FIELD OF THE INVENTION 
     The present invention relates to a support apparatus for supporting a worker involved in operating an injection-molding machine. 
     BACKGROUND OF THE INVENTION 
     Injection-molding machines that inject resin material into a metal mold to manufacture a resin article are widely used in practice. In injection-molding machines, mass production molding is started after the molding conditions have been manually entered by a worker. However, the molding conditions differ according to the shape of the molded article and the properties of the resin. For this reason, the molding conditions must be determined before mass production molding can take place, and work must be performed to determine the molding conditions. 
     The work of determining the molding conditions is preferably carried out by a skilled worker having considerable knowledge and experience. However, if no such skilled worker is available, an unskilled worker must be assigned to do the work of determining the molding conditions. 
     In this case, it is useful to use a support system referred to as an expert system, as disclosed in, e.g., JP 2001-88187 A. In other words, the molding conditions can be derived even by an unskilled worker when an expert system is used. The system disclosed in JP 2001-88187 A will be described with reference to  FIG. 12  hereof. 
     In the system shown in  FIG. 12 , information data  102  related to the molded article and the metal mold is entered by the worker to an initial molding condition determination unit  103  with the aid of an input unit  101 . Machine data  105  related to the injection-molding machine is entered from a machine database file  104 , and resin data  107  related to the resin material is entered from a resin database file  106  into the initial molding condition determination unit  103 . The initial molding condition determination unit  103  calculates the molding conditions  108 . 
     A flow analyzer  109  predicts the parameters that are present during and after molding on the basis of the molding conditions  108 . When this prediction leads to molding defects, a molding condition correction unit  110  corrects the molding conditions. The worker enters the corrected molding conditions  108  to the flow analyzer  109 , and the various parameters that are present during and after molding are predicted again. The worker repeats a series of procedures until the molded article becomes an acceptable product. The final molding conditions are the suitable molding conditions  108 . 
     The worker is not required to actually perform injection molding because the flow analyzer  109  predicts the various parameters that are present during and after molding. This process is advantageous in that more suitable molding conditions can be obtained and wasted time and costs can be eliminated by repeating molding conditions correction and flow analysis without performing test injection molding. 
     In other words, the worker can know the suitable molding conditions  108  by merely entering molding and metal mold information  102  using the input unit  101 . 
     The suitable molding conditions  108  obtained in this manner still generate molding defects when actual injection molding is carried out using an injection-molding machine. The worker is unable to determine which of a plurality of molding conditions to correct when molding defects are generated. This is because the process for computing the suitable molding conditions  108  is a black box for the worker. 
     In other words, the system described in  FIG. 12  is not advantageous from the standpoint of training and developing an unskilled worker. 
     In view of the above, a technique for allowing the worker to be aware of the relationship between the molding conditions and the molded article using a viewable graph has been proposed in, e.g., JP 2006-123172 A. The technique proposed in JP 2006-123172 A will be described with reference to  FIG. 13  hereof. 
     A birefringence graph  121 , which is a single evaluation item of the molded article (disc substrate), can be displayed on a display unit  120  provided to the injection-molding machine for injecting the disc substrate, as shown in  FIG. 13 . 
     The birefringence graph  121  provides a representation of the distance from the center of the molded article to the external periphery shown on the horizontal axis  122  and the birefringence shown on the vertical axis  123 , resulting in a curved line  124 . 
     Also provided below the birefringence graph  121  are displays  125  of the molding conditions composed of “Compression Start Position,” “Heating Cylinder Temperature,” “Injection Velocity,” and “Mold Clamping Force,” as well as increase buttons  126  and decrease buttons  127  associated with the displays  125 . 
     The worker moves the curved line  124  upward or downward by pressing the increase button  126  or the decrease button  127  associated with the “Compression Start Position,” for example. The curved line  124  is alternatively rotated. 
     Specifically, the worker can be visually made aware of the manner in which the birefringence graph  121  changes when the increase button  126  is pressed, and the worker can be visually made aware of how the birefringence graph  121  changes when the decrease button  127  is pressed. Since the birefringence graph  121  can be visually confirmed, the worker can more readily understand the process. For this reason, it is apparent that the technique shown in  FIG. 13  is advantageous for worker training. 
     It should be noted that the plurality of molding conditions affects each other. For this reason, even if the relationship between a single molding condition and a single evaluation item is understood, such knowledge cannot necessarily be adequately applied to the operation of an actual injection-molding machine. Such knowledge is useful for training purposes, but is not useful for operating an injection-molding machine. 
     In view of this situation, there is a need for a support apparatus that is advantageous for training an unskilled worker and useful for operating an actual injection-molding machine. 
     SUMMARY OF THE INVENTION 
     According to the present invention, there is provided a support apparatus of an injection-molding machine for supporting a worker involved in operating an injection-molding machine, the support apparatus comprising: a neural network that uses as input items a plurality of molding conditions used when a non-defective article is obtained in test molding, uses as an output item a measured quality value obtained by measuring the non-defective article, and determines a prediction function based on the input and output items; a first input apparatus that inputs into the neural network fixed values for molding conditions other than selected molding conditions when at least one molding condition has been selected from the plurality of molding conditions; a computer that acquires a prediction function determined by the neural network, sets the output items of the prediction function to an unknown number, enters fixed values for a portion of the input items of the prediction function, enters the selected molding conditions for the remaining portion of the input items of the prediction function in the form of variables, and thereafter uses the prediction function to calculate predicted quality values corresponding to predicted values of the measured quality value; a graph generator that generates in the form of a graph a relationship between the selected molding conditions and the predicted quality values; and a display unit that selectively displays the graph generated by the graph generator. 
     Since the correlation between molding conditions selected by the worker and the prediction quality value predicted from the selected molding conditions can be viewed at any time by the worker, the manner in which the quality value changes can be visually observed by varying the molding conditions. The training effect for an unskilled worker is enhanced because the process is based on visual confirmation. 
     The predicted quality value can be applied with high reliability to mass production molding because the value is calculated using a prediction function determined based on test molding data. 
     Therefore, in accordance with the present invention, a support apparatus can be provided that is advantageous for training unskilled workers and useful for operating an actual injection-molding machine. 
     Preferably, the support apparatus further comprises a second input apparatus used by a worker to enter a target quality value, which is a target value of the quality, and a graph correction unit for correcting the graph on the basis of the target quality value entered using the second input apparatus. 
     The graph is corrected in correspondence to the target quality value entered by the worker. The worker can modify the molding conditions while referencing the changes in the graph. 
     The worker can view at any time the molding conditions that correspond to the target quality value specified by the worker himself. 
     It is also preferred that a plurality of graphs be displayed on the display unit when there is a plurality of the selected molding conditions. The plurality of molding conditions affects each other. However, the worker can easily be made aware of the relationship between the molding conditions by displaying a plurality of graphs. 
     It is also preferred that the quality value be the product weight. The product weight is the most important value among the various quality values, and resin material can be saved by keeping the product weight constant. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Certain preferred embodiment of the present invention will be described in detail below, by way of example only, with reference to the accompanying drawings, in which: 
         FIG. 1  is a block diagram illustrating an injection-molding machine in conjunction with its support apparatus according to the present invention; 
         FIG. 2  is a view showing the basic theory of the neural network included in the support apparatus; 
         FIGS. 3A and 3B  are diagrams showing examples of messages displayed on the display unit included in the support apparatus; 
         FIG. 4  is a diagram illustrating the effect of a computer included in the support apparatus; 
         FIG. 5  is a view of a graph displayed on the display unit; 
         FIGS. 6A and 6B  are views showing examples of messages related to graph correction; 
         FIG. 7  is a view of a corrected graph; 
         FIGS. 8A and 8B  are views showing other examples of displayed messages; 
         FIG. 9  is a view showing yet another example of a corrected graph; 
         FIG. 10  is a view showing yet another example of a display message; 
         FIG. 11  is a view showing yet another example of a corrected graph; 
         FIG. 12  is a view illustrating a basic principle of a conventional support system; and 
         FIG. 13  is a view illustrating the basic principle of a support system using a conventional graph. 
     
    
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     As shown in  FIG. 1 , an injection-molding machine  10  is mainly composed of a mold clamping apparatus  12  that clamps a metal mold  11 , and an injection apparatus  13  that injects resin into the metal mold  11 , and further comprises a support apparatus  20 . 
     The support apparatus  20  is an apparatus that supports a worker involved in operating the injection-molding machine  10 , and comprises a neural network  21  that uses as an input item a plurality of molding conditions used when a non-defective article is obtained in test molding, uses as an output item a quality value obtained by measuring the non-defective article, and establishes a prediction function based on the input and output items; a first input apparatus  22  provided for work of entering fixed values for molding conditions other than selected molding conditions when at least one molding condition has been selected from the plurality of molding conditions; a computer  23  for acquiring a prediction function determined by the neural network, setting the output items of the prediction function to an unknown number, entering fixed values for a portion of the input items of the prediction function, entering the selected molding conditions for the remaining portion of the input items of the prediction function in the form of variables, and then calculating a predicted quality value, which is a predicted value of the quality value, using such a prediction function; a graph generator  24  for generating in the form of a graph the relationship between the selected molding conditions and the predicted quality values; and a display unit  25  for displaying the graph generated by the graph generator. 
     The constituent elements are described in detail below. 
     In the neural network shown in  FIG. 2 , the input items  31 ,  32 , and  33  for the molding conditions used in test molding are, e.g., injection velocity V defined by the forward velocity of the screw; V-P switch position S defined as the switch position when a switch is made to pressure control based on injection pressure from velocity control in which the mode of the movement control of the screw is based on the injection velocity; and maintained pressure P defined as the pressure maintained when the pressure inside the cavity is kept constant immediately after injection. 
     The value of the intermediate layer  41  is determined by processing the input items  31 ,  32 , and  33  using weighting coefficients that have been established for each input and threshold value. The value of the intermediate layer  42  is determined by processing the input items  31 ,  32 , and  33  using weighting coefficients that have been established for each input and another threshold value. The intermediate layers  43  and  44  are determined in the same manner. 
     The output item  51  is determined by processing the values of the intermediate layers  41  to  44  using the weight coefficients that have been established for yet another threshold and each of the intermediate layers  41  to  44 . The output item  51  is a measured quality value, e.g., the product weight W. 
     The neural network is a function, the input items  31  to  33  and the output item  51  can therefore be known quantities, and the threshold values and weighting coefficients in the function can be unknown quantities. 
     In other words, the molding conditions for the input items  31 ,  32 , and  33 , and the product weight measured for the output item  51  are given. A computer repeatedly performs computations while correcting the weighting coefficients and threshold values until the output item  51  matches the measured product weight. The weighting coefficients and threshold values are determined when the output item  51  adequately conforms to the measured product weight. 
     At this point, the V-P switch position S, the maintained pressure P, and the injection velocity V are selected as three input items. 
     Five values S 1  to S 5  are determined for the V-P switch position S. For example, S 1  is 6.81 mm, S 2  is 7.06 mm, S 3  is 7.31 mm, S 4  is 7.56, and S 5  is 7.81 mm. 
     Five values P 1  to P 5  are determined for the maintained pressure P. For example, P 1  is 79.7 MPa, P 2  is 80.2 MPa, P 3  is 80.7 MPa, P 4  is 81.2 MPa, and P 5  is 81.7 MPa. 
     Five values V 1  to V 5  are determined for the injection velocity V. For example, V 1  is 33.8 mm/s, V 2  is 34.6 mm/s, V 3  is 35.3 mm/s, V 4  is 36.1 mm/s, and V 5  is 36.8 mm/s. 
     The molding conditions described above are entered into the injection-molding machine, the other molding conditions are kept constant, several test moldings are carried out by combination, and the weight of the resulting molded article (product weight) is measured. The molding conditions in the test molding and the measured product weights are shown in the following table. 
     
       
         
               
               
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
                 TABLE 
               
             
             
               
                   
                   
               
               
                   
                 Molding conditions in test molding 
                   
               
             
          
           
               
                   
                   
                 Maintained 
                 Injection 
                 Measured quality 
               
               
                 Experiment 
                 V-P switch 
                 pressure 
                 velocity 
                 value 
               
               
                 No. 
                 position mm 
                 MPa 
                 mm/s 
                 Product weight g 
               
               
                   
               
             
          
           
               
                 1 
                 S1 
                 P1 
                 V1 
                 6.6598 
               
               
                 2 
                 S2 
                 P2 
                 V2 
                 6.6546 
               
               
                 3 
                 S3 
                 P3 
                 V3 
                 6.6565 
               
               
                 4 
                 S4 
                 P4 
                 V4 
                 6.6629 
               
               
                 5 
                 S5 
                 P5 
                 V5 
                 6.6627 
               
               
                 6 
                 S1 
                 P2 
                 V3 
                 6.6635 
               
               
                 7 
                 S2 
                 P3 
                 V4 
                 6.6687 
               
               
                 8 
                 S3 
                 P4 
                 V5 
                 6.6671 
               
               
                 9 
                 S4 
                 P5 
                 V1 
                 6.6633 
               
               
                 10 
                 S5 
                 P1 
                 V2 
                 6.6345 
               
               
                 11 
                 S1 
                 P3 
                 V5 
                 6.6695 
               
               
                 12 
                 S2 
                 P4 
                 V1 
                 6.6657 
               
               
                 13 
                 S3 
                 P5 
                 V2 
                 6.6667 
               
               
                 14 
                 S4 
                 P1 
                 V3 
                 6.6541 
               
               
                 15 
                 S5 
                 P2 
                 V4 
                 6.6538 
               
               
                 16 
                 S1 
                 P4 
                 V2 
                 6.6694 
               
               
                 17 
                 S2 
                 P5 
                 V3 
                 6.6663 
               
               
                 18 
                 S3 
                 P1 
                 V4 
                 6.6545 
               
               
                 19 
                 S4 
                 P2 
                 V5 
                 6.6579 
               
               
                 20 
                 S5 
                 P3 
                 V1 
                 6.6439 
               
               
                 21 
                 S1 
                 P5 
                 V4 
                 6.6742 
               
               
                 22 
                 S2 
                 P1 
                 V5 
                 6.6601 
               
               
                 23 
                 S3 
                 P2 
                 V1 
                 6.6432 
               
               
                 24 
                 S4 
                 P3 
                 V2 
                 6.6494 
               
               
                 25 
                 S5 
                 P4 
                 V3 
                 6.6576 
               
               
                   
               
             
          
         
       
     
     The number of test molding experiments is 3 elements×5 types and 125 (5×5×5=125) possibilities, but the possibilities were limited to 25 (25 experiments) as shown in the table above. 
     The V-P switch position S 1 , maintained pressure P 1 , and injection velocity V 1  noted in experiment no. 1 are entered for the input items  31  through  33  in  FIG. 2 , the product weight of 6.6598 g is entered for the output item  51 , and the function of the neural network is calculated. Next, the V-P switch position S 2 , maintained pressure P 2 , and injection velocity V 2  noted in experiment no. 2 are entered for the input items  31  through  33  in  FIG. 2 , the product weight of 6.6546 g is entered for the output item  51 , and the function of the neural network is calculated. The above-described procedure is also used for experiment nos. 3 through 25. In other words, the certainty of the functions can be increased by repeating the calculations 25 times. 
     The prediction function (neural network function) is determined by the above procedure. The prediction function thus determined is stored in the computer  23  in  FIG. 1 . 
     The effect of the first input apparatus  22  shown in  FIG. 1  will be described in the next diagram. The first input apparatus  22  is a touch panel, a mouse and/or keyboard, or the like. Specifically, the worker enters numerical values and commands using the first input apparatus  22  when a message is displayed on the display unit  25 . 
     A message is displayed on the display unit  25  as shown in  FIG. 3A . The plurality of molding conditions is the V-P switch position, maintained pressure, and injection velocity, and these conditions are therefore displayed. The value boxes are aligned, and the injection velocity is selected via the first input apparatus. 
     At this time, the display contents of the display unit  25  are changed to the display contents shown in  FIG. 3B . Specifically, the unselected molding conditions are displayed. The parenthesized numbers and oblong boxes are aligned. The parenthesized numbers are displayed in a numerical range used in TABLE 1, specifically, a range of numbers used for learning. The worker enters the value, e.g., “7.01” in the V-P switch position item and the value “79.7” in the maintained pressure item while referencing the parenthesized numbers. 
     The unselected molding conditions can be fixed using the procedure described above. 
     The prediction function (neural network function), a V-P switch position of “7.01,” and a maintained pressure of “79.7” are given to the computer  23  in  FIG. 1 . 
     Next, the operation of the computer  23  will be described. 
     In the computer, “7.01” is given as a fixed value to the input item  32 , and a “79.7” is given as a fixed value to the input item  33 , as shown in  FIG. 4 . The injection velocity is then given as a variable to the input item  31 . Specifically, the learning range of values of the injection velocity, i.e., 33.8 through 36.8, is finely divided and given as 33.80, 33.81, 33.82, . . . , 36.79, and 36.90. A single product weight per single variable is calculated for the output item  51  using the prediction function (neural network function). 
     A graph in which the injection velocity is represented on the horizontal axis (x axis) and the product weight is represented on the vertical axis (y axis) is generated in the graph generator  24  in  FIG. 1 . The generated graph is displayed on the display unit  25 . A display example of the display unit  25  will be described in the next diagram. 
     The graph  53  generated in the graph generator  24  is displayed on the display unit  25 , as shown in  FIG. 5 . Also displayed on the display unit  25  are the selected molding conditions, the fixed value of the V-P switch position, and the fixed value of the maintained pressure. 
     The effect of the second input apparatus  26  of  FIG. 1  will be described next. The second input apparatus  26  is also a touch panel, a mouse and/or keyboard, or the like. Specifically, the worker enters numerical values and commands using the second input apparatus  26  when a message is displayed on the display unit  25 . The second input apparatus  26  may double as the first input apparatus  22 . In this case, the input apparatuses have been separately provided for convenience of description. 
     A message inquiring whether a target quality value will be specified is displayed on the display unit  25  as shown in  FIG. 6A . The process returns to  FIG. 5  when “Do not specify” is selected. In this case, the worker has selected “Specify.” At this time, the characters of the product weight, (range of numerical values), and an oblong value box is displayed, as shown in  FIG. 6B . The numerical range (6.6345 through 6.6742) conforms to the range noted in the rightmost column of TABLE 1. The worker enters the value, e.g., “6.6540” while referencing the parenthesized numbers. 
     At this point, the graph correction unit  27  of  FIG. 1  corrects the graph as shown in the next diagram. 
     A horizontal line  54  that corresponds to the specified value  6 . 6540  is added to the graph  53 , as shown in  FIG. 7 , and a vertical line  55  is added downward from the point at which the horizontal line  54  intersects the curved line. The vertical line  55  intersects the x axis at the scale position  34 . 62 . 
     Also displayed in the lower portion of the display unit  25  are a product weight of 6.6540 g as the target quality value, and an injection velocity of 34.62 mm/s as the specific molding condition. 
     In other words, if the desired product weight is 6.6540 g, an injection velocity of 34.62 mm/s is recommended, and the worker can set the injection velocity on the basis of this message and carry out injection molding. 
     A detailed description is omitted, but in  FIG. 7 , the worker can manually change the numerical values “7.01,” “79.7,” and/or “6.6540.” The graph  53  and the numerical value of the injection velocity are modified in accordance with the manual modifications of the worker. 
     Therefore, the worker can readily discern the manner in which the numerical value of the injection velocity as a specific molding condition changes when any numerical value is changed by any amount. 
     In addition, the numerical values can be adequately reflected in mass production molding because molding is based on the actual results of test molding, as shown in TABLE 1 and  FIG. 2 . 
     A modified technique of the present invention will be described next. 
     A new message is displayed on the display unit  25 , as shown in  FIG. 8A . The plurality of molding conditions is the V-P switch position, the maintained pressure, and the injection velocity, and these conditions are therefore displayed. The value boxes are aligned and the V-P switch position and injection velocity are selected via the first input apparatus. 
     At this time, the display contents of the display unit  25  are changed to the display contents shown in  FIG. 8B . Specifically, the unselected molding conditions are displayed. The parenthesized numbers and oblong boxes are aligned. The parenthesized numbers are displayed as a learning range of the numerical values used in TABLE 1. The worker enters the value, e.g., “79.7” in the maintained pressure item while referencing the parenthesized numbers. 
     At this point, the graphs  53  and  56  are displayed on the display unit  25 , as shown in  FIG. 9 . Also displayed on the display unit  25  are the selected molding conditions (injection velocity and V-P switch position) and the value  79 . 7 , which is the fixed value of the maintained pressure. 
     Furthermore, a horizontal line  54  corresponding to the specified value  6 . 6540  is added to the graphs  53  and  56 , and a vertical line  55  and vertical line  57  are added downward from the point at which the horizontal line  54  intersects the curved line. The vertical line  55  intersects the x axis at the scale position  34 . 62 , and the vertical line  57  intersects the x axis at the scale position  7 . 01 . 
     Also displayed in the lower portion of the display unit  25  are a product weight of 6.6540 g as the target quality value, and an injection velocity of 34.62 mm/s and a V-P switch position of 7.01 mm as the specific molding conditions. 
     If the desired product weight is 6.6540 g, an injection velocity of 34.62 mm/s and a V-P switch position of 7.01 mm are recommended, and the worker can set the molding conditions on the basis of this message and carry out injection molding. 
     A detailed description is omitted, but in  FIG. 7 , the worker can manually change the numerical values “79.7” and/or “6.6540.” The graphs  53  and  56  are modified in accordance with the manual modifications of the worker, and the numerical value of the injection velocity and the V-P switch position are modified thereby. 
     Therefore, the worker can discern the manner in which the numerical value of the injection velocity and the numerical value of the V-P switch position as specific molding conditions change when any numerical value is changed by any amount. 
     Described next is the ability to input numerical values that are outside of the range of molding conditions. 
     The parenthesized numerical values of the V-P switch position are 6.81 to 7.81, but a value, e.g., “6.57,” that is less than this range can be entered, as shown in  FIG. 10 . In the same manner, the parenthesized numerical values of the maintained pressure are 79.7 to 81.7, but a value, e.g., “79.2,” that is less than this range can be entered. 
     As a result of the above-described inputs, the curved line of graph  53 A is moved upward overall, as shown in  FIG. 11 . In the graph  53 A, the range between points P 01  and P 02  is the range used for learning (range of the injection velocity of TABLE 1). 
     The graph generator  24  of  FIG. 1  extends the curved line of graph  53 A by extrapolation on the basis of the demands in  FIG. 10 . Specifically, in graph  53 A of  FIG. 11 , the broken curved line to the left of the point P 01  is extended and the broken curved line to the right of the point P 02  is extended. 
     Extrapolation extends the trend of the curved line between the points P 01  and P 02  on the basis of the assumption that application can also be made beyond the points P 01  and P 02 . Since this is an assumption, the reliability of the portions extended outward by extrapolation is reduced. For this reason, the extrapolated portion is drawn using a “broken line.” 
     The horizontal line  54  and vertical line  55  are drawn based on the product weight 6.6540 as the target quality value. The vertical line intersects the scale on the x axis at 33.71. This value  33 . 71  is displayed in the lower right portion of the screen. 
     In this manner, a graph can be drawn and specific molding conditions can be calculated even when a numerical value is entered beyond the range. 
     In addition to injection velocity, V-P switch position, and maintained pressure as the molding conditions, other items include injection time, cooling time, and other items related to the molding cycle, as well as screw rotational speed, backpressure, heating cylinder temperature, and other plasticizing conditions. Also, the quality value, in addition to product weight, may be dimensions, warping, birefringence, or another measurable value. 
     Obviously, various minor changes and modifications of the present invention are possible in light of the above teaching. It is therefore to be understood that within the scope of the appended claims the invention may be practiced otherwise than as specifically described.
A support apparatus of an injection-molding machine has a neural network that receives test molding data corresponding to molding conditions and a quality value obtained by measuring a non-defective molded article, and that determines a quality prediction function based on the received test molding data. A computer calculates a predicted value of the quality value using the quality prediction function. An input apparatus inputs into the neural network fixed values for the molding conditions except for a selected at least one of the molding conditions, and inputs a target value of the quality value. A graph generator generates a graphical relationship between the selected at least one molding condition and the predicted value. A graph correction unit corrects the graphical relationship generated by the graph generator on the basis of the target value. A display unit selectively displays the graphical relationship generated by the graph generator and the graphical relationship corrected by the graph correction unit.