A Traditional workstation lacks the capability to ergonomically adjust itself, perform time study of the worker, automatically mark attendance, reduce learning curve and display data about worker performance in real time due to these lack of capabilities, a traditional workstation results in increas
Smart Manufacturing Workstation
A Traditional workstation lacks the capability to ergonomically adjust itself, perform time study of the worker, automatically mark attendance, reduce learning curve and display data about worker performance in real time due to these lack of capabilities, a traditional workstation results in increased fatigue on worker and require additional human resource to be hired to perform time study, mark attendance and teach the new worker about the assembly process, this results in reduced worker’s performance, unnecessary hiring of Human resource and increased nonproductive time therefore this project aims to counter these problems by developing smart work station 2.0 based on the concepts of industry 4.0 by using AI and IOT as tools. The Targets of this project are to reduce the learning curve, stress on workers body by adjusting the height of table to their ergonomical needs, monitor worker and to conduct time studies to monitor worker’s KPIs.
This project uses python as its programming language and employ computer vision and machine learning to register worker’s attendance and suggest the workstation’s height to meet worker ergonomics need. The combination of mechanical devices, sensors, logic and programming will also be used to adjust height of the table, perform time study of the worker, monitor workers performance through camera, track the inventory of the components and indicate which component to pick from the bins thereby reducing the learning curve of the worker. Furthermore, we will also be fabricating a table. Our project will mainly target assembly industry but its applications are also in other industries.
The methodology of the project involves the following phrases;
The purpose of this project is to create a smart workstation 2.0 that allows managers to manage their staff from their office and workers to rapidly learn their jobs. This workstation will have a self-adjusting height mechanism and component identification system to aid workers and decrease the learning curve. Because artificial intelligence and the Internet of Things are ideal tools for revolutionizing the industrial sector, as a consequence, our team made the decision to hire them for this project. The smart workstation 2.0 will help sectors where items are manufactured manually by tracking worker performance using IoT and AI, decreasing management effort.
The Smart Workstation also involves a self-actuating system through which the table would automatically be aligned according to the height of the worker resulting in less worker fatigue and more efficient outcomes. This task would be accomplished by fabricating a metallic table (Base Iron) with concentric legs and an actuator with appropriate power between the legs of the table so that whenever a worker would enter the workstation, the sensor will detect his height and the actuator will lift the table accordingly.
The Workstation will display the cycle time of the product being assembled by the worker to the manager whenever and wherever he requires by using live cloud-app data. The cycle time would be calculated by the means of sensors as the time taken by the worker to pick up the first part of subassembly to sending the finished assembly to the next station. These tasks would be achieved by using tools of IIOT, the hands of the worker will be detected whenever he will perform any task within the workstation. This data will be transferred to the process manager by the means of cloud sharing where the KPIs of the worker, his attendance and the Cycle time of the process would be shared lively. This objective would be attained by using microprocessors (Arduino mega) along with further IOT appliances like esp8322, IR and US sensors etc.
Attendance system will be accompanied by using Camera Module through which the attendance of the workers will be taken via face detection in which Machine Learning will be thoroughly used. The Program for such system has already been developed by our team which is to be fed into the CPU (Raspberry-Pi 4) for accomplishment of the project.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Raspberry Pi 4 | Equipment | 1 | 24000 | 24000 |
| Actuator | Equipment | 2 | 6000 | 12000 |
| Power Supply | Equipment | 1 | 2000 | 2000 |
| Mild Steel Rods | Equipment | 5 | 1800 | 9000 |
| Ultra sonic Sensor | Equipment | 1 | 300 | 300 |
| PIR sensor | Equipment | 4 | 150 | 600 |
| Buzzer | Equipment | 4 | 120 | 480 |
| Cutting Disk | Miscellaneous | 2 | 200 | 400 |
| Welding Rod | Miscellaneous | 2 | 350 | 700 |
| Printing | Miscellaneous | 3 | 1500 | 4500 |
| Baskets | Equipment | 5 | 150 | 750 |
| Transportation | Miscellaneous | 1 | 1000 | 1000 |
| HDMI | Equipment | 1 | 450 | 450 |
| Heat sink | Equipment | 1 | 200 | 200 |
| web cam | Equipment | 1 | 2000 | 2000 |
| Soldering Iron | Equipment | 1 | 500 | 500 |
| Relay Module | Equipment | 1 | 200 | 200 |
| wires | Equipment | 1 | 1000 | 1000 |
| Display Monitor | Equipment | 1 | 5000 | 5000 |
| Total in (Rs) | 65080 |
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