Although surface-mount assembly involves great precision and reliability, things can go wrong when operating continuously at high speed. In the event of errors and exceptions, full-speed operation needs to be restored as quickly as possible.
Whenever the line is stopped, the business incurs costs that reduce profitability. These costs include not only the direct cost of lost production, but also labour costs while the line is stopped, the cost of overtime and rush orders to catch up after the line is restored, and the cost of any rework or items scrapped due to the problem that caused the stoppage. There are costs associate with inventory waiting, and the disruption to the normal flow of operations can cause inefficiencies. Moreover, there can be impact on customer commitments that can result in financial penalties.
Today’s assembly equipment is loaded with sensors to detect issues such as blocked nozzles or pickup misalignment. Automatic inspection can be performed at multiple points in the line, including after screen printing, component placement, and reflow, to assist troubleshooting in real time.
Software tools are relied upon to help manage production equipment, detect any exceptions and defects, and provide information to production supervisors. These include dashboard applications that provide an instant appraisal of equipment and production status. There are also value-added tools such as Yamaha’s QA Options and the closely related Mobile Judgement app. Mobile Judgement can send details of any detected defects, directly to the supervisor’s smartphone to help assess the severity of the problem.
The Human-Machine Interface
These applications represent the place where machines and humans interact to solve the problems that inevitably arise during the normal course of a shift. The team in charge must apply skills to deliver the required quantity of each batch, and ensure that production runs to schedule.
This approach can be dependent on the quality of the tools provided to visualise the data captured from the various locations throughout the line. Identifying the causes of faults and applying a fix, quickly, also depends on the skills and knowledge of the supervisor staff-members present in the factory.
The latest advancements in data science can help to eliminate dependence on individual judgement by automatically identifying the causes of any defects or other production problems. Guiding operators directly to these causes permits more consistent production performance. As an example of this emerging generation of software tools, the latest Yamaha YSUP Analytics Dashboard contains advanced tools that use statistical techniques to identify most likely causes of detected defects. It also recommends suitable corrective actions.
Figure 1. Dashboard visualisation tools help assess process performance.
The new YSUP Analytics Dashboard presents information in an easy-to-read format that can be interpreted quickly. Ultimately, this enables every working shift to deliver consistently high production throughput.
Based on a relational database with built-in querying capabilities that analyse data in situ, the software identifies probable causes of recorded exceptions in near real-time using statistical techniques. The automatic analysis capabilities of the new dashboard application enable any authorised user to extract the information they need, analyse errors, and view corrective actions at any time.
Yamaha’s latest data-driven dashboard tools provide extra assistance to identify and fix the causes of errors, to help overcome the limitations of operators’ knowledge and shorten the time to restore operation, ultimately enabling further improvement of productivity.
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