The cell has been modelled following the steps that are typical of the production scenarios simulation:
1. Study of the machinery operations and operational constraints;
2. Study of the stages of processing and identification of operations sequences:
It is important to keep track of the steps and key actions in which all the resources are involved within the production cell, according to a cyclic sequence:
o The pre-forming machine releases the wrought parts and the operator unloads the machine with the cart-gripper and piles the part up in the basket after marking it (1-2 of Fig. 1);
o The operator moves the cart-gripper in front of the door of the rotating electric furnace and waits for the next billet to be ready to be unloaded (2-3 of Fig. 1);
o The furnace door opens automatically and the operator grabs the hot billet and positions it over the forks on the lift truck in the loading area of the pre-forming machine. The operator launches the drawing sequence pushing a button commanding the beginning of the next processing cycle (3-1 of Fig. 1);
o While the pre-forming machine is working, the cart-gripper is moved toward the input buffer to grab a new billet so to load the furnace with the ingot: when the furnace door closes, the furnace software starts counting the heating time of the part (4-3 of Fig.
1).
Both the supply of pallets of the blank billets in the input buffer and the stocking of the baskets of worked parts from the output buffer are not an operator’s job task, and that is why these operations are considered external to the system;
Capacity of the furnace over time: Figure 2 shows the ideal situation of the loading of four pieces starting from an empty furnace. When the piece 1 has completed its heating time, it is ready to be unloaded and it is replaced with the 5 only after a delay relative to the
discharge-charging time (sc of Fig. 2), which is the time during which the furnace is at a lower capacity and is the sum of some technical times.
In addition to the standard "up to speed" working cycle, there are transient conditions such as in the loading/unloading situation from the empty/full oven and in the transition from one order number to the next.
Collection and elaboration of characteristic time intervals: for the determination of the time intervals of the activities the direct detection method is used. Standard time intervals for operator movements have been defined. A standard time of 15 minutes is set for the
changing order operations carried out by the operator on the pre-forming machine;
Figure 1: Movements of the operator with the hand truck.
Figure 2: Furnace capacity in time, in an ideal situation with 4 pieces inside heating.
Number of pieces heating on the basin, set on the furnace control software: in the ideal case of a furnace without any delay
(instantaneous unloading-loading procedure), number of pieces can be obtained from:
heating time of a piece number of pieces
time interval between two consecutive unloads
In the real case the delay time between loading and unloading must be added to the numerator, while at the denominator the actual
discharge rate consists of the cogging operation plus some handling operations;
3. Building the logical-mathematical model with Arena software, that provides:
Definition of the input: characteristics of the orders that are processed in time from the beginning to the end of the reference week and rates of processing steps;
Deterministic model as the time value are constants;
Resources: the furnace is modelled with variable capacity depending on the number of pieces inside for every order; the operator and the pre-forming machine are modelled as resources with a capacity equal to 1;
Flowchart diagram, which reflects the actual logical sequence of events that occur in the production cell in the reference week (Fig. 3).
In Figure 3 the rectangular areas marked with different letters represent different logic:
o Loading logic of the furnace from an empty basin (a);
o Buffer of cold and blank billets (b);
o Furnace in the heating operation and the unloading sequence carried out by the operator (c);
o Furnace logic for the unloading-loading time intervals, even across several orders (d);
o Phases of the cogging operations, when the operator loads the machine and when the piece has finished the process (e);
o Unloading of the preformed part from the machine (f);
4. Verification and validation of the model: the correct functioning of the model has been checked (debugging) and the results were compared to the performance of the real system. This is done by comparing the actual times of production to those of the simulated process with reference to a fairly wide production time frame (a week). The histogram in Fig. 4 shows that the simulated production time follows sufficiently well the trend of real times, having a mean Cycle Time Ratio CTR (ratio between the Standard cycle time and the Real cycle time) of 95.2% in 3 weeks of validation.
The simulated times are always lower than those of the real system (approximately 5% less). The variability of the production of the actual cell is attributable to:
Operator, who may call the unloading a few seconds before completing the unloading of the pre-formed part;
Set-up time of the machines, which is very variable;
Order changing times, which may lead to the discharge of some parts from the furnace to avoid too long an overheating time of the pieces;
Calculated real time of production.
The validated model can predict the times of production of a certain number of orders, with any number of pieces with different characteristics;
5. Analysis of the model output results: with reference to simulations carried out for the significant week, some results have been determined:
Average, minimum and maximum time values for the heating process that are 47.2, 38.0 and 65.0 minutes for the heating process and 2.9, 2.1 and 4.5 minutes for the cogging process;
Figure 3: Flowchart of the cell model.
Figure 4: Validation of the model.
Accumulated time on all entities for each process: heating (821.6 hours) and drawing (49.6 hours);
Utilization of resources, defined by the ratio of the number of units that are busy to the number of units that are scheduled: time-averaged results are 0.54 for cart-gripper, 0.95 for the furnace and 0.54 for the pre-forming machine;
Total number seized, that is the total number of times in which an entity seizes a unit of resource throughout the simulation: cart-gripper 3146, furnace 1044 and drawing machine 1058;
Over-heating time that is the actual extra time a piece spends inside the furnace compared to its standard heating time. This is generally undesirable.
6. “What-if?” experiments and study of feasible improvements: the number of pieces inside the furnace set for each order influences both the residence time (over heating time) of the pieces at high temperatures and the production capacity of the cell, which is disconnected from the subsequent processing in the department (forging with the power hammer) because of the presence of a stock for semi-finished drawn parts. Ignoring the setup time, Figure 5 shows the simulated production time as well as overheating time by varying the number of pieces set in the furnace (adding or subtracting pieces compared to the calculated analytical number). It shows that:
Increasing the number of pieces in the furnace compared to those determined analytically, the capacity does not change substantially because the processing times are dictated by the subsequent pre-forming operation. That means that even if the number of pieces is increased inside, the furnace is always available to provide hot pieces. The modest reduction in simulation time (just over 1%) is attributable to the shorter waiting time that may occur during order change phases, though this means increasing the overheating phenomenon;
Decreasing the number of pieces in the furnace, the over-heating times will also decrease and the phenomenon can already be considered almost negligible with one piece less (3.6%). The
decrease of the number of pieces in the furnace has the advantage of the reduction of heating time and, if desired, the further advantage of adaptation of the productivity from a pull perspective in the forging department.
Following the analysis both of the stages of processing and the cycle time, questions have been raised as to find which changes could improve the process. Among the different solutions to be proposed for improvement, those which would make the least possible changes to the structure of the cell were preferred, in order to consider minimizing the cost/benefit ratio. In order to reduce the processing cycle time what could change eliminating the
operational constraint that consists of a preforming-machine unloading before furnace discharging has been determined. The new operations by the operator would be: loading the cogging machine, waiting for the piece to be pre-formed (meanwhile loading the furnace), then calling the discharge of a new piece from the furnace when the piece is worked, load the pre-forming machine with a new workpiece and start the next cycle, only then grasping the worked part and moving it to the basket of drawn workpieces.
From the comparison between the current simulation time and those with the elimination of the operational constraint on the pre-forming machine unloading, in Figure 6 it can be seen that:
Simulation time (cell productive capacity) is almost identical if the same number of pieces in the furnace are kept (as already outlined in Fig. 5);
If the number of parts in the furnace are increased, simulation time drops, and reference condition is shifted to two pieces more in the furnace, compared to the configuration with the constraint;
Figure 5: Simulation times and overheating times at different number of pieces in the furnace.
Figure 6: Simulation times of the current cell configuration and those with the elimination of the operational constraint on the pre-forming machine
unloading, at different number of pieces in the furnace.
Decreasing the number of pieces, the production capacity decreases and the simulation time increases.
An increase in production capacity entails an increase in resources utilization, too.