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Computer-integrated manufacturing
Computer-integrated manufacturing
Computer-integrated manufacturing
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Manufacturing Systems Integration Program, NIST 2008

Computer-integrated manufacturing (CIM) is the manufacturing approach of using computers to control the entire production process.[1][2] This integration allows individual processes to exchange information with each part. Manufacturing can be faster and less error-prone by the integration of computers. Typically CIM relies on closed-loop control processes based on real-time input from sensors. It is also known as flexible design and manufacturing.[3]

Overview

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  1. Computer-integrated manufacturing is used in automotive, aviation, space, and ship building industries.[4]
  2. The term "computer-integrated manufacturing" is both a method of manufacturing and the name of a computer-automated system in which individual engineering, production, marketing, and support functions of a manufacturing enterprise are organized.
  3. In a CIM system functional areas such as design, analysis, planning, purchasing, cost accounting, inventory control, and distribution are linked through the computer with factory floor functions such as materials handling and management, providing direct control and monitoring of all the operation.

CIM is an example of the implementation of information and communication technologies (ICTs) in manufacturing.

CIM implies that there are at least two computers exchanging information, e.g. the controller of an arm robot and a micro-controller.

CIM is most useful where a high level of ICT is used in the company or facility, such as CAD/CAM systems, and the availability of process planning and its data.

History

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The idea of "digital manufacturing" became prominent in the early 1970s, with the release of Dr. Joseph Harrington's book, Computer Integrated Manufacturing.[5] However, it was not until 1984 when computer-integrated manufacturing began to be developed and promoted by machine tool manufacturers and the Computer and Automated Systems Association and Society of Manufacturing Engineers (CASA/SME).

"CIM is the integration of total manufacturing enterprise by using integrated systems and data communication coupled with new managerial philosophies that improve organizational and personnel efficiency." ERHUM
In a literature research was shown that 37 different concepts of CIM were published, most of them from Germany and USA. In a timeline of the 37 publications it is possible to see how the CIM concept developed over time. Also it is quite markable how different the concepts of all publications are.[6]

Topics

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CIM & production control system: Computer Integrated Manufacturing is used to describe the complete automation of a manufacturing plant, with all processes running under computer control and digital information tying them together.[7]

Key challenges

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There are three major challenges to development of a smoothly operating computer-integrated manufacturing system:

  • Integration of components from different suppliers: When different machines, such as CNC, conveyors and robots, are using different communications protocols (In the case of AGVs, even differing lengths of time for charging the batteries) may cause problems.
  • Data integrity: The higher the degree of automation, the more critical is the integrity of the data used to control the machines. While the CIM system saves on labor of operating the machines, it requires extra human labor in ensuring that there are proper safeguards for the data signals that are used to control the machines.
  • Process control: Computers may be used to assist the human operators of the manufacturing facility, but there must always be a competent engineer on hand to handle circumstances which could not be foreseen by the designers of the control software.

Subsystems

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A computer-integrated manufacturing system is not the same as a "lights-out factory", which would run completely independent of human intervention, although it is a big step in that direction. Part of the system involves flexible manufacturing, where the factory can be quickly modified to produce different products, or where the volume of products can be changed quickly with the aid of computers. Some or all of the following subsystems may be found in a CIM operation:

Computer-aided techniques:

Devices and equipment required:

Technologies:

Others:

CIMOSA

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CIMOSA (Computer Integrated Manufacturing Open System Architecture), is a 1990s European proposal for an open systems architecture for CIM developed by the AMICE Consortium as a series of ESPRIT projects.[8][9] The goal of CIMOSA was "to help companies to manage change and integrate their facilities and operations to face world wide competition. It provides a consistent architectural framework for both enterprise modeling and enterprise integration as required in CIM environments".[10]

CIMOSA provides a solution for business integration with four types of products:[11]

  • The CIMOSA Enterprise Modeling Framework, which provides a reference architecture for enterprise architecture
  • CIMOSA IIS, a standard for physical and application integration.
  • CIMOSA Systems Life Cycle, is a life cycle model for CIM development and deployment.
  • Inputs to standardization, basics for international standard development.

CIMOSA according to Vernadat (1996), coined the term business process and introduced the process-based approach for integrated enterprise modeling based on a cross-boundaries approach, which opposed to traditional function or activity-based approaches. With CIMOSA also the concept of an "Open System Architecture" (OSA) for CIM was introduced, which was designed to be vendor-independent, and constructed with standardised CIM modules. Here to the OSA is "described in terms of their function, information, resource, and organizational aspects. This should be designed with structured engineering methods and made operational in a modular and evolutionary architecture for operational use".[10]

Areas

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There are multiple areas of usage:

See also

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References

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Further reading

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[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Computer-integrated manufacturing

View on Grokipedia
from Grokipedia
Computer-integrated manufacturing (CIM) is a manufacturing approach that uses computers to control and integrate the entire production process, encompassing design, planning, production, and management activities to achieve seamless data flow and automation across the enterprise. This system unifies manufacturing hardware, software, and managerial philosophies through integrated systems and data communications, enabling efficient coordination of all related functions from product conception to distribution. By centralizing knowledge and processes, CIM aims to improve productivity, quality, and flexibility while reducing costs and lead times in industrial operations. The concept of CIM evolved from early in the mid-20th century, with in (NC) systems developed in 1952 for applications at MIT. By the 1960s, advancements like direct numerical control (DNC) and computer (CNC) in the late 1960s laid the groundwork for broader , leading to flexible systems in the late 1970s. The term CIM gained prominence in the as a strategy for total enterprise integration, driven by U.S. and industrial initiatives, such as the Command's 42 CIM projects under the Methods and program. International standards like ISO TC184/SC5 (established around 1990) and protocols such as /TOP further standardized its implementation. Key components of CIM include computer-aided design (CAD) for product modeling, computer-aided manufacturing (CAM) for production control, computer-aided process planning (CAPP) for workflow optimization, and manufacturing resource planning (MRP II) for inventory and scheduling management. Hardware elements encompass CNC machines, industrial robots, sensors, and flexible manufacturing systems (FMS), while software supports data management via standards like STEP and SQL, and communications through OSI models. Subsystems span nine major areas: , , planning, purchasing, , , warehousing, , , and information processing, all interconnected to minimize human intervention. CIM offers significant benefits, including cost reductions of 5-20% in personnel and 15-30% in , cuts of 30-60%, and decreases of 30-60%, alongside output increases of 40-70% and quality improvements up to 500%. It enhances equipment utilization by 200-300% and design productivity by 300-500%, promoting on-time delivery and adaptability to market changes. However, successful adoption requires addressing challenges like infrastructural upgrades, staff training, and ethical considerations in . In modern contexts, CIM principles underpin advanced paradigms, integrating with technologies like and data analytics for sustained efficiency.

Introduction

Definition and Principles

Computer-integrated manufacturing (CIM) refers to the comprehensive integration of computer systems and technologies throughout the enterprise, encompassing the full lifecycle from and to production execution, , , and distribution. This holistic approach relies on integrated hardware, software, and communication networks to automate and synchronize operations, ensuring seamless exchange that eliminates and supports end-to-end visibility. By leveraging a shared database and standardized protocols, CIM transforms traditional into a unified, responsive system that optimizes resource utilization and responsiveness to market demands. At its core, CIM operates on principles such as closed-loop control systems, which incorporate sensors and actuators to continuously monitor process outputs and automatically adjust inputs in real time, thereby maintaining precision and adapting to variations without manual oversight. processing enables dynamic scheduling and by analyzing live inputs from and production lines, allowing for immediate corrections to disruptions like material shortages or failures. Hierarchical control further structures these operations across distinct levels: the factory level handles and ; the shop floor level manages tactical scheduling and coordination; and the machine level executes direct operational commands, ensuring from individual devices to enterprise-wide oversight. These principles collectively aim to minimize human intervention, reducing errors, operational costs, and cycle times through automated, reliable processes. The of CIM emphasizes the "integrated" nature through interconnected feedback loops that foster adaptive , where from downstream informs upstream adjustments to enhance overall and product quality. A high-level in this model begins with design input, where product specifications and models are developed; proceeds to planning and simulation, involving optimization and resource forecasting; advances to production execution, with automated , assembly, and monitoring; incorporates via inline inspections and corrections; and concludes with output delivery, including and customer fulfillment—all linked by bidirectional flows to enable continuous improvement and closed-loop adaptability.

Scope and Objectives

Computer-integrated manufacturing (CIM) defines a comprehensive approach that integrates computer technologies across core manufacturing functions to streamline operations from inception to delivery. Its scope encompasses design through computer-aided design (CAD), process planning via computer-aided process planning (CAPP), production execution with computer-aided manufacturing (CAM), assembly processes, testing and quality assurance using computer-aided quality control (CAQC), and logistics management. This integration facilitates real-time data flow and automated decision-making within the manufacturing enterprise, often extending to business functions such as marketing and finance for enterprise-wide coordination. The primary objectives of CIM center on enhancing production flexibility to adapt to varying demands, implementing just-in-time () manufacturing to minimize waste, shortening lead times through efficient , elevating product quality via automated monitoring and closed-loop controls, and enabling scalability for to meet diverse customer specifications without proportional cost increases. These goals aim to boost overall productivity, improve customer service, and ensure sustainable profitability by reducing operational inefficiencies. In contrast to traditional manufacturing, which depends on labor-intensive, manual processes with limited coordination, CIM promotes holistic enterprise-wide integration of hardware, software, and human elements to create a unified system. This distinguishes it from partial automation approaches, like isolated CNC machines or robotic islands, which lack interconnected data management and result in fragmented operations; CIM, instead, leverages network connectivity for seamless information exchange, avoiding silos and enabling optimized, responsive manufacturing ecosystems. General industry benchmarks for CIM implementation target 15-30% reductions in design costs and 10-30% in overhead expenses, alongside up to 60% cuts in setup times and 40% in inventory via integrated techniques like group technology, collectively driving 20-50% improvements in cycle times and production costs in adopting firms.

Historical Development

Origins in Automation

The roots of computer-integrated manufacturing lie in the broader history of industrial automation, which began with the mechanization of production during the Industrial Revolution in the late 18th century. James Watt's improvements to the steam engine in the 1770s provided a versatile and efficient power source that drove machinery in factories, enabling the transition from artisanal craftsmanship to large-scale, machine-powered manufacturing processes. This shift mechanized tasks such as textile spinning and weaving, laying the groundwork for automated production by reducing reliance on human or animal labor and increasing output efficiency. By the 19th and early 20th centuries, further advancements in electrical power and assembly lines, exemplified by Henry Ford's moving assembly line in 1913, extended automation to mass production, particularly in the automotive industry, where standardized parts were fabricated and assembled at scale. A pivotal precursor to more sophisticated automation emerged in the mid-20th century with the development of numerical control (NC) systems for machine tools. In the late 1940s, John T. Parsons, an inventor in the aerospace sector, conceived the idea of using punched cards to guide machine tools for precision machining of complex helicopter rotor blades, addressing the limitations of manual operations in producing intricate aircraft components. This concept was realized through collaboration with the Massachusetts Institute of Technology (MIT) Servomechanisms Laboratory, which demonstrated the first working model of a continuous-path NC milling machine in 1952, funded by the U.S. Air Force to meet demands for high-precision parts in aerospace manufacturing. By the 1950s and into the 1960s, NC evolved into computer numerical control (CNC) as digital computers replaced punched tapes with software-driven instructions, enhancing accuracy and flexibility in batch production systems for industries requiring tight tolerances, such as aviation and defense. These systems automated tool paths on lathes, mills, and grinders, reducing setup times and human error while enabling the production of custom geometries unattainable by traditional methods. Conceptual foundations for integrated automation were shaped by the emergence of and in the 1940s and 1950s, which provided frameworks for understanding control in complex processes. , a at MIT, coined the term "" in his 1948 book Cybernetics: Or Control and Communication in the Animal and the Machine, defining it as the study of regulatory mechanisms in mechanical, electronic, and biological systems, emphasizing feedback loops for self-regulation and . This interdisciplinary approach influenced early by promoting the design of closed-loop systems that could monitor outputs and adjust inputs dynamically, drawing parallels between human nervous systems and industrial machinery to inspire automated oversight in production environments. , concurrently advanced by figures like , extended these ideas to view factories as interconnected wholes rather than isolated machines, fostering the notion of holistic control architectures that integrated information flow across operations. The transition toward factory-wide integration gained momentum in the 1960s with the advent of , which offered affordable computational power for coordinating multiple machines. The Digital Equipment Corporation's PDP-8, introduced in 1965, was a landmark that fit into industrial settings due to its compact size, low cost (around $18,000), and real-time processing capabilities, allowing it to control NC machines and monitor production lines in manufacturing plants. These devices enabled the linkage of disparate tools through shared , facilitating early experiments in centralized control that hinted at broader system synchronization without requiring massive mainframes. By making digital oversight scalable and economically viable, bridged isolated efforts, setting the stage for comprehensive production ecosystems.

Key Milestones and Evolution

The term computer-integrated manufacturing (CIM) was first coined by Joseph Harrington in his seminal 1973 book Computer Integrated Manufacturing, which outlined the vision of using computers to unify all aspects of manufacturing from design to distribution. This foundational work emphasized the potential for digital integration to streamline operations and reduce inefficiencies in . Building on Harrington's ideas, Dr. James Browne expanded the concept in his 1984 book Production Management Systems: A CIM Perspective, which provided a detailed framework for implementing CIM in production environments, focusing on hierarchical control systems and flow to enhance . A major U.S. military contribution was the Air Force's Integrated Computer-Aided Manufacturing (ICAM) program, launched in 1976 at Wright-Patterson Air Force Base, which developed integrated tools and reference models for manufacturing automation, including the IDEF family of modeling languages, to support CIM implementation across aerospace and defense sectors. During the 1970s and 1980s, CIM gained traction in the automotive industry, where companies sought to address rising costs and market demands through automated integration. A key milestone was General Motors' launch of the Manufacturing Automation Protocol (MAP) project in 1980, which developed standardized communication protocols to enable interoperability among programmable logic controllers, robots, and other factory equipment, laying the groundwork for broader CIM adoption. Concurrently, the rise of flexible manufacturing systems (FMS) in the late 1970s and throughout the 1980s allowed factories to switch between product types efficiently using computer-controlled machine tools and material handling, significantly improving adaptability in high-volume production sectors like automotive and aerospace. In the , CIM evolved amid intensifying global competition, incorporating () systems for seamless integration of with business functions such as and , enabling more responsive operations across supply chains. This period also saw a shift toward open systems architectures, exemplified by the European CIMOSA model introduced in the early , which promoted standardized, vendor-independent frameworks to facilitate and in environments. Influenced by principles—popularized in the through works like James Womack's analysis of Toyota's practices—CIM emphasized waste reduction and just-in-time production, aligning digital tools with streamlined processes to boost competitiveness. By the early 2000s, CIM incorporated web-based technologies to enhance integration, allowing real-time among global partners via internet-enabled platforms for and coordination. A notable example was Boeing's application of CIM principles in the production of the 787 Dreamliner, launched in 2004, where integrated digital systems facilitated supplier , automated assembly processes, and lifecycle management to accelerate development and reduce costs in a complex, outsourced .

Core Technologies

Design and Planning Tools

Design and planning tools form the foundational upstream components of computer-integrated manufacturing (CIM), enabling the conceptualization, , and preparation of products and processes before production execution. These tools leverage computational power to create digital representations, simulate behaviors, and generate optimized plans, ensuring seamless flow throughout the manufacturing lifecycle. By integrating intent with process feasibility, they reduce errors, accelerate development, and support iterative refinement, ultimately contributing to efficient CIM systems. Computer-Aided Design (CAD) software is essential for creating precise digital models of products in CIM. It supports both 2D drafting for technical drawings and for visualizing complex geometries, such as assemblies of mechanical parts, allowing designers to define shapes, dimensions, and tolerances with high accuracy. CAD systems incorporate capabilities to evaluate design performance under various conditions, including kinematic motion and interference checks, without requiring physical prototypes. , a core feature in advanced CAD tools like Pro/ENGINEER, uses mathematical relationships and constraints to drive geometry based on variables, enabling quick modifications and facilitating through techniques like to produce testable models efficiently. This approach has significantly reduced design cycles in , with historical development tracing back to the at companies like and for automating drafting tasks. Computer-Aided Process Planning (CAPP) automates the creation of manufacturing process plans, bridging the gap between design and production in CIM by determining optimal sequences of operations. It employs two primary approaches: variant process planning, which retrieves and modifies existing plans from a database using group technology to classify parts into families based on similarities, and generative process planning, which synthesizes new plans from scratch using decision logic, of manufacturing , and algorithms to select tools, machines, and parameters. In generative CAPP, manufacturing sequences are generated by analyzing part geometry and requirements, such as identifying operations and sequencing them to minimize setup times. Algorithms for optimal , including cost-minimization models that group elementary volumes and apply optimization techniques like , ensure efficient paths through the production facility, enhancing overall CIM productivity. These methods rely on the expertise of process planners for validation in variant systems, while generative approaches offer greater for novel parts. Computer-Aided Engineering (CAE) tools complement CAD by performing advanced simulations to validate designs, particularly through finite element analysis (FEA) for stress testing in CIM applications. FEA divides complex 3D models into a mesh of finite elements, solving partial differential equations to predict structural responses to loads, such as deformation or failure points, thereby identifying weaknesses early in the design phase. This enables engineers to test material behavior under real-world conditions like tension or compression, optimizing for durability and safety without costly prototypes. A fundamental principle underlying linear elastic analysis in FEA is Hooke's law, which relates stress (σ) to strain (ε) via the material's Young's modulus (E): σ=Eϵ\sigma = E \epsilon Here, σ represents normal , ε is the corresponding strain, and E quantifies the material's , assuming small deformations within the elastic limit. By incorporating such equations into FEA solvers, CAE tools provide quantitative validation, reducing physical testing by up to 25% in workflows and saving significant development time. The integration of CAD, CAPP, and CAE in CIM relies on standardized data formats like , which ensures by representing product models in a neutral, exchangeable structure. STEP facilitates the transfer of geometric data, process plans, and simulation results from upstream tools to downstream systems, such as , through application protocols that maintain semantic integrity. For instance, (an extension of ) uses high-level features like "workingsteps" to convey intent directly, avoiding loss of during translation and enabling flexible routing in distributed CIM environments. This supports end-to-end data flow, enhancing efficiency across global operations.

Production and Control Systems

(CAM) forms the backbone of production execution in computer-integrated manufacturing (CIM), translating digital designs into precise instructions for computer numerical control (CNC) machines. CAM software generates optimized toolpaths by analyzing part geometry, material properties, and machining constraints, ensuring efficient material removal while minimizing cycle times and . This process involves algorithmic path planning, such as constant scallop-height methods for surface finishing, to maintain uniform surface quality across complex contours. A key output of CAM is G-code programming, a standardized numerical control language that directs CNC machines through commands for linear and circular interpolations, spindle speeds, and coolant activation. G-code enables seamless integration of CAM with CNC hardware, allowing for automated execution of multi-operation sequences without manual intervention. For instance, in high-precision applications like component fabrication, G-code supports post-processing tailored to specific machine , reducing setup errors compared to manual coding. CAM also incorporates multi-axis simulations to validate toolpaths prior to physical production, modeling cutter engagement and machine dynamics to predict collisions, vibrations, and overcuts. These simulations, often powered by finite element analysis within the CAM environment, facilitate virtual verification for 5-axis or higher configurations, where tool orientation changes dynamically to access undercuts and steep walls. By iterating on simulated outcomes, manufacturers improve first-pass success rates in CIM setups, enhancing overall throughput. In CIM production systems, industrial robotics and automated guided vehicles (AGVs) enable flexible of assembly and tasks, coordinated through centralized control architectures. Selective Compliance Articulated Robot Arms () excel in high-speed pick-and-place operations for electronics assembly, offering four with compliance in the horizontal plane for tolerant insertions, while articulated robots provide six or more axes for versatile manipulation in or . These robots are programmed using offline tools integrated with CIM, allowing path optimization based on workspace constraints and cycle time targets. Control of robots and AGVs in CIM relies on Programmable Logic Controllers (PLCs), rugged industrial computers that execute programs for sequential operations, interfacing with sensors for feedback loops. PLCs manage real-time , such as triggering robot grippers upon AGV arrival, ensuring deterministic response times under 10 milliseconds for safety-critical tasks. In a typical CIM cell, PLC networks use protocols like to link multiple units, supporting fault-tolerant redundancy for uninterrupted production. AGVs facilitate intralogistics by autonomously transporting work-in-progress between workstations, guided by magnetic tapes, lasers, or vision systems within the CIM environment. These vehicles optimize routes using onboard algorithms that adapt to dynamic layouts, reducing manual dependency and cutting costs in large-scale facilities. Integrated with CIM oversight, AGVs report position and load status in real time, enabling and just-in-time delivery to assembly lines. Shop floor control in CIM encompasses systems that orchestrate real-time operations across production lines, ensuring alignment between planned schedules and actual execution. platforms serve as the primary interface, aggregating data from distributed sensors and PLCs to visualize machine states, throughput rates, and downtime events on human-machine interfaces (HMIs). enables hierarchical oversight, from operator-level alarms to supervisory dashboards, facilitating rapid response to disruptions like tool failures. Real-time scheduling algorithms embedded in control optimize job dispatching amid uncertainties such as machine breakdowns or varying demand. The Shortest Processing Time (SPT) rule, a priority-based , sequences jobs by ascending processing duration to minimize average completion times and work-in-process inventory, proven effective in environments with flow times reduced over random dispatching. In CIM implementations, SPT integrates with models for lookahead validation, dynamically adjusting priorities via event-driven triggers from data streams. Quality control within CIM production systems leverages Computer-Aided Quality Control (CAQC) to embed directly into workflows, minimizing post-process rework. CAQC deploys networked sensors—such as profilometers and vision cameras—for in-process monitoring, capturing dimensional data at key stages like or assembly without halting operations. This closed-loop approach feeds measurements back to control systems, enabling adaptive adjustments like feed rate corrections to maintain tolerances. Statistical Process Control (SPC) underpins CAQC by applying control charts to track process capability over time, distinguishing common-cause variation from special causes through limits set at three standard deviations. X-bar and R charts, for example, plot sample means and ranges to detect shifts, triggering alerts when points exceed control bounds. In CIM, SPC software automates chart generation and analysis, integrating with CAQC sensors to sustain CpK values above 1.33 for critical features, thereby ensuring consistent quality across batches. Tolerance analysis in CAQC quantifies cumulative effects on part dimensions, with the standard deviation of total variation given by σx=σm2+σp2\sigma_x = \sqrt{\sigma_m^2 + \sigma_p^2}
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