Molding machine, molding machine support device, and molding system

Abstract

To provide a molding machine capable of suitably setting a numerical range for classification.SOLUTION: In a die cast machine 1, an operation part 15 receives the input of the set value of a defective indication rate. The defective indication rate is a ratio, when plural molding cycles are performed, in which defectives including molded articles produced by the molding cycle(s) in which the measured value of a physical amount reaches a value other than a prescribed numerical value range occupy in molded articles produced by the plural molding cycles or a ratio correlated with the ratio. A machine side transmission part 35 transmits the set value of the defective indication rate inputted into the operation part 15 to a support device 31. The machine side transmission part 35 receives information on the numerical value range corresponding to the set value of the defective indication rate from the support device 31. A control device 13 performs treatment of classifying the molded articles produced by the molding cycle(s) in which the measured value of the physical amount measured by a sensor 33 reaches the value other than the received numerical value range from the other molded articles.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a molding machine, a support device for supporting the molding machine, and a molding system including the molding machine. The molding machine is a machine that fills a mold cavity with a molding material to obtain a molded product, such as a die-casting machine or an injection molding machine. [Background technology]

[0002] Molding machines that monitor various physical quantities are known (see, for example, Patent Document 1). These physical quantities include, for example, the temperature of the mold, the amount of molding material per shot, the speed at which the molding material is filled into the mold, the pressure of the molding material in the mold, and the temperature, position, speed, pressure, flow rate, and power of each part of the molding machine (or peripheral equipment) that correlate with these. By monitoring these physical quantities, it is possible to, for example, detect abnormalities in the molding machine, determine the quality of molded products, or investigate the cause of defective products. Patent Document 2 states that the quality of molded products can be determined based on the monitoring of physical quantities, but that defective products may occur even when the values ​​of the physical quantities are within the normal range. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-140033 [Patent Document 2] Japanese Patent Application Laid-Open No. 2007-196604 [Patent Document 3] Japanese Patent Application Laid-Open No. 2004-230901 [Patent Document 4] Japanese Patent Application Laid-Open No. 2001-293761 Summary of the Invention [Problem to be solved by the invention]

[0004] As described above, molded products can be determined to be good or bad by determining that a physical quantity falls within a predetermined range. However, the numerical range (upper and lower limits) is set based on, for example, the operator's experience. If the numerical range is narrower than appropriate, for example, there is a high probability that a good product will be determined to be defective and discarded. Furthermore, if the numerical range is wider than appropriate, for example, there is a high probability that a defective product will be determined to be good and subsequently processed. Defective products are discovered during further inspections in subsequent processes, but these defective products will be subjected to unnecessary subsequent processing. Thus, if the numerical range is not set appropriately, productivity will decrease. Therefore, it is desirable to provide a molding machine, a molding machine support device, and a molding system that can appropriately set numerical ranges for sorting. [Means for solving the problem]

[0005] a machine-side transmitter that transmits the set value input to the set value receiver to an assist device; a machine-side receiver that receives from the assist device information on the numerical range corresponding to the set value transmitted by the machine-side transmitter; and a sorting unit that performs a process of sorting molded products produced by a molding cycle in which the measurement value of the physical quantity measured by the sensor falls outside the numerical range received by the machine-side receiver from molded products produced by other molding cycles, with the machine main body producing the molding cycle by repeating the molding cycle;

[0006] A support device for a molding machine according to one aspect of the present disclosure includes: a memory unit that stores a plurality of past data items each including measured values ​​of physical quantities related to a molding cycle that fills a cavity with molding material to produce a molded product, and information on the judgment result as to whether the molded product produced by the molding cycle from which the measured values ​​were obtained is a good product or a defective product; a support receiving unit that receives a set value of the defective product index rate from the molding machine when the proportion of the plurality of past data items in which the measured values ​​are outside the numerical range and the judgment result is a defective product, or a proportion correlated to said proportion, is referred to as a defective product index rate; a calculation unit that specifies the numerical range in which the defective product index rate for the plurality of past data items stored in the memory unit becomes the set value received by the support receiving unit; and a support transmitting unit that transmits information on the numerical range specified by the calculation unit to the molding machine.

[0007] A molding system according to one aspect of the present disclosure includes a machine main body that repeats a molding cycle in which a cavity is filled with molding material to produce a molded product; a sensor that measures a physical quantity related to the molding cycle; a memory unit that stores a plurality of past data each including a measurement value of the physical quantity and information on a determination result as to whether the molded product produced by the molding cycle in which the measurement value was obtained is a good product or a defective product; and a memory unit that stores, when a plurality of molding cycles are performed, a ratio of defective products included in molded products produced by molding cycles in which the measurement value of the physical quantity is outside a predetermined numerical range, among the molded products produced by the plurality of molding cycles, or a ratio When the ratio correlated with the case where the defective product index rate is referred to as a defective product index rate, the machine has a set value receiving unit that receives input of a set value for the defective product index rate, a calculation unit that specifies a numerical range in which the defective product index rate for the plurality of past data stored in the memory unit falls within the set value input to the set value receiving unit, and a sorting unit that performs processing to sort molded products produced by a molding cycle in which the measurement value of the physical quantity measured by the sensor falls outside the numerical range specified by the calculation unit from molded products produced by other molding cycles, with the plurality of molded products produced by the machine main body by repeating the molding cycle as the target. [Effects of the Invention]

[0008] According to the above configuration, it is possible to appropriately set the numerical range for sorting. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a side view showing a configuration of a die casting machine according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a top view showing the configuration of a molding system including the die-casting machine of FIG. 1. [Figure 3] FIG. 3 is a block diagram for explaining the data accumulation operation by the molding system of FIG. 2. [Figure 4] FIG. 3 is a block diagram for explaining the operation of setting a numerical range by the molding system of FIG. 2. [Figure 5] FIG. 3 is a block diagram for explaining a sorting operation by the molding system of FIG. 2. [Figure 6] FIG. 3 is a block diagram for explaining an auxiliary operation by the molding system of FIG. 2. [Figure 7] 3 is a schematic diagram showing an example of the configuration of data stored in the support device of the molding system of FIG. 2; [Figure 8] 3 is a schematic diagram showing an example of setting a numerical range using the molding system of FIG. 2. FIG. [Figure 9] 3 is a schematic diagram showing another example of setting the numerical ranges by the molding system of FIG. 2. FIG. [Figure 10] 3 is a flowchart showing an example of a procedure for a process of selecting a determination target by the support device of the molding system of FIG. 2; DETAILED DESCRIPTION OF THE INVENTION

[0010] In the following, first, an overview of a molding system according to an embodiment of the present disclosure will be described, and then the molding system will be described in detail.

[0011] (Overview of molding system) Fig. 1 is a side view (partially including a cross-sectional view) showing the configuration of a die-casting machine 1 included in a molding system SM1 (reference numeral SM1 in Fig. 2) according to an embodiment of the present disclosure. For convenience, Fig. 1 is illustrated with a Cartesian coordinate system xyz. The z direction is the vertical direction, and the +z side is upward.

[0012] The die-casting machine 1 holds a die 101. The die 101 includes, for example, a fixed die 103 and a movable die 105. As indicated by the two-dot chain line in Fig. 1, the die-casting machine 1 brings the movable die 105 close to the fixed die 103 and brings them into contact with each other (performs die closing). As a result, a cavity Ca having a shape similar to that of the molded product (in other words, the die-cast product or the product) is formed between the fixed die 103 and the movable die 105.

[0013] The die-casting machine 1, for example, fills (injects) an unhardened metal material into the cavity Ca. The metal material filled in the cavity Ca is solidified by the heat absorbed by the mold 101, thereby producing a molded product. Thereafter, the die-casting machine 1 separates the movable mold 105 from the fixed mold 103 to remove the molded product (performs mold opening). The die-casting machine 1 repeats a molding cycle in which, for example, mold closing, injection, and mold opening are performed in sequence as described above.

[0014] FIG. 2 is a schematic top view showing the configuration of the molding system SM1.

[0015] The molding system SM1 has a removal device 21 that removes the molded product 107 from the opened mold 101. The removal device 21 may be regarded as a device separate from the die-casting machine 1, or may be regarded as part of the die-casting machine 1. For convenience, the description of this embodiment may be expressed assuming the former understanding.

[0016] The take-out device 21 places the removed molded product 107 on, for example, a conveyor 23. The molded product 107 transported by the conveyor 23 is subjected to, for example, a post-processing device 29. Examples of the post-processing include heat treatment, deburring, cutting, surface treatment, and / or assembly. In cutting, for example, unnecessary parts such as biscuits are removed.

[0017] The molding system SM1 also has various sensors (not shown) that measure various physical quantities related to the molding cycle. These physical quantities are, for example, physical quantities whose values ​​change as the molding cycle progresses and / or whose values ​​differ between molding cycles. Specific examples of the physical quantities include the temperature of the mold 101, the temperature of the metallic material, the amount of metallic material per shot, the speed at which the metallic material is filled into the mold 101 (injection speed), the pressure of the metallic material in the mold 101 (injection pressure or casting pressure), the temperature or supply amount of substances other than the metallic material (e.g., cooling water, air, or mold release agent), and the temperature, position, speed, pressure, flow rate, and power of each part of the die-casting machine 1 (or peripheral equipment) that correlate with these.

[0018] In the following, for convenience, explanations or expressions may be given focusing on only one type of physical quantity unless otherwise specified.

[0019] The physical quantities as described above correlate with the quality of the molded article 107. Therefore, the take-out device 21 sorts the molded articles 107 depending on whether the measured values ​​of the physical quantities are within a predetermined numerical range. For example, if the measured values ​​of the physical quantities are within the numerical range, the take-out device 21 places the molded article 107 on the conveyor 23 as described above. On the other hand, if the measured values ​​of the physical quantities are outside the numerical range, the take-out device 21 places the molded article 107 in the defective product box 25. This prevents molded articles 107 that are highly likely to be defective from being subjected to subsequent processes.

[0020] For convenience, hereinafter, molded products produced by a molding cycle in which the measured values ​​of physical quantities are within a predetermined range may be referred to as "normal molded products." Furthermore, molded products produced by a molding cycle in which the measured values ​​of physical quantities are outside the predetermined range may be referred to as "abnormal molded products." Additionally, the terms "normal" and "abnormal" may be used in the same manner as above. Unless otherwise specified, multiple molded products refer to products of the same type (products with the same shape, dimensions, and material).

[0021] FIG. 8 is a schematic diagram showing an example of setting the numerical range.

[0022] In this diagram, the horizontal axis represents the order N of the repeated molding cycle. The vertical axis represents a predetermined physical quantity PQ. The circle plots represent the value of the physical quantity PQ in molding cycles in which good products were produced. The cross plots represent the value of the physical quantity PQ in molding cycles in which defective products were produced. Here, the values ​​of the physical quantity PQ for a total of 10 molding cycles, from N1 to N10, are shown.

[0023] Line LL40 indicates the lower limit of the numerical range R40. Line LU40 indicates the upper limit of the numerical range R40. In the illustrated example, five physical quantities, N1 (good), N3 (defective), N4 (good), N7 (defective), and N10 (good), fall within the numerical range. In the illustrated example, which is the opposite of the above, five physical quantities, N2 (defective), N5 (defective), N6 (defective), N8 (defective), and N9 (defective), fall outside the numerical range.

[0024] As shown in this figure, even if the physical quantity falls within the numerical range R40, the molded product may be defective (N3). In other words, a molded product in a normal state is not necessarily a good product. Also, as shown in this figure, even if the physical quantity falls outside the numerical range R40, the molded product may be good (N5). In other words, a molded product in an abnormal state is not necessarily a defective product.

[0025] In the illustrated example, narrowing the range of values ​​reduces the likelihood that defective products will be classified as normal molded products, while increasing the likelihood that good products will be classified as abnormal molded products. If the likelihood of good products being classified as abnormal molded products increases, the likelihood that good products will be discarded also increases.

[0026] Conversely, if the numerical range is widened, for example, the probability that a defective product will be classified as a normal molded product increases, while the probability that a non-defective product will be classified as an abnormal molded product decreases. If the probability that a defective product will be classified as a normal molded product increases, the probability that the defective product will be subjected to subsequent processing also increases.

[0027] Here, the ratio of defective products in an abnormal state to any number of molded products, regardless of whether they are normal or abnormal, and regardless of whether they are good or bad (from another perspective, all molded products), is referred to as the "defect rate." Furthermore, a ratio correlated to the defect rate is referred to as the "defect index rate." Examples of defect index rates other than the defect rate include the ratio of defective products in a normal state to all molded products (good products and defective products) in a normal state (hereinafter sometimes referred to as the "defect content rate"), and the ratio of defective products in an abnormal state to all defective products in a normal and abnormal state (from another perspective, all defective products) (hereinafter sometimes referred to as the "defect detection rate").

[0028] From another perspective, the defect index rate can be defined as the value obtained by dividing the number of defective products under normal conditions or the number of defective products under abnormal conditions by the number of a predetermined population. The population may be appropriately selected from all molded products, molded products under normal conditions, molded products under abnormal conditions, and all defective products. From yet another perspective, the defect index rate can be defined as the rate that changes when the number of defective products under normal conditions (or, from another perspective, the number of defective products under abnormal conditions) changes by changing the numerical range for the same population (in other words, a predetermined number of molded products). Furthermore, for example, when the same population is used, the defect detection rate and the proportion of defective products under normal conditions among all defective products are constant, so the two can be considered equivalent.

[0029] In the example of Figure 8, the defective product content rate is 20% (1 / 5 x 100), the defect rate is 40% (4 / 10 x 100), and the defect detection rate is 80% (= 4 / 5 x 100).

[0030] Furthermore, the proportion of good products in the abnormal state among molded products (good products and defective products) in the abnormal state will be referred to as the "good product content rate." Furthermore, the good product content rate or a rate correlated to the good product content rate will be referred to as the "good product index rate." Examples of good product index rates other than the good product content rate include the proportion of good products in the normal state among molded products in the normal and abnormal states (all molded products), and the proportion of good products in the normal state among good products in the normal and abnormal states (all good products).

[0031] From another perspective, the non-defective product index rate can be defined as the value obtained by dividing the number of non-defective products under normal conditions or the number of non-defective products under abnormal conditions by the number of products in a predetermined population. The population may be appropriately selected from all molded products, molded products under normal conditions, molded products under abnormal conditions, and all non-defective products. From yet another perspective, the non-defective product index rate can be defined as the ratio that changes when the number of non-defective products under normal conditions (or, from another perspective, the number of non-defective products under abnormal conditions) changes by changing the range of values ​​for the same population (i.e., a predetermined number of molded products).

[0032] In the example of Figure 8, the non-defective product content rate is 20% (= 1 / 5 x 100). The proportion of non-defective products in normal conditions among all molded products is 40% (= 4 / 10 x 100). The proportion of non-defective products in normal conditions among all non-defective products is 80% (= 4 / 5 x 100).

[0033] To deepen understanding of the defective product index rate and the non-defective product index rate, Figure 9 shows an example in which a numerical range R20 different from the numerical range R40 in Figure 8 is set for the measurement results of the same physical quantity as in Figure 8. Figure 9 is a diagram similar to Figure 8, with lines LU20 and LL20 indicating the upper and lower limits of the numerical range R20.

[0034] 9, eight physical quantities, N1 (good), N2 (defective), N3 (defective), N4 (good), N5 (good), N6 (defective), N7 (good), and N10 (good), fall within the numerical range R20. In the example shown, two physical quantities, N8 (defective) and N9 (defective), are outside the numerical range R20, which is the opposite of the above.

[0035] In the example of Figure 9, the defective product content rate is approximately 37.5% (3 / 8 x 100), the defect rate is 20% (2 / 10 x 100), and the defect detection rate is 40% (= 2 / 5 x 100).

[0036] In addition, in Figure 9, the non-defective product content rate is 0% (= 0 / 5 × 100). The proportion of non-defective products in normal conditions among all molded products is approximately 50% (= 5 / 10 × 100). The proportion of non-defective products in normal conditions among all non-defective products is 100% (= 5 / 5 × 100).

[0037] In this embodiment, the set value receiving unit of the molding system SM1 receives an input of a set value for the defective index rate. More specifically, for example, an operation unit (described later) of the die-casting machine 1 receives an input of a value for the defective index rate desired by an operator. Then, the molding system SM1 calculates a numerical range (upper and lower limits) within which the set value for the defective index rate is realized, based on data correlating values ​​of physical quantities in past molding cycles with the pass / fail judgment results of molded products produced by those molding cycles.

[0038] By calculating the numerical range for achieving the set value of the defective product index rate based on past data in this way, it is possible to set the numerical range for achieving the desired defective product index rate without relying on the operator's experience, which makes it easier to control wasteful costs incurred by, for example, performing post-processing on defective products.

[0039] In this embodiment, when the numerical range is set as described above, the molding system SM1 may calculate the non-defective product index rate corresponding to the numerical range based on past data. The calculated non-defective product index rate may be used, for example, to verify the validity of the set value of the defective product index rate. For example, if the non-defective product content rate (the proportion of non-defective products among molded products in an abnormal state) is too high, it can be determined that the set value of the defective product index rate is too strict (for example, the proportion of defective products among molded products in a normal state (defective content rate) is too low).

[0040] (Details of molding system) As already described, the molding system SM1 includes, for example, the die-casting machine 1, the removal device 21, the conveyor 23, the defective product box 25, and the post-processing device 29. In addition to these, the molding system SM1 also includes, for example, a marker 27 that marks the molded product 107, and a support device 31 that communicates with the die-casting machine 1.

[0041] The marking links the molding cycle with the molded product, and in turn links the measured values ​​of the physical quantities in the molding cycle with the result of the pass / fail judgment of the molded product. The support device 31 is configured, for example, to include a computer, and is responsible for storing past data, calculating a numerical range based on a set value of the defective product index rate, and calculating the pass / fail product index rate based on the numerical range.

[0042] The molding system SM1 may be defined as including only the die-casting machine 1 and the support device 31. In other words, devices other than the die-casting machine 1 and the support device 31 may be regarded as devices separate from the molding system SM1. For convenience, in the description of this embodiment, the molding system SM1 is basically expressed as including the above-mentioned other devices.

[0043] Here, the molding system SM1 will be roughly described in the following order. Die-casting machine 1 (Figures 1 and 2) The removal device 21, the conveyor 23, and the defective product box 25 (Fig. 2) Marker 27 (Figure 2) Post-processing equipment 29 (Fig. 2) Support device 31 (Figure 2) Physical quantities, numerical ranges and sensors - Judging the quality of molded products Configuration of the signal processing system of the molding system SM1 (Figs. 3 to 6) Example of past data structure (Figure 7) Operation of the molding system SM1 (Fig. 3 to Fig. 6) -Example of how to set the numerical range (Figures 8 and 9) Example of how to select the type of physical quantity to be judged (Fig. 10) Summary of embodiments

[0044] (die casting machine) As described above, the die-casting machine 1 shown in FIGS. 1 and 2 injects an unhardened metal material into the space (including the cavity Ca) defined by the die 101. The unhardened state is, for example, a liquid state or a solid-liquid coexistence state. The solid-liquid coexistence state is a semi-solidified state in which solidification has progressed from a liquid state, or a semi-molten state in which melting has progressed from a solid state. The metal is, for example, an aluminum alloy, a zinc alloy, or a magnesium alloy. Note that, hereinafter, some expressions will be made assuming that the unhardened metal material is a molten metal (liquid metal material).

[0045] As described above, the mold 101 includes, for example, the fixed mold 103 and the movable mold 105. In the drawings of the present disclosure, for convenience, the cross section of the fixed mold 103 or the movable mold 105 is shown with one type of hatching. However, each mold may be a direct-carving type formed as a single unit, or may be a nested type formed by fitting a nest into a main mold. Furthermore, the mold 101 may have a fixed core fixed to the fixed mold 103 or the movable mold 105, and / or a movable core sandwiched between the fixed mold 103 and the movable mold 105.

[0046] The die casting machine 1 includes, for example, a machine body 3 that performs various mechanical operations, and a control unit 5 that controls the machine body 3.

[0047] The machine body 3 has, for example, a mold clamping device 7 that opens, closes, and clamps the mold 101, an injection device 9 that injects molten metal into the mold 101, and an extrusion device 11 (FIG. 1) that extrudes the die-cast product from a fixed mold 103 or a movable mold 105 (movable mold 105 in FIG. 1). The configuration and operation of the machine body 3 (for example, the mold clamping device 7, injection device 9, and extrusion device 11) may be in various forms and may be of a known configuration.

[0048] For example, the mold clamping unit 7 may use a toggle mechanism to open / close and clamp the mold (as shown in the example), or it may not have a toggle mechanism. In the latter embodiment, mold opening / closing and mold clamping may be performed by separate drive sources. Also, for example, the drive system of the mold clamping unit 7 may be electric, hydraulic (hydraulic), or a hybrid system that combines these.

[0049] The injection device 9 may be, for example, for a cold chamber machine (example of FIG. 1), for a hot chamber machine, or a hybrid type that combines both. Also, for example, the drive system of the injection device 9 may be an electric system, a hydraulic system (hydraulic system), or a hybrid system that combines these systems.

[0050] The extrusion device 11 may be, for example, one that extrudes a molded product from a movable mold 105 (the example in FIG. 1), or one that extrudes a molded product from a fixed mold 103. Furthermore, for example, the extrusion device 11 may be one that has an electric or hydraulic (hydraulic) drive source, or one that uses mold opening by the mold clamping device 7 (one that does not have a drive source).

[0051] The control unit 5 includes, for example, a control unit 13 that performs various calculations and outputs control commands, an operation unit 15 that accepts input operations from the operator, and a display unit 17 that visually presents information to the operator. In FIGS. 1 and 2, an interface unit (reference numeral omitted) including the operation unit 15 and the display unit 17 is shown as the control unit 5. The control unit 5 may include a control panel (not shown) installed separately from the interface unit. The interface unit may be located at any position. For example, the interface unit may be provided on the fixed die plate (reference numeral omitted) of the mold clamping unit 7 (in the illustrated example), or may be provided on a control panel installed separately from the machine main body 3.

[0052] The control unit 13 may be configured, for example, by a computer. The computer may include, for example, a central processing unit (CPU), read only memory (ROM), random access memory (RAM), and an external storage device (not shown). The CPU executes programs stored in the ROM and / or the external storage device to configure various functional units that perform various calculations. The control unit 13 may also include a logic circuit that performs only certain processes. The control unit 13 may be configured in an appropriately divided or distributed manner. For example, the control unit 13 may include a lower-level control unit for each of the mold clamping unit 7, the injection unit 9, and the extrusion unit 11, and a higher-level control unit that controls, for example, synchronization of the multiple lower-level control units.

[0053] The operation unit 15 and the display unit 17 may have various configurations, for example, known configurations. For example, the operation unit 15 may be configured to include a touch panel that also serves as the display unit 17, and a mechanical switch (reference numeral omitted). Furthermore, for example, the display unit 17 may include a display (for example, a liquid crystal display or an organic EL display) that displays any two-dimensional image. Furthermore, the display unit 17 may have a lamp (for example, an LED) that presents information depending on the lighting state.

[0054] The display unit 17 is an example of a notification unit that notifies the operator. The die casting machine 1 may have, as a notification unit other than the display unit 17, for example, a unit that presents information acoustically (for example, a speaker).

[0055] (Removal device, conveyor and defective box) The removal device 21 (FIG. 2) removes the molded product from the die 101 in synchronization with the molding cycle, for example, based on a signal from the die-casting machine 1. In other words, the removal device 21 contributes to automating the removal of the molded product. However, unlike this embodiment, the removal device 21 may operate based on the operation of an operator, or the molded product may be removed manually without the removal device 21 being provided.

[0056] The configuration of the take-out device 21 may be various, for example, it may be the same as a known configuration. For example, the take-out device 21 grasps a biscuit (not shown) among the molded products and transports the molded product 107. The take-out device 21 may be a device configured exclusively for removing molded products, or may be a general-purpose articulated robot.

[0057] Unlike the illustrated example, sorting of normal molded products from abnormal molded products may be performed by a device other than the take-out device 21. For example, the molded products taken out by the take-out device 21 may be uniformly transported to a conveyor or box. A sorting robot may then pick up and remove the abnormal molded products from the conveyor or box, or pick up the normal molded products and the abnormal molded products and transport them to different locations. Alternatively, a movable partition on the conveyor may guide the abnormal molded products to a location different from the destination of the normal molded products.

[0058] The conveyor 23 and the defective product box 25 are merely examples of destinations for the molded products. The destinations may be different from those shown in the drawings. For example, unlike the illustrated example, a box for carrying non-defective products and a defective product box 25 may be provided, or a conveyor for carrying non-defective products and a conveyor for carrying defective products may be provided. The conveyor and box may have various configurations, and may be similar to known configurations, for example.

[0059] In the illustrated example, the conveyor 23 is used not only as a sorting destination for molded products in normal conditions, but also to automatically transport molded products in normal conditions to the marker 27 and / or post-processing equipment 29. Unlike the illustrated example, this automatic transport may be performed by a device other than a conveyor. For example, a general-purpose robot may pick up and transport molded products in normal conditions from the conveyor 23 or a box. Furthermore, molded products in normal conditions may not be transported automatically, but may be transported by a conveyor or robot operating at a timing corresponding to an operator's operation, or may be transported manually.

[0060] The operation of automatically transporting normal molded products to the marker 27 and / or the post-processing device 29 may be performed as one aspect of sorting, rather than as an operation after sorting. For example, a robot (not shown) may pick up normal molded products from all molded products (a plurality of molded products in which normal molded products and abnormal molded products are mixed) and transport them to the marker 27 and / or the post-processing device 29.

[0061] The device (conveyor 23 or a robot not shown) that automatically transports the molded product to the marker 27 may transport the molded product to the marker 27 in synchronization with the molding cycle based on a signal from the die-casting machine 1, similar to the removal device 21. In this case, the time difference between the time when each molding cycle is performed and the time when the molded product produced by each molding cycle is transported to the marker 27 is constant. This time difference may be used to identify the molded product marked at the marker 27 and the molding cycle that produced the molded product from the other.

[0062] (marker) The mark formed by the marker 27 on the molded product is, for example, composed of an arrangement of letters, numbers, and / or symbols (hereinafter referred to as a "character string") and indicates information that can link each molded product to each molding cycle. This information may be, for example, a character string arbitrarily assigned to each molding cycle, or the time when each molding cycle was performed. For convenience, this information will be referred to as "identification information" below. The marker 27 may also be used to form information other than identification information.

[0063] The marker 27 may automatically mark the molded product in synchronization with the molding cycle, for example, based on a signal from the die-casting machine 1. However, the marker 27 may also mark based on the operation of an operator. Also, marking can be performed manually without using the marker 27.

[0064] Marker 27 may have the same configuration as various markers used in various technical fields. For example, marker 27 may be a stamping machine that carves a mark (forms a concave or convex shape) on a molded product, or a printing machine that applies paint to a molded product. Furthermore, the stamping machine may be, for example, a stamping machine that applies pressure to a molded product, or a laser stamping machine that irradiates a molded product with laser light.

[0065] 2, the marker 27 marks the molded product 107 being transported on the conveyor 23. However, as can be understood from the above description of the conveyor 23 and the robot that replaces it, the configuration of the marker 27 is not limited to this. For example, the robot may place the molded product on a table held by the marker 27, and mark the molded product on the table.

[0066] If it is possible to link the molding cycle and the pass / fail judgment result for the same molded product, the marker 27 may be omitted. For example, if the length of time from the molding cycle to the pass / fail judgment is constant, linking is possible without marking. However, in the description of this embodiment, there are some expressions assuming that marking is used.

[0067] (post-process equipment) Examples of the post-processing performed by the post-processing device 29 have already been described. The illustrated post-processing device 29 may be regarded as an example of one of multiple post-processing devices corresponding to various post-processing steps, or as a schematic block diagram of all such multiple post-processing devices as a single post-processing device. The configuration of the post-processing device 29 may be various, for example, similar to a known configuration. The post-processing device 29 may automatically perform the post-processing on the molded product transferred from the die-casting machine 1, or may perform the post-processing at a timing in response to an operator's operation (i.e., not automatically). In the former embodiment, the post-processing device 29 may perform the post-processing in synchronization with the molding cycle based on a signal from the die-casting machine 1, or may perform the post-processing asynchronously with the molding cycle.

[0068] In the illustrated example, a post-processing step is performed by a post-processing device 29 after marking by the marker 27. This allows, for example, a link between the molding cycle and the molded product to be subjected to the post-processing step. However, if linking based on such a mark is not necessary, the marking may be performed after the post-processing step. Examples of such cases include a case where linking itself is not necessary for the post-processing step, and a case where linking is possible without a mark because the molding cycle and the post-processing step are synchronized (in other words, the time difference between them is constant). However, for convenience, the description of this embodiment may be based on the assumption that marking is performed before the post-processing step.

[0069] (Support device) The support device 31 may be configured by a computer, for example, as described above. The computer may be configured to include, for example, a CPU, a ROM, a RAM, and an external storage device, although not shown. The CPU executes programs stored in the ROM and / or the external storage device to configure various functional units (a calculation unit 39 described later) that perform various calculations. The support device 31 may also include a logic circuit that performs only certain processing. The support device 31 may be integrated into one location in terms of hardware, or may be distributed and located in multiple locations.

[0070] In relation to the above, one support device 31 may be provided for one die-casting machine 1, or one support device 31 may be provided for multiple die-casting machines 1. From another perspective, the support device 31 may communicate with only one die-casting machine 1, or may communicate with multiple die-casting machines 1. In the latter embodiment, the multiple die-casting machines 1 corresponding to one support device 31 may have the same configuration as each other, or may have different configurations from each other.

[0071] In a mode in which the support device 31 communicates with a plurality of die-casting machines 1, the plurality of die-casting machines 1 may or may not be operated so as to use molds 101 having the same configuration (or, from another perspective, to produce the same type of molded product). Furthermore, the plurality of die-casting machines 1 that communicate with the same support device 31 may be installed in the same factory, or may be installed in different factories.

[0072] The distance between the support device 31 and the die-casting machine 1 is arbitrary. For example, the support device 31 may be disposed adjacent to the die-casting machine 1. Conversely, the support device 31 may be disposed at a location away from the die-casting machine 1 within the factory in which the die-casting machine 1 is installed, or may be disposed at a location separate from the factory in which the die-casting machine 1 is installed. Furthermore, the support device 31 may be provided in a manner that allows it to be regarded as part of a single die-casting machine 1. For example, the support device 31 and the control unit 13 (at least a part thereof) may be housed in the same housing.

[0073] Any configuration may be used for transmitting and receiving signals between the support device 31 and the die-casting machine 1. For example, the support device 31 and the die-casting machine 1 (control unit 13) may be directly connected to each other by a cable, may communicate via short-range wireless communication, or may communicate via a network (e.g., a LAN (Local Area Network) or the Internet). Furthermore, the support device 31 and the control unit 13 (at least a part thereof) may share the same hardware resources (such as a CPU). In other words, unlike the description of this embodiment, the support device 31 and the control unit 13 may not communicate with each other.

[0074] (Physical quantity, numerical range and sensor) Examples of various physical quantities that change as the molding cycle progresses have already been described. The molding system SM1 may set a numerical range for any one or more of the various physical quantities and use the range to sort molded products into normal and abnormal ones. Furthermore, the physical quantities used for sorting may be, for example, those at one or more predetermined points in time within the molding cycle, or may be representative values ​​(e.g., average values), maximum values, or minimum values ​​over part or all of the molding cycle. Even for the same type of physical quantity (or, from another perspective, physical quantities detected by the same sensor), separate numerical ranges for sorting may be set for physical quantities at different points in time. In this case, the physical quantities at different points in time may be regarded as physical quantities of different types.

[0075] Examples of cases where the value of a physical quantity is outside the numerical range corresponding to a normal molded product include cases where the value of the physical quantity is greater than the upper limit of the numerical range and cases where the value of the physical quantity is smaller than the lower limit of the numerical range. Depending on the type of physical quantity, for example, the probability that the value of the physical quantity will exceed the upper limit may be extremely low, or even if the value of the physical quantity increases, the impact on the quality of the molded product may be small. In such cases, the upper limit of the numerical range may not be set, or it may not be necessary to determine whether the value of the physical quantity is greater than the upper limit of the numerical range. While the upper limit has been described, the same applies to the lower limit.

[0076] When two or more physical quantities are used for sorting, the determination result of whether the measured values ​​of the two or more physical quantities are outside the numerical range may be used for sorting as appropriate. For example, if at least one of the measured values ​​of the two or more physical quantities is outside the numerical range, the molded article may be sorted as an abnormal molded article. Furthermore, if the number of types of physical quantities whose measured values ​​are outside the numerical range is equal to or greater than a predetermined number, the molded article may be sorted as an abnormal molded article. In other words, if the number of types of physical quantities whose measured values ​​are outside the numerical range is less than a predetermined number, the molded article may be sorted as a normal molded article. Furthermore, if the measured value of a specific type of physical quantity is outside the numerical range, the molded article may be sorted as an abnormal molded article, while if the number of types of physical quantities whose measured values ​​are outside the numerical range are equal to or greater than a predetermined number, the molded article may be sorted as an abnormal molded article.

[0077] As can be understood from the above explanation, for example, when focusing on one physical quantity and saying "to separate molded articles produced by a molding cycle in which the measured value of the physical quantity is outside the numerical range" from molded articles produced by other molding cycles, the former molded articles may not be separated from the latter molded articles. Therefore, the above can be rephrased as, for example, "to separate molded articles from molded articles produced by other molding cycles, with at least one necessary condition being that they were produced by a molding cycle in which the measured value of the physical quantity is outside the numerical range."

[0078] As can be understood from the description of physical quantities, the molding system SM1 may have various sensors that detect one or more physical quantities. Examples of the types of sensors include temperature sensors, position sensors, speed sensors, acceleration sensors, load sensors, pressure sensors, flow rate sensors, current sensors, and voltage sensors. The configuration of each sensor may be known. Furthermore, one sensor may be used to detect two or more physical quantities. For example, position, speed, and acceleration can be converted into each other by differentiation or integration, and therefore may be detected by one type of sensor. Furthermore, a value calculated from the detected values ​​of two or more sensors may be treated as the detected value of one sensor. That is, two or more sensors may be regarded as one sensor. For example, when statistical values ​​of temperatures detected by two or more temperature sensors are used, the two or more temperature sensors may be regarded as one sensor.

[0079] As can be understood from the explanation of the physical quantities, various sensors may be provided at appropriate positions. For example, various sensors may be provided at various positions in the die 101, the die-casting machine 1, and peripheral equipment of the die-casting machine 1. Examples of peripheral equipment include a supply device (not shown) that supplies molten metal to the die-casting machine 1, a spray device (see FIG. 1; reference numerals omitted) that sprays a release agent onto the die 101, the removal device 21 already described, and a supply device (not shown) that supplies cooling water to the die 101. However, these peripheral equipment may be considered as part of the die-casting machine 1.

[0080] (Judgment of molded product quality) In this embodiment, for example, at least two types of pass / fail judgments are made as follows.

[0081] The first pass / fail judgment is performed on a plurality of molded products, including normal molded products and abnormal molded products. The results of the first pass / fail judgment are used, for example, to calculate a numerical range based on a set value for the defective product index rate, to calculate a pass / fail product index rate based on the numerical range, and to select the type of physical quantity to be judged (described later) (these processes are sometimes referred to as statistical processing). The first pass / fail judgment is performed, for example, on a prototype molded product. From another perspective, the first pass / fail judgment is performed on molded products when a molding cycle is executed while appropriately changing molding conditions, with the aim of, for example, searching for optimal molding conditions.

[0082] The second pass / fail judgment is a pass / fail judgment only for molded products in normal condition after sorting. The result of this pass / fail judgment is used, for example, to determine whether or not the molded products in normal condition can be shipped, and is not used for the above statistical processing. The second pass / fail judgment is performed, for example, on molded products manufactured for the purpose of actual shipping. From another perspective, the second pass / fail judgment is a pass / fail judgment on molded products when a molding cycle is repeated under basically constant molding conditions. However, the molding conditions may be adjusted as appropriate by the operator and / or the control unit 13 during the process of repeating the molding cycle.

[0083] When data including the results of the second pass / fail judgment are added to data including the results of the first pass / fail judgment, for example, data for which the value of the physical quantity is outside the numerical range and the judgment result is "fail" is not added, while data for which the value of the physical quantity is within the numerical range and the judgment result is "fail" is added. As a result, for example, when the set value of the defective index rate is reset, the numerical range corresponding to the set value becomes narrower as more data is added. This ultimately reduces the validity of statistical processing. For this reason, data including the results of the second pass / fail judgment are generally not used in statistical processing. However, data including the results of the second pass / fail judgment may be used in statistical processing based on some kind of inference and processing.

[0084] When the first quality determination is performed, unlike the above description, for example, the take-out device 21 does not sort the molded articles based on the values ​​of the physical quantities, but places all the molded articles on the conveyor 23. The molded articles placed on the conveyor 23 are marked by a marker 27, and then subjected to post-processing by a post-processing device 29. The quality determination of the molded articles is performed, for example, after they are placed on the conveyor 23.

[0085] When the second quality judgment is performed, as explained above, the take-out device 21 sorts the molded products based on the values ​​of the physical quantities. Normal molded products placed on the conveyor 23 are marked by the marker 27, and post-processing is performed by the post-processing device 29. The quality judgment of the molded products is performed after they are placed on the conveyor 23, similar to the first quality judgment.

[0086] As can be understood from the above, the first quality assessment and the second quality assessment may be performed at the same time and by the same method, although the difference is whether or not sorting is performed in the preceding stage. However, the timing and / or method of the two assessments may differ. For example, the first quality assessment may be performed before the subsequent process, while the second quality assessment may be performed after the subsequent process.

[0087] It should be noted that the second pass / fail judgment does not necessarily link the molding cycle with the pass / fail judgment result. Therefore, when the second pass / fail judgment is performed, marking for the purpose of linking as described above does not necessarily have to be performed. Also, the prototype does not necessarily have to undergo post-processing. In other words, when the first pass / fail judgment is performed, post-processing does not necessarily have to be performed.

[0088] In the description of this embodiment, for convenience, the first pass / fail judgment and the second pass / fail judgment may be expressed on the assumption that they are performed in roughly the same way, except for whether or not sorting is performed by the removal device 21 and the specific molding conditions, and no particular distinction may be made between the first pass / fail judgment and the second pass / fail judgment.

[0089] The quality of the molded product may be determined at an appropriate time after the molded product is removed from the mold 101. For example, the quality of the molded product may be determined at an appropriate time during the period from when the molded product is removed from the mold 101 until when it is transported to the marker 27, or while the molded product is located at the marker 27, or while it is located at the subsequent process device 29, or after it is transported from the subsequent process device 29.

[0090] In the description of this embodiment, a mode in which a pass / fail judgment is made after the molded product is transported to the post-processing device 29 is mainly taken as an example. In this case, the device that makes the pass / fail judgment may be regarded as an example of the post-processing device 29. In other words, the post-processing device 29 shown in Fig. 2 may be regarded as an illustrated device that makes the pass / fail judgment. In the following description, the post-processing device 29 may be referred to as a pass / fail judgment device.

[0091] The quality of a molded article may be determined by various methods, including, for example, methods similar to known methods. For example, a computer may determine the quality of a molded article based on an image of the molded article. The image of the molded article may be obtained using visible light or radiation (e.g., X-rays). The image may be a surface image or a tomographic image obtained by computer tomography (CT). For example, the dimensions of the molded article may be measured using a contact or non-contact measuring device, and the computer may determine the quality of the article based on the measured dimensions. The quality determination may be based on whether the dimensional error of the surface shape or the dimensions of voids, etc., fall within a predetermined tolerance range, or may be based on pattern matching or AI (artificial intelligence) technology. The quality determination may also be performed visually.

[0092] A single molded product 107 produced in one molding cycle may contain two or more products. In such cases, the pass / fail judgment may be performed for the molded product 107 as a single unit. However, the pass / fail judgment may also be performed for each of the two or more products contained in one molded product 107. In this case, for example, if at least one product is defective, it may be judged that a defective product has been produced (however, in this case, it may also be considered that the judgment is made on a molded product 107 basis), or the judgment result for each product may be used in statistical processing to calculate a pass / fail index rate or the like as a single judgment result. Note that, for convenience, in the description of this embodiment, expressions may be used assuming that one judgment result is obtained for one molded product 107.

[0093] (Configuration of signal processing system of molding system) Figures 3 to 6 are block diagrams showing the configuration of a signal processing system of the molding system SM1. Figures 3 to 6 also serve to explain the operation of the molding system SM1, and arrows indicating signals between blocks are different from each other. In the explanation here, basically, any of Figures 3 to 6 may be referred to.

[0094] As described above, the molding system SM1 includes the die-casting machine 1, the removal device 21, the marker 27, the post-processing device 29, and the support device 31.

[0095] The die casting machine 1 has, in addition to the control unit 13, operation unit 15, and display unit 17 already described, for example, a sensor 33, a machine-side transmitting unit 35, and a machine-side receiving unit 37. The sensor 33 is a sensor (already described) that detects physical quantities that change with the molding cycle. Here, one of the one or more sensors 33 is shown as a representative. The machine-side transmitting unit 35 transmits signals (from another perspective, information and / or data; the same applies below) to the outside of the die casting machine 1. The machine-side receiving unit 37 receives signals from the outside of the die casting machine 1.

[0096] The communication method between the machine-side transmitter 35 and the machine-side receiver 37 may be any appropriate method, for example, wired and / or wireless. The communication partners of the machine-side transmitter 35 and the machine-side receiver 37 are, for example, the conveyor 23 (not shown), the take-out device 21, the marker 27, the post-process device 29, and the support device 31. As described above, the communication between the machine-side transmitter 35 and the machine-side receiver 37 and the support device 31 may be direct, or may be indirect via a network (for example, a LAN and / or the Internet).

[0097] The support device 31 has, for example, a calculation unit 39 that performs various calculations, a storage unit 41 that stores data, a support side transmission unit 43 that transmits signals to the outside of the support device 31, and a support side reception unit 45 that receives signals from the outside of the support device 31. The storage unit 41 stores, for example, multiple pieces of past data that respectively associate values ​​of physical quantities with pass / fail judgment results. The calculation unit 39 performs, for example, statistical processing based on the multiple pieces of past data stored in the storage unit 41.

[0098] The communication method between the support-side transmitter 43 and the support-side receiver 45 may be any appropriate method, for example, wired and / or wireless. The communication partners of the support-side transmitter 43 and the support-side receiver 45 are, for example, the die-casting machine 1 and the post-process device 29 (good / bad judgment device). As will be understood from the above explanation, the communication between the support-side transmitter 43 and the support-side receiver 45 may be direct, or may be indirect via a network (for example, a LAN and / or the Internet).

[0099] (Example of past data configuration) FIG. 7 is a schematic diagram conceptually showing an example of the configuration of the database DB1 stored in the storage unit 41 of the support device 31. As shown in FIG.

[0100] The database DB1 contains multiple pieces of past data DT1 (represented by multiple rows in the table in the figure). Each piece of past data DT1 contains the values ​​of one or more physical quantities in one molding cycle and information on the pass / fail judgment results for the molded product produced by that one molding cycle. The former are represented by columns labeled "Physical Quantity 1," "Physical Quantity 2," and "Physical Quantity 3." The latter are represented by a column labeled "Judgment Result." The database DB1 is used for statistical processing, such as calculating the numerical range of the physical quantities corresponding to the set value of the defective product index rate, calculating the pass / fail product index rate corresponding to the numerical range, and selecting the physical quantities to be judged (described below).

[0101] The past data DT1 may include identification information. The content of this identification information is, for example, similar to the content of the identification information marked on the molded product by the marker 27. However, since the marked identification information is intended to associate the molding cycle (the value of its physical quantity) with information on the result of the pass / fail judgment of the molded product and store it in the database DB1, it is not essential after storage. Therefore, the past data DT1 does not need to include the content of the marked identification information. Just to be clear, the past data DT1 may include identification information (identification information whose content is unrelated to the content of the marked identification information) for distinguishing multiple past data DT1 from each other in data processing.

[0102] One database DB1 is constructed, for example, when the same type of molded product is repeatedly molded by a single die-casting machine 1. The single database DB1 is used by the single die-casting machine 1. However, the single database DB1 may be constructed when the same type of molded product is molded by a plurality of die-casting machines 1 having the same configuration, and / or may be used when the same type of molded product is formed by a plurality of die-casting machines 1 having the same configuration. From another perspective, the number of die-casting machines 1 used to construct the database DB1 may be the same as or different from the number of die-casting machines 1 that use the database DB1.

[0103] When a single database DB1 is constructed and used by molding the same type of molded product using multiple die-casting machines 1 having the same configuration, the amount of past data DT1 can be increased, reducing variations in calculation results due to a small amount of data. When a database DB1 is constructed and used using a single die-casting machine 1, variations in calculation results due to differences between individual molds 101, differences between individual die-casting machines 1, differences between devices or individual units in the configuration of peripheral equipment for the die-casting machine 1, and differences in the external environment can be reduced. Therefore, either mode may be selected as appropriate.

[0104] Although not particularly shown, the storage unit 41 may have multiple databases DB1. The support device 31 may select a database DB1 according to the type of molded product and / or the type of die-casting machine 1, and store or read (statistically process) the past data DT1 for the selected database DB1.

[0105] More specifically, for example, although not shown, the storage unit 41 may have a database that stores information identifying the database DB1 in association with the database DB1. The information identifying the database DB1 may be, for example, information identifying the type of molded product associated with information identifying the type of die-casting machine 1. Alternatively, the information identifying the database DB1 may be identification information appropriately assigned to each combination of the type of molded product and the type of die-casting machine 1. The support device 31 may then receive both the past data DT1 and the information identifying the database DB1 and store the past data DT1 in the corresponding database DB1, or may receive both a request for statistical processing and the information identifying the database DB1 and perform statistical processing based on the corresponding database DB1.

[0106] However, in the description of this embodiment, for the sake of convenience, the description of the operation of the support device 31 to select one database DB1 from the multiple databases DB1 will basically be omitted, and the support device 31 may be expressed as if it has only one database DB1.

[0107] When multiple prototypes are produced, one database DB1 may include the historical data DT1 for all prototypes or may include the historical data DT1 for only some of the prototypes. As can be understood from the above explanation about the general lack of use of the second pass / fail judgment results in statistical processing, if the physical quantity values ​​in database DB1 are biased toward idiosyncratic values, the validity of the conformance index rate calculated based on database DB1 will be reduced. Therefore, the prototype conditions may be set so that the physical quantity values ​​in database DB1 are distributed over a predetermined range, or the historical data DT1 for constructing database DB1 may be extracted from the historical data DT1 for all prototypes. In this case, the distribution may be an appropriate distribution pattern, such as a uniform distribution or a normal distribution.

[0108] (Modification system operation) The operation of the molding system SM1 can be divided into the following steps, for example: In the following explanation, the operation of the molding system SM1 will be roughly explained in the following order. - Accumulating past data DT1 -Setting the range of values ​​for sorting based on past data DT1 -Sorting based on a set range of values - Operation to assist in setting the numerical range (operation to calculate the non-defective index rate)

[0109] (Accumulation of past data) Fig. 3 shows a schematic diagram of the operation of storing the past data DT1. Fig. 3 may be regarded as showing the operation when the first pass / fail judgment (a pass / fail judgment for a molded product in a normal state and a molded product in an abnormal state) is performed, or from another perspective, as showing the operation when a prototype is being molded.

[0110] When the die-casting machine 1 performs a molding cycle, physical quantities related to the molding cycle are detected, as shown by the arrow from the sensor 33 to the control unit 13. Information DT5 of the measured values ​​of these physical quantities is transmitted from the machine-side transmitting unit 35 and received by the support-side receiving unit 45 of the support device 31. At this time, the information DT5 of the measured values ​​of the physical quantities is accompanied by identification information DT7 including information specifying the molding cycle in which the measured values ​​included in the information DT5 were obtained.

[0111] The identification information DT7 is information for identifying the molding cycle described above for the marker 27. For example, as described above, the identification information DT7 may be a character string arbitrarily assigned to the molding cycle by the control unit 13, or information on the time when the molding cycle was performed. The identification information DT7 may be generated by the control unit 13, for example, and transmitted to the marker 27 via the machine-side transmission unit 35.

[0112] The marker 27, having received the identification information DT7, marks the molded product with the identification information DT7. At this time, appropriate measures may be taken so that the molding cycle indicated by the identification information DT7 matches the molding cycle used to mold the molded product marked with the identification information DT7. For example, as already mentioned, the take-out device 21 and conveyor 23 (and marker 27) may operate in synchronization with the die-casting machine 1 based on a signal from the die-casting machine 1, and transport the molded product to the marker 27 when a certain time has elapsed since the molding cycle. Then, the marker 27 may mark the identification information DT7 on the molded product transported to the marker 27 when a certain time has elapsed since receiving the identification information DT7.

[0113] The molded product marked with the identification information DT7 by the marker 27 is judged to be good or bad by a quality judgment device which is an example or part of the post-processing device 29. Then, information DT9 of the judgment result is received by the support-side receiving unit 45 of the support device 31, with the identification information DT7 attached. The post-processing device 29 may read the identification information DT7 marked on the molded product, for example, by an imager, although this is not particularly shown.

[0114] As described above, the support-side receiving unit 45 of the support device 31 receives the information DT5 of the measurement value of the physical quantity, to which the identification information DT7 is attached, and the information DT9 of the pass / fail judgment result, to which the identification information DT7 is attached. Then, the calculation unit 39 associates the information DT5 and the information DT9, to which the same identification information DT7 is attached (generating past data DT1), and adds the information to the database DB1. In this way, the past data DT1 is accumulated.

[0115] (Setting the numerical range) FIG. 4 shows a schematic diagram of the operation for setting the range of values ​​used for sorting.

[0116] As shown by the arrow from the operation unit 15 to the control unit 13 in the die-casting machine 1, for example, the control unit 13 accepts input of a set value for the defective index rate via the operation unit 15. That is, the operator inputs an arbitrary value as the set value for the defective index rate. Then, information DT11 on the input set value is transmitted from the machine-side transmitter 35 of the die-casting machine 1 to the support-side receiver 45 of the support device 31, and is eventually acquired by the calculation unit 39.

[0117] Unlike the illustrated example, the set value of the defective index rate may be input to the control unit 13 from an external input device (e.g., a personal computer; the same applies below) via the machine-side receiving unit 37. Alternatively, the set value of the defective index rate may be input via an operation unit (not shown) possessed by the support device 31, or input to the support-side receiving unit 45 from an external input device. In other words, the set value of the defective index rate may be acquired by the calculation unit 39 without going through the die-casting machine 1.

[0118] The calculation unit 39 identifies a numerical range that realizes the acquired set value of the defective product index rate by referring to the database DB1 stored in the storage unit 41. Then, information DT13 of the identified numerical range is transmitted from the support-side transmitter 43 to the machine-side receiver 37 of the die-casting machine 1, and is then acquired by the control unit 13.

[0119] (Sorting) FIG. 5 shows a schematic diagram of the sorting operation.

[0120] As described with reference to Fig. 4, the control unit 13 holds the numerical range information DT13. As shown by the arrow from the sensor 33 to the control unit 13, the control unit 13 acquires a measured value of the physical quantity for each molding cycle. Then, the control unit 13 determines whether the acquired measured value of the physical quantity is outside the numerical range for each molding cycle, and outputs a different signal to the take-out device 21 depending on the determination result. Then, the take-out device 21 performs sorting depending on the received signal.

[0121] As already mentioned, a further determination as to whether the molded article is normal or abnormal may be made based on the respective determination results of the plurality of types of physical quantities, and this determination may also be made by the control unit 13. The signal that differs depending on the determination result as to whether the measured value of the above physical quantity is outside the numerical range may be interpreted as a signal indicating whether the molded article is normal or abnormal.

[0122] (Calculation of non-defective index rate) 6 is a diagram illustrating the operation of calculating the non-defective index rate, and also illustrates a part of the operation illustrated in FIG.

[0123] As described with reference to FIG. 4 , the setting value of the defective index rate (information DT11 thereof) input to the operation unit 15 of the die-casting machine 1 is acquired by the calculation unit 39 of the support device 31 via the machine-side transmission unit 35 and the support-side reception unit 45. As described above, the calculation unit 39 calculates a numerical range for realizing the setting value of the defective index rate. Furthermore, the calculation unit 39 calculates the value of the non-defective index rate based on the calculated numerical range and the database DB1 stored in the memory unit 41. Information DT15 on the calculated value of the non-defective index rate is transmitted from the support-side transmission unit 43 to the machine-side reception unit 37 and is then acquired by the control unit 13. As indicated by the arrow from the control unit 13 to the display unit 17, the value of the non-defective index rate is displayed on, for example, the display unit 17.

[0124] Although not shown, in addition to displaying the value of the non-defective product index rate calculated as described above, control unit 13 may cause display unit 17 to display a message inquiring whether or not sorting based on the input setting value of the defective product index rate is permitted. Then, control unit 13 may accept, via operation unit 15, an operation to approve sorting based on the setting value of the defective product index rate and an operation to reset the setting value of the defective product index rate. When the latter operation is performed, the setting of the numerical range and the calculation of the non-defective product index rate may be performed again, as described with reference to FIGS. 4 and 6.

[0125] As the information DT15 relating to the value of the non-defective index rate, the support device 31 may transmit information on the rank of the non-defective index rate value when the value is ranked, or may transmit information on the result of a judgment on the appropriateness of the set value of the defective index rate based on the value of the non-defective index rate, instead of or in addition to the information on the value of the non-defective index rate itself. Alternatively, the control unit 13 may perform the above-mentioned ranking or judgment on the appropriateness based on the received value of the non-defective index rate. Then, the display unit 17 may display the above-mentioned rank or judgment result instead of or in addition to the value of the non-defective index rate itself.

[0126] (Example of how to set the numerical range) There may be various specific procedures for specifying the numerical range that realizes the set value of the defect index rate, and an example of the specific procedure will be described with reference to FIGS.

[0127] First, a reference value indicated by the line RL is set. The reference value is, for example, a representative value (e.g., average or median) of the physical quantity of the non-defective data. Next, upper and lower limit values ​​are calculated that have the same difference d1 (focusing only on the absolute value) from the reference value and that can achieve the set value of the defective index rate.

[0128] The method for calculating the upper and lower limits that can achieve the set value of the defective product index rate may be any appropriate method. For example, the upper and lower limits that can achieve the set value of the defective product index rate may be calculated by repeatedly setting temporary upper and lower limits and calculating the defective product index rate while decreasing or increasing the difference d1. Alternatively, for example, the values ​​of the physical quantities of defective products may be sorted in descending order of the difference d3 from a reference value, and the difference d3 between the difference d3 in the order corresponding to the set value of the defective product detection rate (the proportion of defective products in abnormal conditions to defective products in normal and abnormal conditions) and the difference d3 in the next order may be specified as the difference d1 from the reference value of the upper and lower limits.

[0129] For example, if the difference d3 in the order corresponding to the set value of the defective product detection rate is the same as the difference d3 in the next order, this difference d3 may be used as the difference d1 from the reference values ​​of the upper and lower limits, or the difference d3 in the order following the difference d3 may be used as the difference d1 from the reference values ​​of the upper and lower limits. In this way, the upper or lower limit value that realizes the set value of the defective product index rate may have an error corresponding to a small number of samples (one or more).

[0130] The above procedure may be modified as appropriate. For example, the reference value may be calculated based on the physical quantity values ​​of defective products or all molded products, rather than based on the physical quantity values ​​of non-defective products. The upper and lower limits may be specified without calculating the reference value. For example, a temporary numerical range having a temporary width (2 × d1) may be set, and the temporary numerical range may be moved along the vertical axis PQ to determine whether or not a position in the temporary numerical range that can achieve the set value for the defective product index rate exists. This process may be repeated while decreasing or increasing the width (2 × d1).

[0131] There are multiple numerical ranges that can achieve the same defective product index rate. For example, in Figure 9, the two defective products (N9 and N8) with large physical quantities are excluded to achieve a 40% defect detection rate. However, the defective product (N9) with the largest physical quantity and the defective product (N2) with the smallest physical quantity can be excluded to achieve a 40% defect detection rate, or the two defective products (N2 and N3) with the smallest physical quantities can be excluded to achieve a 40% defect detection rate. Depending on the method for specifying the numerical range, multiple numerical ranges may be specified. In such cases, the method for specifying the numerical range to be actually used for sorting from multiple numerical ranges may be appropriate. For example, the non-defective product content (the proportion of non-defective products among abnormal molded products) for each numerical range may be calculated, and the numerical range with the smallest non-defective product content may be selected.

[0132] (Example of how to select the type of physical quantity to be judged) The type of physical quantity used for sorting may be selected appropriately.

[0133] For example, the types of physical quantities used for sorting may be selected by the manufacturer of the molding system SM1. From another perspective, information specifying the types of physical quantities used for sorting may be stored in advance in the ROM or external storage device of the die-casting machine 1 and / or the support device 31. Also, for example, the types of physical quantities used for sorting may be selected by an operator of the die-casting machine 1. From another perspective, information specifying the types of physical quantities used for sorting may be input via the operation unit 15 or the like and stored in the external storage device of the die-casting machine 1 and / or the support device 31.

[0134] Furthermore, for example, the type of physical quantity used for sorting may be selected by the control unit 13 of the die-casting machine 1 or the calculation unit 39 of the support device 31 based on the database DB1. In this case, the control unit 13 or the calculation unit 39 may select the type of physical quantity according to a predetermined algorithm, or may select the type of physical quantity using AI technology. An example of the procedure when the calculation unit 39 selects the type of physical quantity according to a predetermined algorithm is shown below.

[0135] 10 is a flowchart showing an example of a procedure for selecting the type of physical quantity (hereinafter, sometimes referred to as "determination target") to be used in the sorting performed by the calculation unit 39. This process may be performed at any time after the past data DT1 is accumulated and the database DB1 is constructed, but before the sorting operation using the database DB1 is performed.

[0136] For ease of explanation, Figure 10 uses the defective product detection rate (the proportion of defective products in abnormal conditions among all defective products) as an example of the defective product index rate, and the non-defective product content rate (the proportion of non-defective products in abnormal conditions among all molded products) as an example of the non-defective product index rate. In the following explanation, terms such as "large" or "small" in these explanations may be interpreted as the opposite, such as "small" or "large," depending on the type of index rate.

[0137] In step ST1, the calculation unit 39 selects an arbitrary physical quantity from among the plurality of types of physical quantities. The plurality of types of physical quantities are measured by the die-casting machine 1, and the measurement values ​​are included in the past data DT1.

[0138] In step ST2, the calculation unit 39 sets a tentative value (virtual value) as a setting value for the defective product index rate. This virtual value may be set in advance by, for example, the manufacturer of the support device 31, or may be set by the operator of the die-casting machine 1. In other words, it may be stored in advance in the ROM of the support device 31 or an external storage device, or may be input via the operation unit 15. Furthermore, a virtual value may be set for each type of physical quantity.

[0139] In step ST3, the calculation unit 39 refers to the database DB1 to identify a numerical range that realizes the above-mentioned provisional setting value, and calculates the non-defective index rate when the identified numerical range is used. The calculation procedure at this time may be the same as the above-mentioned procedure for calculating the non-defective index rate from the setting value of the defective index rate.

[0140] In step ST4, the calculation unit 39 determines whether the calculated non-defective product content rate is smaller than a predetermined threshold value. The threshold value may be set in advance by, for example, the manufacturer of the support device 31, or may be set by the operator of the die-casting machine 1. In other words, the threshold value may be stored in advance in the ROM of the support device 31 or an external storage device, or may be input via the operation unit 15. Furthermore, the threshold value may be set for each type of physical quantity.

[0141] Here, the reason why the non-defective content rate is high may be that the setting value of the defective index rate is too strict (in other words, the setting value of the defective product detection rate is too high, or the numerical range corresponding to the setting value is too narrow), or that the correlation between the physical quantity and the quality of the molded product is low. Therefore, for example, when the provisional setting value of the defective product detection rate set in step ST2 is set to an appropriately low value, if the non-defective content rate in step ST4 is equal to or greater than the threshold value, this means that there is a low correlation between the currently selected physical quantity (the physical quantity selected in step ST1) and the quality of the molded product.

[0142] Therefore, if the determination in step ST4 is affirmative, the calculation unit 39 proceeds to step ST5 and designates the currently selected physical quantity as the physical quantity to be determined (the physical quantity to be used for sorting). On the other hand, if the determination in step ST4 is negative, the calculation unit 39 skips step ST5.

[0143] In step ST6, the calculation unit 39 determines whether or not the processing of steps ST1 to ST5 has been performed for all physical quantities. If the determination is negative, the calculation unit 39 returns to step ST1 and performs the same processing for the other physical quantities. If the determination is positive, the calculation unit 39 ends the processing.

[0144] 3, information on the type of physical quantity specified in step ST5 may be transmitted as designation information DT17 from the support-side transmitter 43 of the support device 31 to the machine-side receiver 37 of the die-casting machine 1. Then, the control unit 13 may perform sorting using the measured values ​​of the type of physical quantity specified by the designation information DT17. Furthermore, when accepting input of the set value of the defective index rate via the operation unit 15, the control unit 13 may present the type of physical quantity specified by the designation information DT17 via the display unit 17, and accept input of the set value of the defective index rate only for the presented type of physical quantity.

[0145] The specific magnitudes of the provisionally set value of the defective product index rate in step ST2 and the threshold value of the non-defective product index rate in step ST4 may be set as appropriate. For example, from the perspective of excluding physical quantities with extremely low correlation, the provisionally set value of the defective product detection rate may be set to 50%, and the threshold value of the non-defective product content rate may be set to 50%. This is because if half of the defective products are excluded as abnormal molded products, and half of the abnormal molded products are non-defective, then it cannot be said that the physical quantity is appropriate for use in sorting. For example, from the perspective of selecting physical quantities with a higher correlation, the provisionally set value of the defective product detection rate may be set to 90%, and the threshold value of the non-defective product content rate may be set to 1%.

[0146] 10, the value of the pass / fail index rate is checked for only one provisional setting value (step ST2) for one type of physical quantity. However, the value of the pass / fail index rate may be checked for multiple provisional setting values. In this case, for example, a provisional setting value that makes the non-defective content rate less than a threshold may be identified from multiple provisional setting values ​​for the defective product detection rate, and when the identified provisional setting value is equal to or greater than a predetermined allowable value, the physical quantity may be designated as the physical quantity for sorting. Alternatively, when the absolute value of the rate of decrease in the non-defective content rate increases with an increase in the provisional setting value, the physical quantity may be designated as the physical quantity for sorting.

[0147] (Summary of the embodiment) As described above, the molding machine (die-casting machine 1) according to this embodiment includes a machine main body 3, a sensor 33, a set value receiving unit (operation unit 15), a machine-side transmitter 35, a machine-side receiver 37, and a sorting unit (control unit 13). The machine main body 3 repeatedly performs a molding cycle, filling a cavity Ca with molding material to produce a molded product. The sensor 33 measures physical quantities related to the molding cycle. The operation unit 15 receives a set value for the defective product index rate. The defective product index rate can be defined as the percentage of defective products contained in molded products produced in a molding cycle in which the measured value of a physical quantity falls outside a predetermined range, when multiple (i.e., any predetermined number) molding cycles (which do not have to be consecutive molding cycles) are performed, or a percentage correlated to that percentage, among the molded products produced in the multiple molding cycles. The machine-side transmitter 35 transmits the set value for the defective product index rate input to the operation unit 15 to the support device 31. The machine-side transmitting unit 35 receives from the support device 31 information on the numerical range corresponding to the set value of the defective product index rate transmitted by the machine-side transmitting unit 35. The control unit 13 performs processing to sort molded products produced by a molding cycle in which the measurement value of the physical quantity measured by the sensor 33 is outside the numerical range received by the machine-side receiving unit 37, from molded products produced by other molding cycles, targeting multiple molded products produced by the machine main body 3 through repeated molding cycles.

[0148] From another perspective, the support device 31 according to this embodiment includes a memory unit 41, a support receiving unit 45, a calculation unit 39, and a support transmitting unit 43. The memory unit 41 stores multiple (i.e., any predetermined number) sets of past data DT1. Each set of past data DT1 includes measurement values ​​of physical quantities related to a molding cycle that fills a cavity Ca with molding material to produce a molded product, and information on the determination result indicating whether the molded product produced by the molding cycle in which the measurement value was obtained is a pass or fail product. The support receiving unit 45 receives a set value of the defect index rate from the molding machine (die-casting machine 1). The defect index rate can be defined as the proportion of the multiple sets of past data DT1 whose measurement values ​​fall outside a predetermined numerical range and whose determination result is a fail product, or a proportion correlated to that proportion. The calculation unit 39 identifies a numerical range within which the defect index rate for the multiple sets of past data DT1 stored in the memory unit 41 falls within the set value of the defect index rate received by the support receiving unit 45. The support side transmitting unit 43 transmits the information DT13 of the numerical range specified by the calculation unit 39 to the die casting machine 1.

[0149] From another perspective, the molding system SM1 includes a machine main body 3, a sensor 33, a memory unit 41, a set value receiving unit (operation unit 15), a calculation unit 39, and a sorting unit (control unit 13 and / or take-out device 21). The machine main body 3 repeats a molding cycle in which molding material is filled into a cavity Ca to produce a molded product. The sensor 33 measures physical quantities related to the molding cycle. The memory unit 41 stores multiple pieces of past data DT1. Each piece of past data DT1 includes a measured value of the physical quantity and information on the determination result of whether the molded product produced by the molding cycle in which the measured value was obtained is a good product or a defective product. The operation unit 15 accepts input of a set value for the defective product index rate. The defective product index rate can be defined as the proportion of defective molded products produced by a molding cycle in which the measured value of a physical quantity falls outside a predetermined numerical range, or a proportion correlated to that proportion, when multiple molding cycles are performed. The calculation unit 39 specifies a numerical range in which the defective product index rate for the plurality of past data DT1 stored in the memory unit 41 becomes the set value input to the operation unit 15. The control unit 13 and / or the take-out device 21 performs a process of sorting, for the plurality of molded products produced by the machine body 3 through repeated molding cycles, the molded products produced by a molding cycle in which the measurement value of the physical quantity measured by the sensor 33 is outside the numerical range specified by the calculation unit 39, from the molded products produced by other molding cycles.

[0150] Therefore, for example, as mentioned above, it is possible to set a numerical range that achieves a desired defective product index rate without relying on the experience of the operator, which in turn makes it easier to control unnecessary costs that arise from performing post-processing on defective products.

[0151] In the die-casting machine 1, the machine-side receiving unit 37 may receive from the support device 31 information DT15 relating to the value of the non-defective index rate corresponding to the numerical range corresponding to the set value of the defective index rate transmitted by the machine-side transmitting unit 35. The non-defective index rate can be defined as the proportion of non-defective products among molded products produced by a molding cycle in which the measured value of the physical quantity falls outside the numerical range, or a proportion correlated to that proportion. The die-casting machine 1 may further include a notification unit that notifies an operator of the information DT15 relating to the non-defective index rate received by the machine-side receiving unit 37.

[0152] From another perspective, in the support device 31, the calculation unit 39 may use a numerical range determined based on the set value of the defective index rate received by the support-side receiving unit 45 to determine the value of the non-defective index rate for the multiple past data DT1 stored in the memory unit 41. The non-defective index rate can be defined as the proportion of past data DT1 whose judgment result is non-defective, to the past data DT1 whose measured value of the physical quantity is outside the numerical range, or a proportion correlating with that proportion. The support-side transmitting unit 43 may transmit information DT15 related to the non-defective index rate value determined by the calculation unit 39 to the die-casting machine 1.

[0153] In this case, for example, as described above, it is easier to verify the appropriateness of the set value of the defective index rate. For example, if the non-defective content rate is too high, it can be determined that the set value of the defective index rate is too strict (e.g., the defective content rate is too low). Also, for example, as can be understood from the explanation of FIG. 10, the operator can also consider whether the types of physical quantities used for sorting are appropriate. In response to such effects, as described above, the die casting machine 1 may be able to accept selection of the types of physical quantities used for sorting via the operation unit 15.

[0154] The die-casting machine 1 may have multiple sensors 33 that measure multiple types of physical quantities. The machine-side receiving unit 37 may receive designation information DT17 that designates one or more types of the multiple types of physical quantities. The sorting unit (control unit 13) may sort molded products based on the numerical ranges of only the types of physical quantities designated by the designation information DT17 among the multiple types of physical quantities.

[0155] From another perspective, in the support device 31, the multiple pieces of past data DT1 may each include measurement values ​​of multiple types of physical quantities and information on the pass / fail judgment results of the molding cycle in which the measurement values ​​were obtained. The calculation unit 39 may select one or more types of physical quantities based on the multiple pieces of past data DT1. The support-side transmission unit 43 may transmit, to the die-casting machine 1, designation information DT17 indicating the one or more types selected by the calculation unit 39.

[0156] In this case, for example, the operator does not need to select the type of physical quantity to be used for sorting based on empirical rules. As a result, the burden on the operator is reduced. A situation in which the burden on the operator is reduced includes, for example, when a new type of physical quantity (or, from another perspective, a new sensor) is introduced as a measurement target. Furthermore, the burden on the operator can be reduced even when the type of physical quantity highly correlated with the quality of the molded product varies depending on the type of mold 101, the type of die-casting machine 1, and the operating status of the die-casting machine 1.

[0157] The calculation unit 39 may include, in the one or more types specified by the specification information DT17, the types of physical quantities whose non-defective product index rates corresponding to the numerical range when the defective product index rate is a predetermined virtual value are located on the side where the non-defective product content rate is low relative to a predetermined threshold value.

[0158] In this case, for example, a simple algorithm using the non-defective index rate can be used to determine the correlation between the physical quantity and the quality of the molded product, and the physical quantity to be used for sorting can be selected. Also, in an embodiment where the molding system SM1 has a function to present to the operator the value of the non-defective index rate according to the set value of the defective index rate, part of this function can be used to determine whether the type of physical quantity is appropriate.

[0159] In the above embodiment, the die-casting machine 1 is an example of a molding machine. The metal material is an example of a molding material. The control unit 13, which sends a signal to the take-out device 21 to perform sorting, is an example of a sorting unit that performs the sorting process. Note that the take-out device 21 may also be considered an example of a sorting unit of a molding system.

[0160] The operation unit 15 is an example of a set value receiving unit that receives input of a set value for the defective index rate. The control unit 13, which acquires the set value for the defective index rate via the operation unit 15, may also be considered an example of a set value receiving unit. In the explanation of the operation of setting the numerical range with reference to FIG. 4, it was stated that the set value for the defective index rate may be input without via the operation unit 15. As can be understood from this explanation, the machine-side receiving unit 37, which receives information related to the set value from an external device, may also be considered an example of a set value receiving unit of the die-casting machine. Alternatively, the support-side receiving unit 45, which receives information related to the set value from an external device, or an operation unit (not shown) included in the support device 31 may also be considered an example of a set value receiving unit of the molding system.

[0161] The technology according to the present disclosure is not limited to the above-described embodiments and may be implemented in various forms.

[0162] For example, the molding machine is not limited to a die-casting machine. For example, the molding machine may be another metal molding machine, an injection molding machine for molding resin, or a molding machine for molding a material in which wood powder is mixed with a thermoplastic resin or the like. Furthermore, the molding machine is not limited to a horizontal clamping / horizontal injection molding machine, and may be, for example, a vertical clamping / vertical injection molding machine, a vertical clamping / horizontal injection molding machine, or a horizontal clamping / vertical injection molding machine.

[0163] As mentioned in the embodiment, the molding system does not have to be configured to distinguish between the molding machine and the support device. For example, at least one of the storage unit that stores multiple pieces of past data and the calculation unit that specifies the numerical range, etc., for the multiple pieces of past data may be housed in a housing together with the control unit of the molding machine, or may share hardware resources (CPU, ROM, RAM, and external storage device) with the control unit of the molding machine.

[0164] The division of roles between the die-casting machine and the support device may be different from that in the embodiment. For example, in the embodiment, the die-casting machine determines whether the measured value of the physical quantity is within a numerical range, but the determination may be made by the support device.

[0165] From the present disclosure, an invention may be extracted that does not require setting a numerical range based on a set value of the defective product index rate. For example, an invention may be extracted that focuses on selecting the type of physical quantity to be determined, as shown in Figure 10. In such an invention, the numerical range may be set by an operator based on experience, or the molding system or support device may calculate the numerical range so that the non-defective product content rate is equal to or less than a predetermined set value. [Explanation of symbols]

[0166] 1... die-casting machine (molding machine), 3... machine body, 5... control unit, 13... cooling unit, 15... spray device, 59... temperature sensor (sensor), 101... mold (die).

Claims

1. a machine body that repeats a molding cycle in which a molding material is filled into a cavity to produce a molded product; a sensor that measures a physical quantity related to the molding cycle; a set value receiving unit that receives an input of a set value of the defective product index rate when the ratio of defective products contained in molded products produced by a molding cycle in which the measured value of the physical quantity falls outside a predetermined numerical range is referred to as a defective product index rate, or a rate correlated to said ratio, when a plurality of molding cycles are performed; a machine-side transmitting unit that transmits the setting value input to the setting value receiving unit to an assistance device; a machine-side receiving unit that receives, from the support device, information on the numerical range corresponding to the setting value transmitted by the machine-side transmitting unit; a sorting unit that performs a process of sorting, for a plurality of molded products produced by the machine main body through repetition of the molding cycle, molded products produced by a molding cycle in which the measurement value of the physical quantity measured by the sensor falls outside the numerical range received by the machine-side receiving unit from molded products produced by other molding cycles; It has The defective product index rate is the proportion of defective products among molded products produced by two or more molding cycles in which the measured values ​​of the physical quantities are different from each other within the numerical range; The proportion of defective molded products produced by two or more molding cycles in which the measured values ​​of the physical quantities are different from each other and outside the numerical range, among the molded products produced by the plurality of molding cycles; or the proportion of defective products produced by the plurality of molding cycles that are produced by two or more molding cycles in which the measured values ​​of the physical quantities are different from each other and outside the numerical range, Molding machine.

2. When the proportion of non-defective products among molded products produced by a molding cycle in which the measured value of the physical quantity is outside the numerical range, or a proportion correlated to the proportion, is referred to as a non-defective product index rate, the machine-side receiving unit receives, from the support device, information relating to the value of the conforming product index rate corresponding to the numerical range corresponding to the setting value transmitted by the machine-side transmitting unit; The machine further includes a notification unit that notifies an operator of information related to the value of the non-defective index rate received by the machine-side receiving unit. The molding machine according to claim 1.

3. The non-defective index rate is the proportion of non-defective molded products produced by a molding cycle in which the measured value of the physical quantity falls outside the numerical range; The proportion of non-defective molded products produced by a molding cycle in which the measured value of the physical quantity falls within the numerical range, among the molded products produced by the plurality of molding cycles; or the proportion of conforming products produced by molding cycles in which the measured value of the physical quantity falls within the numerical range, to the conforming products produced by the plurality of molding cycles. The molding machine according to claim 2.

4. a plurality of sensors for measuring a plurality of types of physical quantities; the machine-side receiving unit receives designation information that designates one or more types of the plurality of types of physical quantities; The sorting unit sorts molded products based on the numerical ranges only for the types of physical quantities designated by the designation information among the plurality of types of physical quantities. The molding machine according to any one of claims 1 to 3.

5. a storage unit that stores a plurality of past data items, each including a measurement value of a physical quantity related to a molding cycle that fills a cavity with a molding material to produce a molded product, and information on a determination result as to whether the molded product produced by the molding cycle that obtained the measurement value is a good product or a defective product; a support side receiving unit that receives a set value of the defective product index rate from the molding machine when the proportion of the past data in which the measurement value is outside a predetermined numerical range and the judgment result is a defective product, or a proportion correlated to the proportion, is referred to as a defective product index rate; a calculation unit that specifies the numerical range in which the defective product index rate for the plurality of past data stored in the storage unit falls within the set value received by the support side receiving unit; a support-side transmitting unit that transmits information on the numerical range identified by the calculation unit to the molding machine; It has The defective product index rate is the proportion of past data in which the determination result is a defective product among two or more past data in which the measured value of the physical quantity is a different value within the numerical range; a ratio of past data in which the determination result is a defective product and which includes two or more past data in which the measured values ​​of the physical quantity are different from each other and outside the numerical range, to the plurality of past data; or the ratio of past data in which the judgment result is a defective product, the ratio being the ratio of past data in which the judgment result is a defective product and which includes two or more past data in which the measured values ​​of the physical quantity are different from each other and outside the numerical range. Support equipment for molding machines.

6. When the ratio of past data in which the measurement value is outside the numerical range and the judgment result is a non-defective product, or a ratio correlated to the ratio, is referred to as a non-defective product index rate, the calculation unit uses the numerical range specified based on the setting value received by the support side receiving unit to specify the value of the non-defective product index rate for the plurality of past data stored in the storage unit; The support-side transmitting unit transmits information relating to the value of the non-defective index rate identified by the calculation unit to the molding machine. The support device for a molding machine according to claim 5.

7. the plurality of past data each include measurement values ​​of a plurality of types of the physical quantities and information on the judgment results of the molding cycles in which the measurement values ​​were obtained, the calculation unit selects one or more types of the plurality of types of physical quantities based on the plurality of past data; The support-side transmitting unit transmits, to the molding machine, designation information indicating the one or more types selected by the calculation unit.

7. The support device for a molding machine according to claim 5 or 6.

8. When the proportion of past data in which the measurement value is outside the numerical range and the judgment result is a non-defective product content rate is referred to as the non-defective product content rate or a rate correlated to the non-defective product content rate is referred to as the non-defective product index rate, The calculation unit includes, in the one or more types, a type of physical quantity whose value of the non-defective product index rate corresponding to the numerical range when the defective product index rate is a predetermined virtual value is on the side where the non-defective product content rate is low with respect to a predetermined threshold value. The support device for a molding machine according to claim 7.

9. a machine body that repeats a molding cycle in which a molding material is filled into a cavity to produce a molded product; a sensor that measures a physical quantity related to the molding cycle; a storage unit that stores a plurality of past data each including a measurement value of the physical quantity and information on a determination result as to whether the molded product produced by the molding cycle in which the measurement value was obtained is a good product or a defective product; a set value receiving unit that receives an input of a set value of the defective product index rate when the proportion of defective products contained in molded products produced by a molding cycle in which the measurement value of the physical quantity falls outside a predetermined numerical range, among the molded products produced by the plurality of molding cycles, is referred to as a defective product index rate, or a rate correlated to the proportion; a calculation unit that specifies a numerical range in which the defective product index rate for the plurality of past data stored in the storage unit falls within the set value input to the set value receiving unit; a sorting unit that performs a process of sorting, for a plurality of molded products produced by the machine body through repetition of the molding cycle, molded products produced through a molding cycle in which the measurement value of the physical quantity measured by the sensor falls outside the numerical range specified by the calculation unit, from molded products produced through other molding cycles; It has The defective product index rate is the proportion of defective products among molded products produced by two or more molding cycles in which the measured values ​​of the physical quantities are different from each other within the numerical range; The proportion of defective molded products produced by two or more molding cycles in which the measured values ​​of the physical quantities are different from each other and outside the numerical range, among the molded products produced by the plurality of molding cycles; or the proportion of defective products produced by the plurality of molding cycles that are produced by two or more molding cycles in which the measured values ​​of the physical quantities are different from each other and outside the numerical range, Molding system.

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