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Abstract: The purpose of food factories is to maximize the use of energy, resources, and practical technologies to process raw materials into the desired products. Therefore, food factory systems effectively integrate various unit operations. To meet the requirements of low cost, high efficiency, and the production of high-quality products, food processing automation technology has developed rapidly alongside the advancement of low-cost microprocessors. Programmable controllers enhance the automation level of equipment; Neuro-Fuzzy Logic improves the control of food processing operations; Statistical Process Control (SPC) is used for online quality control of products; Automated information control systems are not only essential for controlling manufacturing processes but also ensure product quality and production efficiency. This article provides a preliminary discussion on the demands and challenges associated with automation in food factories. It gives a brief introduction to FMS and CIMS using the machinery industry as an example; CIMS is also set to become the future model for the development of food factories. Keywords: Food automation; Computer integrated manufacturing. Chinese Library Classification Number: TP202. Document Code: B. Article ID: 1005 - 1295(2000) 01 - 0026 - 05.1 The need for automation in food factories. The food industry is facing increasingly fierce global competition, hence there is a strong demand for automation. (1) Automation can replace manual, monotonous and repetitive tasks, reducing damage caused by localized muscle strain in workers; (2) it meets the increasingly stringent quality requirements; (3) it enables rapid and effective detection and adjustment of quality defects on the production line, reducing differences in quality between batches and cutting down the costs associated with offline quality control; (4) it can effectively detect foreign objects and contaminants in food, thereby reducing quality risks. It is introduced from seven aspects below. 111 Reducing product costs: Automation in food factories helps to reduce labor intensity, minimize waste, and enable efficient mass production, thereby lowering the cost of products. Generally speaking, the implementation of automation reduces the number of equipment in the factory, as well as labor and maintenance costs. Automation systems possess accuracy and repeatability that are unmatched by humans, and can quickly reach their optimal operating condition through fine tuning. Therefore, automation features high efficiency and low costs, and **increases production capacity. In particular, the use of fully automatic weighing and packaging technology enables food manufacturers to increase their production capacity. 112 Ensuring quality: Modern quality control methods such as Statistical Quality Control (SQC) and Statistical Process Control (SPC) provide strong assurance for product quality. At the same time, factory automation is more conducive to the implementation of Total Quality Control (TQC). 113 Superior information processing capability: Data storage in computer control systems is easy to accomplish, and the storage capacity is virtually unlimited. The system can store a large amount of information related to processes and products, as well as generate complete operation reports and inventory lists. Quality data across product batches can be used for quality analysis, assisting technicians in process improvement and development. The operator can use this information to generate reports, adjust process parameters, change system settings, and run process tests, among other things. Reports indicate that an online measurement system for a frozen pizza production line can automatically calculate the average weight, weight range, standard deviation, and confidence interval of the products. 114 Applications of robots: Modern robots in food factories can control a variety of operations, and the combination of new robots with modern sensor technologies has contributed to the development of flexible, standardized processing techniques in the food industry. With the advancement of grasping capabilities, sensors, vision technology, and cutting-edge information processing techniques, it has become an inevitable trend for robots to enter the food processing industry. The robot’s arm can perform tasks such as picking, placing, transporting, and positioning in a manner similar to a human arm, but with greater strength, higher precision, better repeatability, and faster speed. Guangdong Jingwei Intelligent Machinery Co., Ltd. has developed a customized production line system for parallel robotic arms-based automatic sorting and stacking, which enables intelligent operations such as automatic sorting, automatic stacking, and automatic sequencing through the use of visual application systems. 115 Development of visual technology: Machine vision technology is used to detect defects in products in synchronization with the production line. It can systematically and comprehensively detect foreign objects, abnormal colors, scratches, defects, and other imperfections in food. Machine vision technology plays an important role in online quality control on production lines, but there are still some challenges in using it for the sorting of fruits and vegetables. 116 Health requirements: The requirements of food hygiene regulations are becoming increasingly strict. One way to meet these requirements is to minimize human involvement and interference in the production process, which can be achieved through mechanization and automation, and it may even be possible to have fully unmanned factories. Modern controllers are more compact and easier to clean, while automatic cleaning and sterilization systems provide reliable hygiene assurance. 117 Others: The perishability of food ingredients and the seasonality of production make them more suitable for comprehensive automated control and production in factories. 2 Difficulties in Implementing Automation in Food Plants The challenges associated with automating food processing lie in the varied formulation processes, strict hygiene requirements, the perishability of raw materials and finished products, as well as the cyclical nature of food production. Therefore, the automated control of food production is much more complex than that in other processing industries. For example, when making pizza, having too much or too little of any ingredient such as soy sauce, cheese, meat, or vegetables can affect its quality, taste, appearance, and cooking time. Therefore, the great variation in the size, shape, and uniformity of raw materials is the biggest obstacle to automation in food processing; moreover, the proliferation of microorganisms and physical damage also pose significant challenges. Furthermore, sensors for online detection of flavor and taste have not yet been developed. The processing of fluid materials such as beverages and dairy products is generally carried out on a continuous basis, while other materials are mostly processed in a batch mode; both approaches are gradually shifting toward automation or semi-automation. 211 In terms of process engineering, before designing an automated system, it is necessary to optimize the process and various process parameters, as well as to collect comprehensive and accurate information regarding the relevant operations. Most food processing processes are dynamic and require fine tuning; all parameters related to product uniformity should be fully automated. However, in general, the experiential skills and intuitive knowledge of skilled workers are difficult to clarify and implement using computer systems. The design of food processing controllers must take into account the type of each unit operation, the operating conditions, potential interference factors, measurements during operation, as well as issues related to startup and shutdown. 212 In terms of processing models, in order to design food processing controllers, it is necessary to establish mathematical models of the physical and chemical changes that occur during food processing, so as to facilitate the design of control programs. Mathematical models help automatic control systems make correct judgments, simplifying and improving accuracy in processing, while providing continuous guidance for the processing operations in the presence of external disturbances and changes in operating parameters over time. Therefore, understanding the inherent laws of change in food processing is crucial for designing effective control systems. However, the main obstacle is that the mechanisms behind most food processing processes are not well understood, and there is very little data available regarding their physical, chemical, and microbial aspects, which makes it difficult to measure these parameters; as a result, it is hard to develop accurate models to describe the actual processing processes. Furthermore, the variable biological characteristics of food increase the difficulty of designing control models. 3 Solutions for Automation in Food Factories: Food factories first need to determine which processes require automation, while also considering the potential for future upgrades to the control systems. Most of the processes in a food factory can be partially automated using small-scale computer systems, with the possibility of expanding them to large-scale or fully automated systems in the future when conditions permit. Control schemes and information are easily modified, copied, and damaged, so system security is particularly important. Secondly, computer viruses can destroy entire systems, so unauthorized access and use of such systems are strictly prohibited. The failure of a particular instrument should not affect other unrelated functions, in order to minimize the risk of sudden shutdowns. The advantages of a distributed, modular design lie in the ability to replace faulty devices or modules without interrupting the operation of the system, thereby preventing partial failures from leading to a complete collapse. Additionally, in control program design, it should also be taken into account the possibility of changing certain processing parameters online without stopping the production line. The 311 programmable controller (PLC) features low cost, good safety, powerful functions, and it reduces maintenance time and costs. PLCs can control almost anything – gearboxes, temperature, mixing time, packaging lines, etc. – and they can also provide functions for alarm generation, monitoring, and data collection. PLCs can also control energy-intensive devices such as evaporators, air compressors, and cooling water circulation systems. 312 On the operator side, many food factories still rely on the experience of operators and overlook technical knowledge. Many traditional manufacturing processes still rely on the operator’s vision, sense of smell, experience, and touch to determine product quality, while automation systems reduce the number of workers. Therefore, the following points should be considered during this transition: (1) Information instructions need to be simple and clear; (2) The method of displaying information should be appropriate—through vision, sound, multimedia, etc.; (3) The user interface must be good; (4) There should be a moderate level of modernization. The main purpose of automation is to increase efficiency and output while reducing consumption. The program should control all parameters and display them in real time; (5) The reduction in production time and the savings in training costs should also be taken into account as part of the automation costs; (6) New technologies should be easy to use; (7) Data information should be presented in tabular form to reduce errors; (8) Attention must be paid to the safety of operators. 313 Adaptability: System adaptability refers to the ability to be used for multiple functions and purposes, as well as the capability to integrate new technologies with minimal changes and at low cost, thereby keeping up with advancements in hardware. The controller features good adjustability, strong adaptability, and modularity. A good automation system can produce multiple products on a single production line with only minor adjustments. Therefore, system adaptability can significantly save investment in the event of future changes in processing conditions or products. 4 Computer Integrated Manufacturing Systems (CIMS) automate production and management activities on the basis of production process automation, enabling factory automation. The Computer Integrated Manufacturing System (CIMS) – which is developed based on software and hardware such as numerical control technology, flexible manufacturing technology, computer-aided design/computer-aided manufacturing technology, and computer-aided resource management – represents the future of mechanical manufacturing. It aims to integrate the design, production, and management aspects of a factory in an organic way, thereby achieving high efficiency, high flexibility, and intelligence, and ultimately realizing full automation and unmanned operation in factories of the future. The food industry is still an emerging sector compared to the machinery manufacturing industry. Due to the complexity of its raw materials, the relevant sensing technologies are not yet mature, and the process models are still under investigation; as a result, implementing automation presents certain difficulties. Here, only the machinery manufacturing industry will be discussed. 411 Composition and functions of CIMS: The composition of CIMS varies greatly depending on the nature of the factory or industry, as well as the scope or scale of CIM technology implementation. Figure 1 shows the typical structure of CIMS in manufacturing. Humans are the core of CIMS. A typical CMIS consists of four functional subsystems: the Management Information System (MIS), the Engineering Design Integration System (EDIS), the Automated Manufacturing System (AMS), and the Total Quality Management System (TQMS), as well as two support subsystems: the computer network (NET) and the database system (DBS). 1) Management Information System (MIS): MIS is a computer-assisted system for business operation and management. The current MIS systems are based on Manufacturing Resource Planning (MRP II), and they have been continuously improved and enhanced in terms of functions such as business decision-making and optimized production. Its goals are “no defects, zero inventory, no idling, low cost.” 2) Engineering Design Integration System (EDIS): EDIS is a computer-aided product engineering design system whose goal is to minimize or even eliminate the use of paper-based tasks in product design, automatically generating manufacturing data from design data in order to shorten the “design-to-production” cycle. EDIS mainly consists of CAD, CAPP, CAM, and simulation software. The main technologies used in the Engineering Design Integration System EDIS are: (1) Group Technology (GT): This is a simple yet extremely important concept in today’s manufacturing industry; it involves classifying and coding components based on principles of similarity, thereby grouping together a large number of parts with diverse specifications into a limited number of part families. Parts within the same family share similarities in terms of geometry, physical properties, mass, or manufacturing processes. Based on these similarities, corresponding workstations, manufacturing units, machining centers, or production lines are established, thereby enabling the manufacturing workshop to achieve scale benefits similar to those of an assembly line. (2) Computer-Aided Design and Manufacturing (CAD/CAM) technology: CAD/CAM integration technology is the most effective approach for achieving high-efficiency production of multiple product varieties in small batches. It is a fundamental element for realizing automated production, as well as the best way to improve the quality of design and manufacturing processes and productivity. It is one of the most notable major technologies in the world today. Although current CAD/CAM integration systems are still in their early stages and many integration techniques require further exploration and development, the benefits obtained from integrated CAD/CAM systems have already far exceeded those of any non-integrated CAD and CAM systems. (3) Computer-Aided Process Planning (CAPP) technology: Process planning involves designing the methods for processing a product and the manufacturing process based on the information provided by the product design requirements. CAPP can quickly generate complete and detailed process documents, **improving the efficiency of process engineers. It enables the development of optimized process plans that take into account the actual conditions of the enterprise, as well as the determination of reasonable labor quotas and material consumption levels. CAPP is a bridge that connects CAD and CAM; it is an important technology for achieving integration among CAD, CAM, and even CMIS. 3) Automated Manufacturing System (AMS) AMS is the system in CMIS for manufacturing actual products. Clearly, the information flows related to management, design, and quality generated by the other three subsystems of CIMS converge in AMS with the material flow, thereby transforming into actual products. 4) Total Quality Management System (TQMS): The TQMS is responsible for the collection, processing, dissemination, and utilization of comprehensive quality information across the entire enterprise, from market forecasting and sales, product design, and manufacturing to after-sales service. Its typical functions include quality planning and decision-making, quality information collection and analysis, as well as evaluation and control. 412 Information integration in CIMS: Computer networks (NET) and database systems (DBS) are the supporting tools for achieving information integration in CIMS. Computer networks are responsible for the data communication between various computers and terminals in CIMS; databases are responsible for storing and managing various types of data generated within CIMS. The various functional subsystems of CIMS are connected through network communication and share the data in the database according to their permissions, thereby achieving integration of information across functions. A typical food processing plant information system is shown in Figure 2. 413 Outlook on CIMS: The production models of modern industry can be divided into three categories: rigid production systems, flexible production systems, and autonomous, decentralized production systems. Current industry still relies mainly on rigid systems, but flexible production systems have begun to become widespread in developed countries. As the flexible model improves, it evolves toward an ideal autonomous, decentralized production system. CIM is a stepping stone toward automated factories. Japan has divided the development of CIMS into four stages: The first stage involves achieving automation for individual machines by introducing automated equipment; the second stage entails combining several automated devices to achieve automation at specific workstations; the third stage involves integrating these various workstations to automate the entire manufacturing process; and the fourth stage consists of integrating the manufacturing process with administrative management systems. In Japan, around 30 advanced companies have reached the third or fourth stage, but the overall level remains in the transition period from the first stage to the second stage. Following this approach, apart from a few advanced enterprises in China’s manufacturing sector that are developing FMS, the overall level of this industry is still in the first stage, where computerized numerical control technology is needed to achieve automation for individual machines. Therefore, CIM can serve as a goal to pursue. Guided by the concept of CIM integration, it is necessary first to take into account China’s national conditions and the actual situations of various organizations, develop the various component technologies related to CIM, create CIM systems that require less investment yet yield high returns, enhance the ability to compete, and gradually achieve a fully functional CIMS through long-term efforts. 5 Conclusion New types of controllers, sensors, and accessories are more accurate, reliable, adaptable, compact, portable, cost-effective, easy to maintain, durable, and standardized. Low-cost, more sensitive sensors will continue to emerge. All of these will contribute to achieving complete uniformity of the product and uninterrupted production. Better mathematical models will make the control system more effective. In the future, process control will enable operation conditions to be maintained at their optimal level at minimal operational costs. The resolution of key technical challenges in the future, along with reductions in the cost of digital electronic components, computers, and sensors, will greatly contribute to the automation of food processing. Implementing automation in food production will have unparalleled advantages. Food factories have strict requirements regarding hygiene management; implementing automation can minimize human intervention and reduce the risk of food contamination. Food processing has long relied on the experience of operators to ensure control, resulting in inconsistent product quality; automation can address this issue and guarantee standardized production. Food production often involves a variety of products in small batches, with strict expiration dates; given these time-sensitive constraints, implementing CIM becomes even more meaningful in the food industry.