A programmable device (D) arranged in a production environment, to assist an operator (O) in performing manual assembly operations carried out by the operator (O), particularly during assembly operations performed on pieces (P) transported by pallets (5) in a production line (1). The device (D) comprises an assembly means usable by the operator (O), a lighting device (4) for lighting a work area in which the operator (O) works, a sensor (6) configured to detect the position of the assembly means, an input device (10) usable by the operator, and an electronic control system (8) configured to memorize a learning sequence including a sequence of manual assembly operations.

A worker guidance system for guiding a worker when carrying out a process using a process system. The process system includes at least one process device having process device capabilities, the process including at least one work step, including a control unit and a communication device. The control unit is designed to activate the communication device based on process data to communicate to the worker the work step to be carried out. The control unit is provided with the process device capabilities as process device capability data, the process data including the process device capability data.

A device for endorsing screwed assemblies, includes a screw gun intended to be handled by an operator, and computer equipment connected to the screw gun, the screw gun being equipped with measurement sensors sending information representative of the torque applied by the screw gun and an angle of rotation applied by the screw gun, the computer equipment storing, in a memory, reference data relating to a screwed assembly to be produced, the computer equipment being configured to compare information representative of the angle and the torque measured during the assembly with the reference data, and to provide a notification as to the conformity of the measurements of the angle and the torque in relation to the reference data.

Aspects of the disclosed technology provide a computational model that utilizes machine learning for detecting errors during a manual assembly process and determining a sequence of steps to complete the manual assembly process in order to mitigate the detected errors. In some implementations, the disclosed technology evaluates a target object at a step of an assembly process where an error is detected to a nominal object to obtain a comparison. Based on this comparison, a sequence of steps for completion of the assembly process of the target object is obtained. The assembly instructions for creating the target object are adjusted based on this sequence of steps.

A system for controlling a quality of a vehicle includes: a communication device that communicates with a plurality of terminals respectively located on vehicle product lines; and a controller that monitors the part assembly state of the worker, and displays an official assembly video of a defectively assembled part through a terminal on a product line where the worker is located when detecting the defective assembly of the worker.

A task assistance device assists a worker who performs tasks by a predetermined task procedure, the tasks including: a first task with a first driver that is state-switchable between usable and unusable; and a second task performed by the worker without the first driver. The task assistance device includes: a communicator that obtains a start signal indicating a start and an end signal indicating an end of each task; a generator that generates a first control signal that makes the first driver usable when the communicator obtains the start signal of the first task and/or the end signal of the second task, and generates a second control signal that makes the first driver unusable when the communicator obtains at least one of the end signal of the first task or the start signal of the second task; and a communicator that outputs the first control signal and the second control signal generated by the generator to the first driver.

The work instruction system includes: a computer configured to generate a work instruction screen; and a display device connected to the computer and configured to display the work instruction screen. The work instruction screen includes a first display region in which a three-dimensional model used for describing an assembly work of an object is displayed based on three-dimensional design data, a second display region 320 in which at least a part of attribute information of the three-dimensional design data is displayed, and a third display region in which when predetermined component information registered in advance is included in the attribute information, related information associated with the predetermined component information is displayed. In the first display region, when the object is a wiring line, the three-dimensional model is displayed for allowing a first predetermined operation.

In an operation management system in the related art, it is necessary to add an identification mark such as an RFID tag to each component to be managed and prepare a fastening tool having an antenna. In contrast, a mechanical fastening unit management method is provided using an augmented reality space generated by superimposing a virtual space on a real space. In the augmented reality space where a real fastening unit (RBn) and a virtual fastening unit (IBn) are in a one-to-one correspondence, information that the real fastening unit is selected as a fastening target is acquired with a camera or the like, and analyzed by an augmented reality server connected to the camera. Accordingly, it is possible to easily record that a predetermined operation is progressing as scheduled, and to provide a high quality mechanical fastening unit management method or system that is low in operation cost.

A monitoring and inspection system for a work area includes a non-visual sensory detection sensor, such as a microphone or vibration detection sensor, and a processor. The sensor is configured to sense sounds or vibrations generated in the work area during the performance of an action that are then received by the processor. The processor analyzes the received acoustic and/or vibrational signals and compares the received signals to an expected signal to verify that the identified acoustic and/or vibration signature of the detected signal is an acoustic and/or vibration signature associated with the operational step that was performed to confirm that the operational step has been performed, and that it has been performed properly.

Aspects of the disclosed technology provide an Artificial Intelligence Process Control (AIPC) for automatically detecting errors in a manufacturing workflow of an assembly line process, and performing error mitigation through the update of instructions or guidance given to assembly operators at various stations. In some implementations, the disclosed technology utilizes one or more machine-learning models to perform error detection and/or propagate instructions/assembly modifications necessary to rectify detected errors or to improve the product of manufacture.