Method and device for determining the maximum number of constant tables in a plurality of pick-and-place lines having pick-and-place machines

Abstract

Provided is a method for determining the maximum number of constant tables at table locations in one or more pick-and-place lines for the placement of components on modules using pick-and-place machines, the fittings on the constant tables being used in all the fitting families of a pick-and-place line, one fitting family including a set of modules which can be produced on a pick-and-place line with a common component fitting, wherein the maximum number of constant tables is determined by calculation using mixed-integer liner optimization on the basis of input data describing the pick-and-place infrastructure, wherein the pick-and place infrastructure can include in particular the pick-and-place lines having the pick-and-place machines, each with pick-and-place head, at least one transport system for the modules, the said constant tables, and further tables which can be variably fitted.

Claims

1. A method for determining a maximum number of constant tables at table locations in one or more pick-and-place lines for the placement of components on assemblies with the aid of pick-and-place machines, wherein the fittings on the constant tables are used in all the fitting families of a pick-and-place line, wherein a fitting family comprises a set of assemblies which can be produced on a pick-and-place line with a common component fitting, wherein a total amount of component types required can be accommodated on table locations available to the pick-and-place line, the method comprising: determining the maximum number of constant tables with the aid of mixed integer linear optimization on the basis of input data describing the pick-and-place infrastructure, wherein the pick-and-place infrastructure can comprise in particular the pick-and-place lines having the pick-and-place machines, each having pick-and-place heads, at least one transport system for the assemblies, said constant tables and further tables which can be variably fitted, wherein the following data are used as the input data: set of component types, set of assembly types, set of component types per assembly type, track occupation per component type, maximum track capacity per table, set of tables of a pick-and-place line, and set of permissible component types per table, wherein tables with maximum total track capacity are selected as constant tables.

2. The method as claimed in claim 1, wherein the following parameters are furthermore used as predefinable input parameters for calculating the maximum number of constant tables: the maximum degree of filling for the constant tables; the maximum degree of filling for the tables which can be variably fitted.

3. The method as claimed in claim 1, wherein minimum clusters are predefinable as further input data for calculating the maximum number of constant tables, wherein a minimum cluster comprises a set of assembly types which have to be produced together in a fitting family.

4. A device for determining a maximum number of constant tables at table locations in one or more pick-and-place lines for the placement of components on assemblies with the aid of pick-and-place machines, wherein the fittings on the constant tables are used in all the fitting families of a pick-and-place line, wherein a fitting family comprises a set of assemblies which can be produced on a pick-and-place line with a common component fitting, wherein a total amount of component types required can be accommodated on table locations available to the pick-and-place line, comprising: a pick-and-place infrastructure, which can comprise in particular the pick-and-place lines having the pick-and-place machines, each having pick-and-place heads, at least one transport system for the assemblies, said constant tables and further tables which can be variably fitted, and a calculation unit designed to determine the maximum number of constant tables with the aid of mixed integer linear optimization on the basis of input data describing the pick-and-place infrastructure, wherein the following data are used as the input data: set of component types, set of assembly types, set of component types per assembly type, track occupation per component type, maximum track capacity per table, set of tables of a pick-and-place line, and set of permissible component types per table, wherein tables with maximum total track capacity are selected as constant tables.

5. The device as claimed in claim 4, wherein the following parameters are furthermore usable as predefinable input parameters for calculating the maximum number of constant tables: the maximum degree of filling for the constant tables; the maximum degree of filling for the tables which can be variably fitted.

6. The device as claimed in claim 4, wherein minimum clusters of assembly types are predefinable as further input data for calculating the maximum number of constant tables, wherein a minimum cluster comprises a set of assembly types which are to be produced together in a fitting family.

7. The device as claimed in claim 4, wherein the device is part of a production or mounting line.

8. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method for determining a maximum number of constant tables at table locations in one or more pick-and-place lines for the placement of components on assemblies with the aid of pick-and-place machines, wherein the fittings on the constant tables are used in all the fitting families of a pick-and-place line, wherein a fitting family comprises a set of assemblies which can be produced on a pick-and-place line with a common component fitting, wherein a total amount of component types required can be accommodated on table locations available to the pick-and-place line, the method comprising: determining the maximum number of constant tables with the aid of mixed integer linear optimization on the basis of input data describing the pick-and-place infrastructure, wherein the pick-and-place infrastructure can comprise in particular the pick-and-place lines having the pick-and-place machines, each having pick-and-place heads, at least one transport system for the assemblies, said constant tables and further tables which can be variably fitted, wherein the following data are used as input data: set of component types, set of assembly types, set of component types per assembly type, track occupation per component type, maximum track capacity er table, set of tables of a pick-and-place line, and set of permissible component types per table, wherein tables with maximum total track capacity are selected as constant tables.

Description

BRIEF DESCRIPTION

(1) Some of the embodiments will be described in detail, with references to the following FIGURES, wherein like designations denote like members, wherein:

(2) The FIGURE shows one exemplary configuration of a pick-and-place line having two pick-and-place machines.

DETAILED DESCRIPTION

(3) The pick-and-place machine BA1 consists of four conveying tables KT1, KT2, VT1, VT2, of which two are variable tables VT1, VT2 and two are constant tables KT1, KT2. Furthermore, the pick-and-place machine BA1 consists of four pick-and-place heads BK1-BK4, each of the CP20 type. The pick-and-place heads BK1-BK8 of a pick-and-place machine BA1, BA2 pick up the components from the feed devices ZE1, ZE2 and move them to a pick-and-place region of the pick-and-place machine BA1, BA2, where the assembly to be equipped (e.g. a base board in SMD production) is provided, and places the components on the assembly. The pick-and-place heads BK1-BK8 are usually movable by a positioning system. By way of example, so-called belt feeders can be used as feed devices ZE1, ZE2 for providing the components.

(4) The pick-and-place machine BA2 having two variable tables VT3, VT4 and two constant tables KT3, KT4 is likewise arranged at the transport system TS that provides the base board. Furthermore, the pick-and-place machine BA2 consists of four pick-and-place heads BK5-BK8, each of the CP12 type.

(5) The FIGURE illustrates by way of example that the variable tables VT2 and VT4 have feed devices ZE1 and ZE2, respectively, for providing the components to be mounted (e.g. chips, transistors, etc.).

(6) In SMD production (e.g. for electronic components), pick-and-place machines BA1, BA2 are used for populating printed circuit boards (assemblies), which pick-and-place machines place components from feed devices ZE1, ZE2 onto the printed circuit boards with the aid of a pick-and-place head BK1-BK8. The printed circuit boards are provided at the pick-and-place machines BA1, BA2 by the transport system TS.

(7) Usually (e.g. in electronics production), the batches to be produced on a pick-and-place line are combined in fitting families. All batches of a fitting family are produced in each case with the same line fitting.

(8) Constant tables KT1-KT4 are fixedly set-up and fixedly fitted tables that are identical for all fittings of the fitting families (clusters). It is thus possible to reduce outlay for set-up conversion for the production volume produced with variable fittings and to save fitting equipment.

(9) A variable table VT1-VT4 is understood to mean a shuttle or conveying table which is assigned to a station side of a pick-and-place machine and in which the fittings need not be constant for all fitting families, but rather can vary, i.e. can be altered. Variable tables VT1-VT4 have to be exchanged as necessary. This means outlay for set-up conversion. Furthermore, variable tables VT1-VT4 are kept ready for exchange. This causes a high storage space requirement.

(10) The method for determining the maximum number of constant tables KT1-KT4 is based on the mathematical method of integer linear optimization (integer linear programming) or mixed integer linear optimization (mixed integer linear programming, MIP).

(11) As a mathematical optimization method, a description is given below of (linear) IP models (IP stands for integer programming or for integer program or integer optimization model) and MIP models (mixed integer linear optimization (mixed integer linear programming)) for this problem posed. The use of exact mathematical methods makes it possible to achieve significantly better solutions than with heuristics or optimization methods used hitherto. IP solution approaches have the following advantages: Global optimization approach. Easily extendible. Very good commercial standard solvers (e.g. Ilog, Gurobi) which are widely used and proven in practice. The standard solvers are being constantly improved, and so it should be expected that the entities can be solved even more rapidly in the future.

(12) The maximum number of constant tables in a plurality of pick-and-place lines having pick-and-place machines can be ascertained or determined by a unit that is not illustrated in the FIGURE. Such a unit can be integrated into a pick-and-place machine, e.g. BA1, of the pick-and-place line BL. Said unit can also be implemented or integrated in a computer which is separated from the pick-and-place machines and which controls the pick-and-place machines.

(13) The IP model illustrated should be regarded merely as one possible exemplary formulation and does not constitute a restriction for the method.

(14) The objective criterion is formulated by a linear expression that is to be maximized or minimized.

(15) In the exemplary embodiment, the following input data or input parameters are used in the IP model:

(16) Mixed integer linear optimization models (MIP, mixed integer program) are proposed with which the number of maximum possible constant tables or the maximum total track capacity thereof can be estimated very rapidly and exactly.

(17) The following designations are applicable in the MIP formulation:

(18) TABLE-US-00001 Indices C Set of component types R Set of assembly types R.sub.c Set of assemblies having component type c Parameters Width.sub.c Space occupation of a component type c Cap Line capacity of tracks Binary variables SetupConst.sub.c Variable indicating whether the component type c is fitted on the constant tables. It assumes the value 1 in this case, otherwise the value 0. SetupVar.sub.c, r Variable indicating whether the component type c is fitted in the variable fitting portion for the assembly type r. It assumes the value 1 in this case, otherwise the value 0.

(19) Estimators for the maximum total track capacity of the constant tables:

(20) Continuous Variables:

(21) CapVar.sub.r Track requirement for the variable fitting portion of the assembly type r

(22) CapConst Total track capacity of the constant tables

Maximize CapConst

(23) Under the Condition:

(24) TABLE-US-00002   (1) SetupConst.sub.c + SetupVar.sub.r,c = 1 c∈C,r∈R.sub.c (2) CapConst + CapVar.sub.r ≤ Cap r∈R (3) $\underset{c\in C}{.Math.}{\mathrm{Width}}_c{\mathrm{SetupVar}}_{c,r}\le {\mathrm{CapVar}}_r$ r∈R (4) $\underset{c\in C}{.Math.}{\mathrm{Width}}_c{\mathrm{SetupConst}}_c\le \mathrm{CapConst}$ SetupConst.sub.c ∈ {0, 1} c∈C SetupVar.sub.c,r ∈ {0, 1} c∈C, r∈R.sub.c CapVap.sub.r ≥ 0 r∈R CapConst ≥ 0

(25) Re (1): For an assembly type, all component types contained must be situated either in the constant fitting portion or in the variable fitting portion.

(26) Re (2): The sum of the total track capacities for the constant fitting portion and for a variable fitting portion must not exceed the line capacity.

(27) Re (3): The sum of the track widths of the component types in the variable fitting portion of an assembly type must not exceed the capacity thereof.

(28) Re (4): The sum of the track widths of the component types in the constant fitting portion must not exceed the capacity thereof.

(29) Estimation of the maximum number of constant tables with individual table viewpoint:

(30) Additional Indices:

(31) TABLE-US-00003 T Set of tables of the line. C.sub.t Set of component types which are permitted to be fitted at table t, i.e. component types which can be placed by a head which accesses said table.

(32) Additional Parameters:

(33) TABLE-US-00004 Cap.sub.t Space capacity of table t

(34) Additional Binary Variables:

(35) TABLE-US-00005 IsConst.sub.t Variable indicating whether a table is constant. SetupConst.sub.c, t Variable indicating whether the component type c is fitted on the constant tables t. It assumes the value 1 in this case, otherwise the value 0. SetupVar.sub.c, r, t Variable indicating whether the component type c is fitted in the variable fitting portion for the assembly r on the table t. It assumes the value 1 in this case, otherwise the value 0.

(36) $\mathrm{maximize}\underset{t\in T}{.Math.}{\mathrm{IsConst}}_t$

(37) Under the Condition:

(38) TABLE-US-00006 (1) $\underset{t\in T}{.Math.}{\mathrm{SetupConst}}_{c,t}+{\mathrm{SetupVar}}_{c,r,t}=1$ c∈C, r∈R.sub.c (2) $\underset{c\in C,}{.Math.}{\mathrm{Width}}_c{\mathrm{SetupVar}}_{c,r,t}\le {\mathrm{Cap}}_t$ r∈R, t∈T (3) $\underset{c\in C,}{.Math.}{\mathrm{Width}}_c{\mathrm{SetupConst}}_{c,t}\le {\mathrm{Cap}}_t$ t∈T (4) SetupConst.sub.c,t ≤ IsConst.sub.t c∈C, t∈T (5) SetupVar.sub.c,r,t ≤ (1 − IsConst.sub.t) t∈T (6) SetupConst.sub.c,t = 0 t∈T, c∈C\C.sub.t (7) SetupVar.sub.c,r,t = 0 r∈R, t∈T, c∈C\C.sub.t SetupConst.sub.c,t ∈ {0, 1} t∈T, c∈C SetupVar.sub.c,r,t ∈ {0, 1} r∈R, t∈T, c∈C IsConst.sub.t ∈ {0, 1} t∈T

(39) Re (1): For an assembly type, all component types contained must be situated either on a constant table or a variable table.

(40) Re (2): The sum of the track widths of the component types on a variable table must not exceed the capacity thereof.

(41) Re (3): The sum of the track widths of the component types on a constant table must not exceed the capacity thereof.

(42) Re (4): Only constantly fitted component types are permitted on constant tables.

(43) Re (5): Only variably fitted component types are permitted on variable tables.

(44) Re (6), (7): Only component types permissible on tables are permitted to be fitted there.

(45) The following supplementary configurations are possible: Taking account of degrees of filling of the variable and constant tables. Instead of the assembly types, minimum clusters of assembly types can also be used (e.g. the top side and underside of an assembly, subset of the assemblies which is to be assigned to the same cluster). The estimators can also be applied to “finished” or “partly finished” clusterings. In this regard, e.g. a cluster method having the combined targets “small number of clusters” and “maximum number of constant tables” is conceivable. Maximum degree of filling of a fixed fitting (expressed in percent), i.e. the space occupation of the component types of the fixed fitting is permitted to be only this percentage of the entire line capacity.

(46) The implementation of the processes or method sequences described above can be effected on the basis of instructions present on computer-readable storage media or in volatile computer memories (referred to in combination hereinafter as computer-readable memories). Computer-readable memories are for example volatile memories such as caches, buffers or RAM and non-volatile memories such as exchangeable data carriers, hard disks, etc.

(47) The functions or steps described above can be present here in the form of at least one instruction set in/on a computer-readable memory. In this case, the functions or steps are not tied to a specific instruction set or to a specific form of instruction sets or to a specific storage medium or to a specific processor or to specific execution schemes and can be implemented by software, firmware, microcode, hardware, processors, integrated circuits, etc. in standalone operation or in arbitrary combination. In this case, a wide variety of processing strategies can be employed, for example serial processing by a single processor or multiprocessing or multitasking or parallel processing, etc.

(48) The instructions can be stored in local memories, but it is also possible to store the instructions on a remote system and access them via a network.

(49) The term “processor”, “central signal processing”, “control unit” or “data evaluation means”, as used here, encompasses processing means in the broadest sense, that is to say for example servers, general purpose processors, graphics processing units, digital signal processors, application-specific integrated circuits (ASICs), programmable logic circuits such as FPGAs, discrete analog or digital circuits and arbitrary combinations thereof, including all other processing means that are known to the person skilled in the art or will be developed in the future. In this case, processors can consist of one or a plurality of devices or facilities or units. If a processor consists of a plurality of devices, the latter can be designed or configured for the parallel or sequential processing or execution of instructions.

(50) Although the invention has been illustrated and described in greater detail with reference to the preferred exemplary embodiment, the invention is not limited to the examples disclosed, and further variations can be inferred by a person skilled in the art, without departing from the scope of protection of the invention.

(51) For the sake of clarity, it is to be understood that the use of “a” or “an” throughout this application does not exclude a plurality, and “comprising” does not exclude other steps or elements.