b) complexity of form, c) nature of material, d) size of part, e) section thickness, f) dimensional accuracy, g) cost of raw material, h) possibility of defects and crap rate, etc. [1]When process choice is based on the individual’s knowledge, close familiarity with one process or one class of processes can led to priority process choice. Once technical feasibility is established, process choice is further narrowed by cost and availability.
The decision tables [1] give general guidelines only and are based on good standard practices. Each table have characteristics of part (material, shape, size of part, minimum section, minimum hole diameter, surface detail, uniformity of cross, section thickness, dimensional accuracy), cost (equipment, die, labor, finishing), production (operator skills, lead time, rates, pieces/machine, minimum quantity or length) for different processes.
2.5.2Influence of the level of automation
We discuss two sufficient spaced out levels(as statistical contrasts) of automation machine tools (universal and working centre).
2.5.3Influence of shape complexity of products[5]
In terms of manufacturing processes, production costs and quality of the end product, complexity plays a vital role in achieving the best design and selection of the most suitable manufacturing process. We would like to examine relation between shape complexity and production costs, technology used to produce such part. To do so we need some kind of criterion for part shape complexity. This is our attempt to create some sort of quantification.Our aim is to compute the shape complexity number. Complexity is expressed as entropy of the curvature probability density along contour. To be able to determine curvature probability density we must find mathematical expressions that give a good approximation of the shape. Input is image of shape in bitmap file format. The first step is to find coordinates of pixels on contour of the shape. An algorithm was written for this purpose. These points are stored. Next algorithm analyses points and searches for vertices, straight segments, places where x or y values change trend. This is important because points are grouped in logical segments. Points belonging to certain logical segments are approximated by lines/curves. Algorithm contains goodness measure for these approximations and when it is satisfied each logical segment is presented by its mathematical equation (Figure 3a). This is input for another part of algorithm which needs to find curvature change along the contour of the shape. This curvature should be computed in finite number of points which are uniformly sampled across curve. Since points are sampled uniformly along contour instead of curvature change we used angle change along contour (Figure 3b). After that we need to estimate the probability density function of these samples (Figure 4) and compute the entropy of the shape. Expression for entropy calculation is:, where pi is probability of certain angle change along contour. Important thing is that this approach gives exact value of shape complexity, so different shapes can be compared.
+
Figure 3Analysis of the shape: a) splines (with nodes) that approximate shape;
b) tangents on sample points along contour
Figure 4 Probability distribution of samples with entropy value (H)
Shape complexity from this method is probably not enough to define the part enough for classifications such as group technology or for shape recognition. Some more information is needed like number of faces in a model, number of sides of a polygon, symmetry, number of turns, degree of compactness, angular variability.
Since we know mathematical expressions that describe the shape we can use them to generate other information about specific part, and use all this information for different kind analysis regarding process planning.
The classification of shape is important in many aspects of engineering. It is often useful to group parts by their required manufacturing processes or common features, as addressed by group technology codes. However, shape complexity is also a major factor in the determination of component manufacturing difficulty and associated costs.
2.6Influence of variants on machining times and costs
Cost estimation is an essential part in the design, development and use of products. In the development and design of a manufactured product, phases include concept assessment, demonstrations of key features, and detailed design and production. The focus is on products defined by dimensions and tolerances, made from solid materials and, fabricated by some manufacturing process. As more details of the product are specified, the cost estimates should become more accurate.
In our work, we would use empirically based method of cost estimating. Our manufacturing cost estimation is based on machining time tt, preparatory time tpzs/n, auxiliary time tp (setup, tooling time) and estimated costs of machine tools per hour. Costsproduction and the quality are strongly influenced by the process plan. Creation and analysis of different process plans can improve process planning by fast and simple calculation of machining time, overall times and costs.
3. Case study: Design the experiments for the variants of machining
Designed experiments require some planning to be successful. We need to decide on our objective, a number of factors, kind of factors (qualitative, quantitative), number of levels and repetition, type of factorial design, what is our expected response, what we can decide after retrieved results of experiments, etc.
At this case, in this phase of investigation we decide to include two qualitative factors (kind of primary process and level of automation) with two levels (Table 1) for the specific product (Figure 1, 2). Why only two factors with only two levels? Therefore these mentioned factorsfrom previous experience can be significant in the analysis of variance. In the next phase of investigation, we include factor of shape complexity which can lead our investigation toward group technology, associatively connections with kind of operations (turning, milling, grinding, etc) (Figure 2),
Table 1.Production time and costs as the result of the kind of primary process and machine tools automation [4]
Production time/piece / Machine tools automationProduction costs kn/piece / Universal machine tools (M1) / Working centre
(M2)
Primary process / 1st Rolled bar (P1) / 68.13 / 19.81
184.33 / 90.03
2nd Forging (P2) / 57.51 / 13.39
151.00 / 70.80
operations sequencing, chucking, ‘’complexity’’ of machining, primary process (Figure 3), etc.
4. Analysis of the full factorial experiment
Table 2.Response: Production time in ANOVA for Selected Factorial Model
Source / Sum of Squares / Degree of Freedom / Mean Square / Mean FValue / Prob > F
Model / 2208.88 / 2 / 1104.44 / 250.44 / 0.0446
significant
Level of Automation
(M) / 2136.29 / 1 / 2136.29 / 484.42 / 0.0289
Kind of Primary Process
(P) / 72.59 / 1 / 72.59 / 16.46 / 0.1538
Residual / 4.41 / 1 / 4.41
Cor Total / 2213.29 / 3
For the observed full factorial 22 model analysis of variance and use of Fisher distribution [2, 4] (Table 2) we can conclude about level significance observed factors, interactions and Index of determination. From the response of production time per piece, we can see very high significance for factor M. Index of determination for this model was 0.9980. At this example significance for the kind of primary process is essentially lesser than factor M. Possible explanation for this result we can find in fact that the influence of machine tool productivity is more important than influence through machining times of final machining after realised primary process.
5. Conclusion
In this paper are discussed influence and significance of observed factors on production times. The most important significance is influence of automation machining tool with index of determination of 0.9980. Shape complexity is quantitative factor and would be included in the next phase of work.
Acknowledgement
This project is a part of the scientific project titled IntelligentProcess Planning and Reengineering 0120029 financed by the Ministry of Science and Technology of the Republic of Croatia in the period from 2002 to 2005. We express gratitude for the financial support of the project.
References
[1]ASM Handbook Vol.20 “Materials Selection And Design”, ASM Int., Ohio , 1997.
[2]Breyfogle III, F. W., "Statistical Methods", New York, John Wiley and Sons, 1992.
[3]Lenac, D.‘’Undergraduated work’’, TVZ, Zagreb, 2005.
[4]Montgomery, D., ‘’Design and Analysis of Experiments’’, New York, John Wiley and Sons, 2004.
[5]Volarevic, N., Cosic, P.,’’Shape complexity measure study", 16th DAAAM, 19-22.11.2005, Opatija, Croatia, 11.2005. (in evaluation process).
First Author's Predrag COSIC, Ph.D, Associated. Professor
University of Zagreb,Faculty of Mechanical Engineering and Naval Architecture, Department of Industrial Engineering, Ivala Lucica 5, Zagreb, Croatia, Telephone +385 1 61 68 340, Telefax, +385 1 6157 123; e-mail ;
Second Author's Nikola VOLAREVIC, B.Sc., Ph.D. student, Young Researcher
University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Department of Industrial Engineering, Ivala Lucica 5, Zagreb, Croatia, Telephone +385 1 61 68 377, Telefax, +385 1 6157 123; e-mail
Third Author's Dubravka LENAC, Undergraduate Work, TVZ, 2005, INA, e-mail
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