Development of Test Automation Framework for Printed Circuit Board Assembly

A Test Automation Framework is a bunch of rules or coding guidelines for test-process handling that is followed during coding. These are simple strategies that produce helpful results such as improved code re-usability, higher portability, diminished script support cost, reduced interdependency, higher interchangeability, and so on. This paper presents the development of a test automation framework for Printed Circuit Board Assembly (PCBA) to test the functionality and fault identification of the electronic circuit in the PCBA. It provides an integrated array of test and measurement Instruments which is automated using SCPI (Standard Command for programming Instrument) based commands to control test and measurement processes, making it simpler to configure and query. These test instruments imitate the environment in which the PCBA is to be deployed, which helps to observe its characteristics to determine whether it meets the expectation for which it was designed. Any possible variations in its characteristics due to fault are observed during testing and those faults are identified automatically using the data-driven fault analysis method.

Locate faulty components by IR based Direct Current Injection method with Analog Signature Analysis

This article describes a method to integrate Analog Signature Analysis (ASA) into IR based Direct Current Inject method (IRDCI) for Printed Circuit Board Assembly failure analysis, which extends IRDCI application from diagnostic shorted power rails to any measurement locations that show signature differences. Also, it extends the application of component failure modes from electrical short to breakdown or degradation that can be identified by signature comparison and still keep high efficiency to eliminate the needs to guess and remove suspected faulty components one by one from the board to validate.

Model updating of a temperature field simulation of a printed circuit board assembly based on the Kriging model

Purpose The simulated temperature profile of the printed circuit board assembly (PCBA) during reflow soldering process deviates from the actual profile. To reduce this relative deviation, a new strategy based on the Kriging response surface and the Multi-Objective Genetic Algorithm (MOGA) optimizing method is proposed. Design/methodology/approach The simulated temperature profile of the PCBA during reflow soldering process deviates from the actual profile. To reduce this relative deviation, a new strategy based on the Kriging response surface and the MOGA optimizing method is proposed. Findings Several critical influencing parameters such as temperature and the convective heat transfer coefficient of the specific temperature zones are selected as the correction parameters. The hyper Latins sampling method is implemented to distribute the design points, and the Kriging response surface model of the soldering process is constructed. The updated model is achieved and validated by the test. The relative derivation is reduced from the initial value of 43.4%–11.8% in terms of the time above the liquidus line. Originality/value A new strategy based on the Kriging response surface and the MOGA optimizing method is proposed.

Developing Predictive Engineering Analytics to Formulate the Closed-Loop Management for Achieving Re-Industrialisation

Regarding the process of printed circuit board assembly (PCBA), existing failure location methods are reactive in nature, while process parameters and performance cannot be predicted to achieve a high level of operational excellence. Designated PCB designs are not customized for specific manufacturing sites, while process performance becomes uncertain to clients and manufacturers. In this paper, an intelligent manufacturing performance predictive framework (IMPPF) is proposed in this paper, which structures the predictive engineering analytics for the smart manufacturing. First, the data collection from the PCBA process is structured by means of multi-responses Taguchi method, which guarantees the data reliability and quality. Second, the artificial neural network is adopted to learn from the existing operational data so as to provide the prediction on machine settings and process performance at the Gerber drawing stage. The contribution of this study is mainly to establish a closed-loop framework to facilitate the predictive engineering analytics for achieving re-industrialization.

A Robot Learning from Demonstration Framework for Skillful Small Parts Assembly

Increasing demand for higher production flexibility and smaller production batch size pushes the development of manufacturing expertise towards robotic solutions with fast setup and reprogram capability. Aiming to facilitate assembly lines with robots, the learning from demonstration (LfD) paradigm has attracted attention. A robot LfD framework designed for skillful small parts assembly applications is developed, which takes position, orientation and wrench demonstration data into consideration while utilizes impedance control to deal with the motion error. In view of constraints in industrial assembly applications, we propose a robot LfD framework where policy learning is carried out with separated assembly demonstration data to avoid potential under-fitting problem. With the proposed assembly policies, reference orientation and wrench trajectories are generated as well as coupled with the position data to boost their generalization and robust performance. Effectiveness of the proposed LfD framework is validated by a printed circuit board assembly experiment with a 7-DOF torque-controlled robot.