A pressing apparatus 1 includes a storage 27 that stores a plurality of actual data which are load data which was actually measured and which are configured of a first information and a second information, and a plurality of reference data which is a reference for the load data, an adjustor 23 that expands and contracts the first information of at least one of an actual data pattern which is based on the plurality of the actual data stored in the storage 27 and an reference data pattern based on the plurality of the reference data stored in the storage 27 to correspond with each other, and a determiner 24 that determines appropriateness of a load applied to the ram 9 in a pressing operation based on a similarity of the actual data pattern adjusted by the adjustor 23 and the reference data pattern adjusted by the adjustor 23.

A press machine includes: a learning-model generating unit that uses one data from among data collected from sensors, as an objective variable, and uses data other than the one data as an explanatory variable to perform machine learning to generate a learning model for the one data, the generation being performed for all the data; a predicted-value calculating unit that inputs an actually measured value of data other than one data from among the data collected from the sensors, into the learning model for the one data to calculate a predicted value of the one data, the calculation being performed for all the data; a degree-of-abnormality calculating unit that calculates a degree of abnormality based on a difference between an actually measured value and a predicted value of the data; and a degree-of-abnormality outputting unit that outputs the calculated degree of abnormality.

A press machine includes a display that displays a plurality of images, and a processor that generates a plurality of images corresponding to a slide operation and a movement operation. The plurality of images include a first image having an annular shape, a second image having an arc shape, and a third image. A first region of the second image represents a movement time required for the movement operation, a third region represents a stop time required for a slide stop process, and a second region represents a delay time until the slide stop process starts after the movement time elapses. The processor displays the third image on the display as a determination element for determining whether the slide operation and the movement operation are settable in a relationship between the slide operation and the movement operation based on an input from the user interface.

A press system includes a press portion and a controller configured to control the press portion. The controller includes a load pattern generator, an evaluation input unit, and an optimum load pattern setting unit configured to set an optimum load pattern based on a plurality of evaluations of the quality input by the evaluation input unit. The optimum load pattern setting unit includes a response surface generator configured to create from evaluation value data based on the plurality of load patterns and the plurality of evaluations of the quality, by using as an objective function, the evaluation value data with the load pattern being defined as a design variable, a response surface of the objective function, and an optimization calculator configured to find an optimum solution of the objective function of the response surface by optimization.

A press and a process for operating it, has an upper tool and a lower tool for shaping a workpiece and a work unit including a press drive and/or a drawing device, is controllable by a controller, to perform the shaping process. The controller outputs, to the work unit, press process values, which define the sequence of events of the shaping process. Workpiece parameters characterize, for example, the material and/or the shape and/or at least one dimension and/or quality of the workpiece, which still has to be shaped or has already been shaped. The controller is assigned a composition matrix which contains, for each defined workpiece parameter and for each defined press process value, an individually changeable function, to describe the relationship between every one of the defined workpiece parameters and the defined press process values. This composition matrix allows a quick and rapid adjustment and adaptation of the press.

A pressing apparatus 1 includes a storage 27 that stores a plurality of actual data which are load data which was actually measured and which are configured of a first information and a second information, and a plurality of reference data which is a reference for the load data, an adjustor 23 that expands and contracts the first information of at least one of an actual data pattern which is based on the plurality of the actual data stored in the storage 27 and an reference data pattern based on the plurality of the reference data stored in the storage 27 to correspond with each other, and a determiner 24 that determines appropriateness of a load applied to the ram 9 in a pressing operation based on a similarity of the actual data pattern adjusted by the adjustor 23 and the reference data pattern adjusted by the adjustor 23.

Control method for a press installation and installation, where the installation includes a servo press, a conveyor device for conveying pieces from a position of origin to the servo press, and a controller. The controller controls the movements of the conveyor device and of the servo press by means of one and the same master signal, causing a synchronized actuation of both. In the method, the pieces are conveyed to the position of origin and during the travel thereof until reaching the position of origin, the passage of the pieces through a predetermined position of reference before the position of origin is detected, and depending on the detection, the master signal is adjusted for adapting the actuation speed of the servo press.

A technique for optimizing metal extrusion process parameters includes receiving values representing properties of an extrusion press machine, and calculating an estimated surface exit temperature of a metal work product resulting from an extrusion of a metal billet using the extrusion press machine based on the machine property values, an initial temperature of the metal billet prior to the extrusion, an extrusion force applied to the metal billet during the extrusion, and an extrusion speed of the metal work product. The estimated surface exit temperature of the metal work product is compared with a target hot shortness exit temperature of the metal work product. The initial temperature of the metal billet, the extrusion speed, and the extrusion force are changed based on the comparison until the estimated surface exit temperature equals the target hot shortness exit temperature.

A press machine includes: a learning-model generating unit that uses one data from among data collected from sensors, as an objective variable, and uses data other than the one data as an explanatory variable to perform machine learning to generate a learning model for the one data, the generation being performed for all the data; a predicted-value calculating unit that inputs an actually measured value of data other than one data from among the data collected from the sensors, into the learning model for the one data to calculate a predicted value of the one data, the calculation being performed for all the data; a degree-of-abnormality calculating unit that calculates a degree of abnormality based on a difference between an actually measured value and a predicted value of the data; and a degree-of-abnormality outputting unit that outputs the calculated degree of abnormality.

A press system includes a press portion and a controller configured to control the press portion. The controller includes a load pattern generator, an evaluation input unit, and an optimum load pattern setting unit configured to set an optimum load pattern based on a plurality of evaluations of the quality input by the evaluation input unit. The optimum load pattern setting unit includes a response surface generator configured to create from evaluation value data based on the plurality of load patterns and the plurality of evaluations of the quality, by using as an objective function, the evaluation value data with the load pattern being defined as a design variable, a response surface of the objective function, and an optimization calculator configured to find an optimum solution of the objective function of the response surface by optimization.