EMS factories have collected and used machine data for many decades. Over that time, much of the value derived from machine data collection has come from three operational use cases: allowing fewer operators to simultaneously monitor more machines for errors, reducing common operational mistakes through programmatic interlocks, and maintaining traceability records in case of product recalls. There has been significantly less use of machine data for strategic optimization of factory operations, with the notable exception of asset utilization monitoring using simple calculations like Line Utilization and OEE. One of the historical reasons for the absence of large-scale analysis of machine data in the EMS industry has been that it was difficult to interpret machine data absent external context on what intended operation was being performed when the data was collected. More recently however, the advent of big data analysis techniques and machine learning algorithms has largely removed this traditional limitation. In this paper we discuss the difference between tactical and strategic data analysis approaches to the common EMS factory goals of lowering component attrition and increasing line utilization. We show how machine data can provide significant value at the strategic level if it is stored and analyzed in granular detail instead of being pre-aggregated into high level key performance indicators before being analyzed. As the EMS industry looks forward to Industry 4.0, we argue that one of the biggest areas of efficiency gain may come from such strategic data analysis.

Smart Factories require continuous and reliable operation of all equipment. Therefore, equipment maintenance is becoming more and more important. A simple approach for equipment maintenance, such as “let it break – then fix / repair it”, does not fulfill the needs of a continuously and reliably operating Smart Factory: It leads to unexpected, not plannable downtimes of equipment and, thus, impacts production in an unacceptable way. A preventive maintenance approach can avoid this problem of unplanned production downtime due to breaking down machines. A predictive or prescriptive maintenance approach may improve planning of maintenance even further. All these approaches have in common that they need data from operation of equipment. A Smart Factory needs Smart Maintenance, i.e., data driven maintenance.

This presentation provides an overview of an ongoing examination of test methodologies within the ESA, supported by IPC, for the evaluation of stacked and staggered (offset) microvia structures, including air-to-air thermal shock, convection reflow assembly simulation and current induced thermal cycling.

As EMS Providers such as Contract Manufacturers look forward to Industry 4.0, their need for complex data analysis to inform manufacturing decisions takes on even more significance. Managing manufacturing data has become as important to operational excellence as managing the solder paste and electronic components placed on each circuit board. However, while there are many examples of small-scale, operational data collection systems that inform real-time factory operations, there are relatively few examples of large-scale historical machine data collection systems. In this paper we describe one such system and discuss both the advantages it brings as well as the practical implementation challenges to build and maintain it at scale. It is expected that in the years to come many EMS factories will find use for such systems as increasingly proven use cases involving complex machine learning, predictive maintenance and even artificial intelligence become more common in the industry. Each of these advanced Industry 4.0 applications requires as a prerequisite the kind of large, centralized, historical machine dataset that a system like what we describe collects and manages.

The smart factory project for Electronics Manufacturing initiative was developed to create a sandpit to carry out industry 4.0 use cases working with industrial members and partners. The Manufacturing Technology Centre (MTC) based in Coventry UK have carried out a project that outlines the implementation of IPC CFX to demonstrate the connectivity between the machine, the broker and the dashboard. The main objective focuses on the implementation elements for the Adaptor machines and the RabbitMQ server. This includes configuration requirements and user guides around maintaining the delivered implementation.

The paper will review the above example of the successful connecting using IPC CFX, a SMT DEK printer and the Ersa Reflow 10 zone oven which are both classed as legacy machine due to its age and the operating system. The benefits to this is to allow legacy machines used in the electronics manufacturing industry to communicate, transfer operational data for track/trace and monitoring while, in future add-ons, allow them to be agile and have autonomous ability to change parameters based on the live shop floor situation. This will eventually lead to the ‘lights out’ capabilities in the low volume/high mix electronics manufacturing industry.

Why use robotics?

Precision -> improved quality

Consistency -> improved quality

Traceability -> improved quality

Dexterity -> to handle the parts

Robust -> reliability for multi shift operations

Reconfigurable -> flexibility

This paper discusses the automation of inspection using Augmented Reality (AR).   Machine vision is used to find small components since it reduces the amount of time needed for inspection.  Augmented reality overlays the information from the design onto the PCB under the lens.  It then assists the operator in seeing the small components on the board.  The visuals are also uploaded to a network.  Augmented Reality increases speed, quality, and reduced cost.  

This presentation provides an overview of an ongoing examination of test methodologies within the ESA, supported by IPC, for the evaluation of stacked and staggered (offset) microvia structures, including air-to-air thermal shock, convection reflow assembly simulation and current induced thermal cycling.

IPC/IMEC/ESA Microvia TV CITC Outline

 CITC Coupon and Test Plan Overview

 CITC Test Introduction

 Temperature Coefficient of Resistance (TCR) Test and Results

 Relative Life of the 3 HDI microvia structures provided

 CITC Life Curves, Nf vs Temperature

 Reliability Calculations by type and temperature