Known as the “mother of electronic products,” the printed circuit board (PCB) is an important carrier in the electronics industry, playing a role in supporting and interconnecting electronic devices. PCBs can be used in various fields such as consumer electronics, automotive electronics, network communication, industrial control and medical, aerospace, etc. For example, they are found in devices like smartphones, home appliances, drones, VR equipment, GPS navigation systems, car audio, car dashboards, industrial computers, inverters, measuring instruments, and medical displays. The development level of the PCB industry reflects the speed and technology level of a country’s or region’s electronic industry to some extent. With the increasing functionality of consumer electronic products, the demand for circuit boards to accommodate more components has risen, making existing circuit boards inadequate to meet spatial requirements. At the same time, the pressure of global market competition is increasing, and the lifecycle of electronic products is getting shorter. Additionally, the demographic dividend in our country is reaching its tipping point, leading to a disparity between rising labor costs and falling robot prices. To meet development needs, reduce labor costs, shorten production cycles, and improve product yield, the PCB industry is building smart factories to meet customer demands and enhance their competitive capabilities.
The circuit board industry has undergone a transformation from “rapid development/rough management” to “slowed development/refined management.” The core drivers behind this transformation are: first, after two decades of rapid economic growth, demand has slowed, capacity has exceeded, and competition has intensified amid a trend of declining consumption; second, the demographic dividend is gradually disappearing, and automation in production and efficiency improvements are the current trends; third, overseas trade orders are being lost, leading to a shrinkage and migration on the demand side. During this period, while some companies have seen slight growth, many small and medium-sized enterprises’ rough management can no longer adapt to the industry’s changing trends, and management has fallen behind operations, making transformation imperative.
PCB Intelligent Manufacturing Solutions
｜Automation of Equipment and Production Data
Automatic issuance of process parameters: Based on product models, the necessary production process parameters (MI/Recipe) are issued to the equipment and automatically switched; for example, drilling machines and milling machines retrieve CAM programs through control mechanisms and verification methods.
Automatic data collection: Real-time, high-frequency automatic collection of production process data (man/machine/material/method/environment/measurement) is conducted, and linked to product Lot, PNL/SET (PCS); for instance,AOI, electrical measurement dataAutomatic reporting: Equipment automatically reports the loading and unloading times for each LOT; the production start and end times for each PNL/SET (PCS), and the equipment used, etc.
｜Refined Traceability and Quality Control
Production processes can be traced: Comprehensive refined traceability of LOT, even down to PNL/SET/PCS production history; traceability of people, machines, materials, methods, environments, and measurements for each Lot or even each product (PNL/SET/PCS) during the production process; automatic collection and control of production process data from key process equipment, achieving real-time warnings and early prevention of quality issues and foundational quality data for analysis.
｜Visualization of Planning and Production Sites
Production plans are visible: Based on sales orders (ERP), production plans, production orders (MES), and batches (Lot) to be produced;Comprehensive equipment utilization rates are visible: Real-time display of equipment utilization status and rates in various process segments/workshops; production processes are visible: real-time display of each order’s execution status: pending quantity, in-process quantity, output quantity, etc.; equipment status configuration: simulating operational parameters of each equipment through collected quantities, and displaying real-time status through industrial control configuration software, such as equipment temperature, pressure, chemical concentration, flow, speed, etc.
｜Management of Chemical Formulas for Development, Copper Deposition, Gold Deposition, etc.
Automatic dispensing: Automatically senses and dispenses based on changes in the density of chemical solutions during production to ensure the stability of formula ratios.
｜Tool Automation Management
Lifetime management: Digital management of drill bit usage counts, times, and precision; Usage quantity management: Fine management of tool issuance, storage, and return.
Top Ten Directions for PCB Intelligent Manufacturing Digitization
Smart products typically include mechanical, electrical, and embedded software components, with memory, perception, computation, and transmission capabilities. Typical smart products include smartphones, smart wearables, drones, smart cars, smart home appliances, and smart vending machines, among many other smart hardware products. Smart equipment is also a form of smart product. Companies should consider how to incorporate intelligent units into their products to enhance added value.
Based on sensors and the Internet of Things (IoT), the status of products can be sensed, allowing for preventive maintenance and timely assistance for customers in replacing spare parts. Additionally, understanding the operational status of products can help customers seize business opportunities. Big data from product operations can also be collected to assist companies in making market marketing decisions. Furthermore, developing customer service-oriented apps is another means of providing smart services, offering targeted services for products purchased by enterprises, thereby locking in users and conducting service marketing.
Manufacturing equipment has evolved from mechanical equipment to CNC equipment, and is now gradually developing into smart equipment. Smart equipment has detection capabilities, enabling in-machine detection to compensate for processing errors and improve processing precision, as well as compensating for thermal deformation. Previously, some precision equipment had high environmental requirements, but now, due to closed-loop detection and compensation, these requirements can be reduced.
4. Smart Production Lines
Many industries heavily rely on automated production lines, such as steel, chemicals, pharmaceuticals, food and beverage, tobacco, chip manufacturing, electronic assembly, and automotive manufacturing. Automated processing, assembly, and inspection have been implemented, and some standard mechanical parts production has also adopted automated production lines, such as bearings. However, equipment manufacturing companies still primarily engage in discrete manufacturing. Many companies focus their technological transformation on establishing automated production lines, assembly lines, and inspection lines. Boeing’s aircraft assembly plant has established a U-shaped pulsed assembly line. Automated production lines can be divided into rigid and flexible automated production lines, with flexible lines generally incorporating buffers. To improve production efficiency, industrial robots and overhead systems are increasingly applied in automated production lines.
A workshop typically contains multiple production lines, which either produce similar parts or products or have upstream and downstream assembly relationships. To achieve smart workshops, real-time collection and analysis of information regarding production status, equipment status, energy consumption, production quality, and material consumption are necessary for efficient scheduling and reasonable staffing to improve equipment utilization (OEE). Therefore, regardless of the manufacturing industry, a Manufacturing Execution System (MES) has become an inevitable choice for enterprises.
A factory typically consists of multiple workshops, and large enterprises have several factories. As a smart factory, not only should the production processes be automated, transparent, visualized, and lean, but product inspection, quality testing and analysis, and production logistics should also achieve closed-loop integration with the production process. Information sharing, just-in-time delivery, and collaborative operations among the multiple workshops in a factory are essential. Some discrete manufacturing enterprises have also established production command centers similar to those in process manufacturing enterprises to command and schedule the entire factory, promptly identifying and resolving unexpected issues, which is also an important hallmark of smart factories. Smart factories must rely on seamlessly integrated information systems, primarily including PLM, ERP, CRM, SCM, and MES core systems. Large enterprises’ smart factories need to utilize ERP systems to formulate production plans (Production planning) for multiple workshops, which are then detailed by MES systems based on each workshop’s production plans (Production scheduling). The scheduling by MES can be at the level of days, hours, or even minutes.
Discrete manufacturing enterprises have already applied CAD/CAM/CAE/CAPP/EDA tools and PDM/PLM systems in product R&D, but many companies do not utilize these software tools to their full potential. To develop smart products, companies need collaborative efforts across multiple disciplines, including mechanical, electrical, and software; to shorten product R&D cycles, deep application of simulation technology is necessary to establish virtual digital prototypes and achieve multidisciplinary simulation, thereby reducing physical testing; standardization, serialization, and modularization should be implemented to support mass customization or personalized product customization; and simulation technology should be combined with testing management to enhance the credibility of simulation results. Process manufacturing enterprises have begun to apply PLM systems for process management and formula management, and LIMS (Laboratory Information Management System) is widely used.
The core operational management systems of manufacturing enterprises also include Human Capital Management (HCM), Customer Relationship Management (CRM), Enterprise Asset Management (EAM), Energy Management Systems (EMS), Supplier Relationship Management (SRM), Enterprise Portals (EP), and Business Process Management Systems (BPM). Domestic enterprises also consider Office Automation (OA) as a core information system. In recent years, Master Data Management (MDM) has been deployed in large enterprises to unify the management of core master data. Achieving smart management and decision-making requires accurate foundational data and seamless integration of major information systems.
9. Smart Logistics and Supply Chain
Within manufacturing enterprises, procurement, production, and sales processes are accompanied by the flow of materials. Therefore, more and more manufacturing enterprises are paying attention to logistics automation alongside production automation. Automated three-dimensional warehouses, Automated Guided Vehicles (AGV), and intelligent overhead systems have been widely applied; in the logistics centers of manufacturing and logistics enterprises, the application of intelligent sorting systems, stacking robots, and automatic roller systems is becoming more prevalent. WMS (Warehouse Management System) and TMS (Transport Management System) are also gaining widespread attention from manufacturing and logistics enterprises.
10. Smart Decision-Making
During their operations, enterprises generate a vast amount of data. On one hand, there is core business data generated from various departments and business systems, such as data related to contracts, payments, expenses, inventory, cash, products, customers, investments, equipment, output, and delivery times. This data is generally structured and can be analyzed and predicted from multiple dimensions, falling within the realm of BI (Business Intelligence) technology, also known as management dashboards or decision support systems. At the same time, enterprises can use this data to extract their KPIs and compare them with preset goals, breaking down KPIs layer by layer for evaluating managers and employees, which falls under the scope of EPM (Enterprise Performance Management). From a technical perspective, in-memory computing is a crucial support for BI.
Source: Benchmark Leading
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