Neural network based deep learning models increasingly demonstrates high accuracy in object detection and image classification in digital image processing. The manufacturing industry is adapting this advanced technology to assist in automated quality assurance. Successful in implementing prototypes and small-scale deployment to employ AI models for quality inspection has been achieved. AI-assisted quality inspection significantly improves inspection accuracy, operation throughput and efficiency. “A Framework for Large-Scale AI-Assisted Quality Inspection Implementation in Manufacturing Using Edge Computing” [1] was previously presented, in which details are discussed highlighting challenges in large-scale deployment of AI models for quality inspection operation and focused on IT architectural decisions to fulfill the OT requirement, including user experience in the quality inspection ecosystem.

This paper focuses on AI model benchmarking at the edge, with respect to the architecture presented in [1]. It discusses the technical challenges to meet specific inference performance requirement at the edge. Benchmarking study of various AI models on a set of edge hardware including Nvidia Jetson TX2 and IBM Power servers are performed and recommendations on AI model and edge hardware selection is presented.

Keyword: Quality Inspection, AI Models Benchmarking, Edge Computing

Photonic soldering utilizes high intensity flashes of visible light to achieve wide area heating with exceptional uniformity. Solder paste is heated to its liquidus temperature using radiative energy transfer, and light is converted to heat through optical absorption. This process can be made selective by exploiting the high absorptivity of solder pastes relative to most other printed circuit board (PCB) materials, or with the aid of shadow masks. The optical flash can be modulated digitally, with high temporal resolution, which enables highly customizable processing flows ranging from traditional to highly innovative.

Photonic soldering is compatible with standard high temperature lead free solder alloys (e.g., SAC305) in combination with temperature-sensitive substrates (e.g., PET). The nonequilibrium nature of the heating process enables thermal isolation of active regions from temperature sensitive regions. The resulting flexibility in material selection gives designers significant freedom and new options in outlining device architectures.

Previous presentations of this technology focused on the quality of junctions formed through this process. This paper focuses on the unique features of the photonic soldering process, as they relate to production line design and operation. The main advantages of the process are rapid change of process conditions with limited hysteresis combined with short dwell time and high throughput of the system. Together, these unique advantages enable a fresh approach to tool setup and timing, which better meets the needs of next generation electronics. This paper highlights the advantages of the new technology and discusses the application space for the photonic soldering technology.

These innovations enable product designers to combine components, substrates and solder alloys that are not feasible with reflow ovens while allowing very high volume – and high throughput – manufacturing processes in a digital format.

IPC-HERMES-9852 as the smart replacement for the long-used IPC-SMEMA-9851 provides machine-to-machine (M2M) communication that ensures consistency of each PCB and its individual data while traveling down an SMT Line in production.  Thus, Hermes enables machines to consistently transfer a PCB together with its Digital Twin. This Digital Twin alone already provides valuable support for basic reporting functionality, such as monitoring and traceability reporting. But this data together with the M2M communication can do much more: It can be used for further automating certain workflows of a flexible production, bringing a cost-effective solution for automated mixed production.

In this presentation, we look at some advanced workflow examples in an automated flexible production, which can be easily automated using M2M communication provided by IPC-HERMES-9852, including:

•Automated machine program selection based on PCB related data such as barcode, product name, work order ID, etc.

•Control of the oven error loop to prevent PCBs from entering the oven while the buffer after is full and cannot take anymore PCBs from the oven

•Coordination of the interaction between AOI and Flipping Unit to allow inspection of top and bottom side of a PCB

With the advancement of emerging technologies such as AI, Cloud and Block Chain, the electronics manufacturing industry is entering a new era of smart manufacturing. More and more Electronics Manufacturing Service providers (EMS) are investing in data and deep AI capabilities as part of their smart factory effort to improve production efficiency, process capability and quality. These data and deep AI capabilities are often implemented through enterprise hybrid clouds to achieve high availability, high scalability and low IT operational cost. 

This paper discusses current status and trends of smart manufacturing implementation in the EMS industry, specifically focusing on quality management, as there are plenty of use cases of data and AI in quality management that are good candidates for smart factory implementation. It elaborates details with examples of several quality management use cases involving data, AI and enterprise integration. In this paper, we also discuss the current maturity level and future trending and challenges in technology adoption and integration for smart factory in EMS industry.

Keyword: Quality Management, Data, Smart Factory, EMS

The Factory of the Future or a Lights out Factory will require significant automation, including many functions that are challenging and are still performed manually due to the complexity and human dexterity needed. One such example is SMT screen printer changeover process, which has been one of the most labor-intensive tasks running a SMT production line. This paper will focus on the application requirements of an automated changeover process including the need for progressive steps towards full automation. The specific changeover actions reviewed here will include solder paste cartridges, the solder paste bead, support tooling requirements, squeegees, stencils, and a necessary delivery system for the printer along with the traceability and verification systems to support an automated printer changeover. In addition, general solder management will be discussed as this becomes a potential issue when an operator is removed from the changeover process. An automated changeover will allow for fast and consistent changeovers, reduced operator requirements and errors, resulting in a higher machine utilization and yield.

Key words: Factory of the Future, Smart Factory, Automation, Robotics, Industry 4.0

The IPC Connected Factory Exchange (IPC-CFX) standard provides genuine plug-and-play IIoT data exchange between machines and supervisory systems across the shop floor. To realise the benefit of IPC-CFX, however, the whole value chain of production stations should be considered, as any missing link becomes a blind-spot for even the most basic visibility and control.

The smart factory project for Electronics Manufacturing initiative was developed to create a sandbox 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 reviews 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 are 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.

Further the paper introduces and details the CFX standards-based open-hardware interface project, which is designed to enable existing machines that cannot support CFX natively through software alone, to become part of the CFX communication infrastructure. We explore use-cases ranging from the building of “home-grown” boxes by end-users through to pre-built solutions provided by original machine vendors, and we discuss how this impacts Smart Factory Industry 4.0 realization, the additional opportunity for machine vendors, as well as how the standards-based approach with open-source community development and support works to the mutual benefit of all those in the industry.

With the Industry 4.0 transformation going full steam ahead, analytics have transformed the industry from being process-driven to now data-driven, where data speaks for itself. Conventionally, only failed measurement data are logged for repair and troubleshooting purposes. Now, all measurements regardless of whether they are pass or fail, are recorded for further advanced analytics like anomaly detection and predictive analysis.

These data type includes Meta Data, Measurement Data and Environmental Data. All these big chunks of granular data alone provide great analytics potential, but even greater potential when these data are analyzed collectively with correlation. For example: higher temperature will cause resistance measurement to be slightly higher as well.

In this presentation, we will be using PathWave Manufacturing Analytics (PMA) as an example to discuss this topic on “Single Source of Truth.” With the data agnostic nature of PMA, data are transferred to PMA seamlessly where data analytics are performed at diverse levels and perspective, with several different analytics results. Different users ranging from Original Equipment Manufacturers (OEM) monitoring multiple Contract Manufacturers (CM); Factory managers overseeing factory operation; Engineers handling In-Circuit Test or Functional station to Technicians maintaining system uptime, can tap on data analytics results from this “Single Source of Truth.”

There is no ‘data’ ambiguity between the stakeholders as it all comes from a single source. When low First Pass Yield (FPY) is observed by OEM, the factory manager sees the same information as the rest. Engineers can be triggered to review and drill down to the next level analytics to identify root causes such as false failure, degradation anomaly (situation where probes are worn-out and affecting the measurement) and the degraded probes can be easily replaced by the manufacturing stuffs with the aid from the Probe Heatmap (a graphically view of the probes locations).

“Data does not lie.” With the actual data comes together collectively, uncovering insights and identify actual opportunities to improve manufacturing efficiencies and product quality in a brief time becomes a reality.