Computer Integrated Manufacturing Research Articles

Enhancing engineering education through mini project-based learning in computer integrated manufacturing laboratory: A student-centric approach

ABSTRACT In response to evolving engineering education, there is a growing focus on fostering students’ practical skills. This study implemented a Mini Project Based Learning (MPBL) approach within the Computer Integrated Manufacturing (CIM) course. The tasks involved SOLIDWORKS modelling, writing CNC code using CADEM seeNC Turn software, and hands-on CNC machining by students. Quantifiable outcomes demonstrated the development of students’ CNC expertise. Numerical evidence showcased a significant increase in academic performance, with the percentage of students scoring over 80 marks rising from 33.33% in the previous academic year (CAY-M1) to an impressive 45.83% in the current academic year (CAY), highlighting the positive impact of MPBL. MPBL improved comprehension and enhanced critical thinking, collaboration, creativity, and communication skills. Its integration in CIM sparked creative enthusiasm among students and educators. Effective evaluation tools played a pivotal role in elevating project results, ensuring a holistic learning experience that integrated theory and engineering design.

Literature Review: Application of Computer Integrated Manufacturing (CIM) to Increase Operational Effectiveness and Company Productivity

Computer Integrated Manufacturing(CIM) is a manufacturing method and the name of an automated computer system by which the individual engineering, production, marketing, and support functions of a manufacturing company are organized. In a CIM system, functional areas such as design, analysis, planning, purchasing, cost accounting, inventory control, and distribution are linked via computers providing direct control and monitoring of all process operations. This study aims to analyze and review the literature on Computer Integrated Manufacturing in previous research through references to international journal articles by considering the discussion of the application of CIM in a manufacturing industry case for production efficiency and company operations with article sampling parameters such as being indexed in Scopus Q3.

Role of digitalized sustainable manufacturing in SME’S: A bibliometric analysis

The evolution of digital manufacturing is inherently linked with Computer Integrated Manufacturing (CIM) since 1980. CIM triggers the partial or fully automation in the industrial sector. Due to the global competitiveness and mandatory requirement of triple bottom line sustainability approach, the industries are on the verge of adapting digitalized sustainable manufacturing. The Digital manufacturing encompasses the whole integration throughout product life cycle and process, real-time monitoring of entire system, adaption of new innovative technologies in the context of sustainability. Nevertheless, DM is not restricted to a specific area, it includes all the tools and technologies for the quality improvement, customization, and efficient production strategies. The I4.0 technologies are vital elements of the Digital manufacturing paving the way to the sustainability. DM is the culmination of all information and communication technologies with high-speed computation capability, real-time data analysis by AI technology and finally cluster of all innovative technologies. Mostly all large-scale industrial sectors are adapting the digital technologies for existence survival in the international market. But the scenario of SME’S is contradictory, as they are perplexed in financial return, and ambiguous about the impact of these technologies on the accomplishment of overall sustainability. Especially, the adaption of innovative technologies in context of overall sustainability will cause a huge economic burden on the SME’S due to lack of funds and resources, and They must be reinforced by the government legislations and full back support from all corners. However, the past academic research was focused on the economic benefits of the digitalization and seldom efforts on providing insights about the integration of digitalization with triple bottom line sustainability. In addition, they envisioned the profitable aspects of the digital technologies in large scale manufacturing sector. These research gaps had been explored in the current study. The paper aims to conduct a bibliometric analysis on the past research developments in the digitalized sustainable manufacturing enhanced by the visualization software VOS in SME’S for providing a clear insight into the strategies, impediments, and ongoing trend in the small-scale sector.

History, Features, Challenges, and Critical Success Factors of Enterprise Resource Planning (ERP) in The Era of Industry 4.0

ERP has been adopting newer features over the last several decades and shaping global businesses with the advent of newer technologies. This research article uses a state-of-the-art review method with the purpose to review and synthesize the latest information on the possible integration of potential Industry 4.0 technologies into the future development of ERP. Different software that contributed to the development of the existing ERP is found to be Material Requirement Planning (MRP), Manufacturing Resource Planning (MRPII), and Computer Integrated Manufacturing (CIM). Potential disruptive Industry 4.0 technologies that are featured to be integrated into future ERP are artificial intelligence, business intelligence, the internet of things, big data, blockchain technology, and omnichannel strategy. Notable Critical Success Factors of ERP have been reported to be top management support, project team, IT infrastructure, communication, skilled staff, training & education, and monitoring & evaluation. Moreover, cybersecurity has been found to be the most challenging issue to overcome in future versions of ERP. This review article could help future ERP researchers and respective stakeholders contribute to integrating newer features in future versions of ERP.

Metamodelling of Manufacturing Processes and Automation Workflows towards Designing and Operating Digital Twins

The automation of workflows for the optimization of manufacturing processes through digital twins seems to be achievable nowadays. The enabling technologies of Industry 4.0 have matured, while the plethora of available sensors and data processing methods can be used to address functionalities related to manufacturing processes, such as process monitoring and control, quality assessment and process modelling. However, technologies succeeding Computer-Integrated Manufacturing and several promising techniques, such as metamodelling languages, have not been exploited enough. To this end, a framework is presented, utilizing an automation workflow knowledge database, a classification of technologies and a metamodelling language. This approach will be highly useful for creating digital twins for both the design and operation of manufacturing processes, while keeping humans in the loop. Two process control paradigms are used to illustrate the applicability of such an approach, under the framework of certifiable human-in-the-loop process optimization.

History, Features, Challenges and Critical Success Factors of ERP in the ERA of Industry 4.0

This paper uses a state-of-the-art review system with a purpose to review and synthesize the latest information on the possible integration of potential disruptive technologies into the future development of ERP. Different software, such as Material Requirement Planning (MRP), Manufacturing Resource Planning (MRPII), and Computer Integrated Manufacturing (CIM) have been found to be the base of existing versions of ERP. Potential disruptive Industry 4.0 technologies that can be integrated into ERP are found to be artificial intelligence, business intelligence, internet of things, big data, blockchain technology, and omnichannel strategy. Notable Critical Success Factors of ERP are top management support, project team, IT infrastructure, communication, skilled staff, training & education, and monitoring & evaluation. Moreover, cybersecurity has been found to be the most challenging issue to overcome in future versions of ERP. This paper could help the future ERP researchers and respective stakeholders contribute to integrate newer features in the future versions of ERP.

Reference Architecture for CIM the Bi-Level Architecture for Efficient Manufacturing BLAEM

The combination of innovative advance and fluctuating client needs are paving the way to continuous changes in the manufacturing environments. As a result, mechanical enterprises aim for the honing of their processes by means of Computer Integrated Manufacturing (CIM). Their holy grail is connecting the shop floor systems to the high business layer systems. Based on our previous literature review on the CIM architectures available in the literature, we have identified six major aspects and detected the limitations of the existing appeals. We attempted to get the better of these fall backs and to be as exhaustive as to cover all 6 aspects through the BLAEM Architecture (Bi-Level Architecture for efficient Manufacturing). Through this paper, we will be presenting an execution of the BLAEM by means of a real case study.

Retraction Notice to Mathematical model for quality cost optimization. [Robotics and Computer-Integrated Manufacturing. Vol. 24/6 (2008) pp 811-815

Retraction Notice to Mathematical model for quality cost optimization. [Robotics and Computer-Integrated Manufacturing. Vol. 24/6 (2008) pp 811-815

Study of industrial interactive design system based on virtual reality teaching technology in industrial robot

Abstract Across numerous disciplines, virtual reality (VR) had been used to aid decision-making in training, design, and evaluation processes. Both the educational and industrial groups have contributed to a vast knowledge based on a variety of VR topics during the last two decades. VR has been expanded to industry in recent years, but the majority of its applications do not involve industrial robots. To study the application of VR technology in industrial design, it is better to combine the design activities with computer-integrated manufacturing system and bring new opportunities for the innovation of industrial design. Therefore, in this article, an application of industrial interactive design system based on VR technology in the education domain is explored. First, the function and scheme design of industrial robot assembly and adjustment system are designed, and the model is established. Finally, SolidWorks and 3DsMAX are selected as three-dimensional model development tools. Unity 3D is used as the VR development engine; HTC VIVE is used as VR equipment. The study shows that the design of the machine motion instruction interpreter is effective, and the specific steps of the system to realize real-time control are also given. The feasibility of the system is verified through the analysis of typical applications of industrial robots.

COVID-19, economic crises and digitalisation: How algorithmic management became an alternative to automation.

The COVID‐19 crisis witnessed a major rise in investment in software for the digital organisation and rationalisation of work, while investment in robotics is continuously lagging behind expectations. This article argues that we can understand this development as the continuation of the rise of algorithmic management as a technological fix for profitability crises. Thus, in the face of falling wage rates and a structural overaccumulation of capital since the 1970s, algorithmic management has become an alternative to automation. The article reconstructs the history of algorithmic management in connection to economic crises. This allows for periodisation of the rise of algorithmic management from 'computer‐integrated manufacturing' to remote work in four waves. In times of crisis, algorithmic management functions as a substitute for investment in 'tangible capital' such as robots. Structural economic forces thus interact with labour conflicts at the company level, shaping the rise of algorithmic management.

The Implementation of Multiobjective Flexible Workshop Scheduling Based on Genetic Simulated Annealing-Inspired Clustering Algorithm

Multiobjective flexible workshop scheduling is an important subject to improve resource utilization and production efficiency and enhance the competitiveness of enterprises. As the situation of resource constraints becomes more and more severe, the problem of companies rationally allocating limited resources in production is becoming more and more serious. Today, the manufacturing industry widely adopts advanced manufacturing modes such as computer-integrated manufacturing and intelligent manufacturing, but in these semi-intelligent manufacturing modes with a high degree of uncertainty and a high degree of personnel dependence, it is difficult to adapt to the work of large-scale production. Therefore, suitable clustering algorithms are urgently needed to help solve these problems, and this paper selects a clustering algorithm based on the genetic simulation annealing algorithm. This article is aimed at studying the problem of efficiency improvement in the production process of large-scale manufacturing and at finding a stronger and more effective production mode for the manufacturing industry. Firstly, this paper introduces the basic principles of simulated annealing genetic algorithm and regularized clustering algorithm. These algorithms have excellent performance in searching for global optimal solutions. They can be constantly tested and computed to keep the calculation results close to the global optimal solution. In this paper, the K -means clustering algorithm is used to select the shortest completion time to represent the clustering target. According to the minimum distance principle, the machine, workpiece, and other objects are input into the clustering of the algorithm, and the K -means algorithm will send out the sorting plan. Therefore, a multiobjective flexible job shop scheduling model based on genetic simulated annealing algorithm and clustering algorithm is established. Then, by using hypothetical production data to simulate the operation of the workshop, the scheduling model was applied to conduct a deduction and empirical comparative study. The experimental results showed that the model shortened the completion time of the workpiece by 4.4% and increased the average load rate of the machine by 10%.

Towards synchronization-oriented manufacturing planning and control for Industry 4.0 and beyond

Benefiting from the marriage of just-in-time (JIT) and computer integrated manufacturing (CIM), a series of manufacturing planning and control (MPC) systems has been introduced for contemporary production and operations management. However, the increasing requirements for customized and personalized products and services, together with the acceleration of the socio-economic environment in the age of Industry 4.0, forced manufacturers to re-evaluate their current MPC strategies that mainly emphasize time, cost and quality priorities. Industry 4.0 presents a solid ambition for transforming to a digital, data-driven and interconnected manufacturing industry centred around cyber-physical convergence. Although the relevance of Industry 4.0 technologies’ disruptions on operations management has been acknowledged, a significant gap in the evolution of the logic and design of MPC systems has been observed. The requirements of on-demand products and services with enhanced flexibility, resilience, sustainability and humanitarianism in this new industrial revolution call for innovations of MPC architectures and approaches. This paper first summarizes the evolution of MPC systems with enabling technologies and the changing business climate at that time. To explore innovative MPC that complies with the increasing economic, social and environmental requirements, the concept of synchronization-oriented MPC with four principles is proposed. Following the principles, a general framework for the synchronization-oriented MPC with enhanced economic, social and environmental benefits is developed. This work potentially provides insights into innovative MPC in the age of Industry 4.0 and even Industry 5.0.

An Intelligent Approach to Evaluating CAD Software Packages Through Hesitant Fuzzy AHP

The evaluation of computer-aided design (CAD) packages among the rising number of alternatives in the market has been a critical process for companies because it directly affects the performance of their design/engineering-related activities in a new product development (NPD) as well as in R&D departments. Moreover, it is seen as an important step towards the computer-integrated manufacturing (CIM) environment. On the other hand, the evaluation process of CAD software packages is a multiple-criteria decision-making (MCDM) problem in the presence of sets of alternatives and evaluation criteria, which should be solved using one of the MCDM methods in the current literature. Therefore, in this study, the analytic hierarchy process (AHP), successfully implemented in both academic research and practices, is used for CAD software selection problem. The method is also used with the group decision-making approach to reach a more reliable solution. Additionally, due to the fact that the crisp pairwise comparison in the traditional AHP method seems to be insufficient and imprecise to capture the right judgments of decision makers (DMs), the hesitant fuzzy linguistic term-sets are integrated with the AHP approach (the hesitant F-AHP) to model the vagueness and uncertainty on judgments of the DMs. This proposed approach is also supported with a user-friendly Excel template that provides an effective tool for companies to evaluate and rank CAD software packages without many tiresome fuzzy comparisons and complex calculations. Moreover, in the paper, a numerical example is presented to demonstrate the effectiveness and applicability of the proposed approach to potential readers and practitioners. In this example, five CAD software options are evaluated based on six criteria by three DMs; and the best alternative is determined.

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis. These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units (CPUs) and graphics processing units (GPUs). Computation-in-memory (CIM) provides a possible solution for highly efficient morphology operations. In this study, we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromorphic computation on a multi-array-based memristor system. Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors. Moreover, hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit (IC) manufacturing and medical image analysis. In addition, we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism. The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed, respectively, exhibiting the advantage of the proposed memristor-based morphology operation.

Selection of Computer-integrated Manufacturing Technologies Using a Combined Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Selection of Computer-integrated Manufacturing Technologies Using a Combined Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Pair-to-Pair Peer Learning in a Lab Environment

Pair-to-pair peer learning (PPPL) is the simplest form of group-to-group peer learning (GGPL). GGPL may be defined as a learning method where two or more peer groups interact and thus increase the knowledge of all group members; while PPPL may be defined as GGPL with group sizes of only two members each. A simple PPPL experiment was conducted and analyzed indicating an increase in knowledge gain as compared to peer learning (PL) alone. The experiment was conducted in an undergraduate engineering lab in a required computer-integrated manufacturing course for two engineering programs, mechatronics and industrial engineering.

A Case Driven Study of the Use of Time Series Classification for Flexibility in Industry 4.0

With the Industry 4.0 paradigm comes the convergence of the Internet Technologies and Operational Technologies, and concepts, such as Industrial Internet of Things (IIoT), cloud manufacturing, Cyber-Physical Systems (CPS), and so on. These concepts bring industries into the big data era and allow for them to have access to potentially useful information in order to optimise the Overall Equipment Effectiveness (OEE); however, most European industries still rely on the Computer-Integrated Manufacturing (CIM) model, where the production systems run as independent systems (i.e., without any communication with the upper levels). Those production systems are controlled by a Programmable Logic Controller, in which a static and rigid program is implemented. This program is static and rigid in a sense that the programmed routines cannot evolve over the time unless a human modifies it. However, to go further in terms of flexibility, we are convinced that it requires moving away from the aforementioned old-fashioned and rigid automation to a ML-based automation, i.e., where the control itself is based on the decisions that were taken by ML algorithms. In order to verify this, we applied a time series classification method on a scale model of a factory using real industrial controllers, and widened the variety of parts the production line has to treat. This study shows that satisfactory results can be obtained only at the expense of the human expertise (i.e., in the industrial process and in the ML process).

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Using intelligent technology and real-time feedback algorithm to improve manufacturing process in IoT semiconductor industry

To enable the Internet of things, the semiconductor manufacturing process has progressed from the micron to the deep submicron level. Quality improvement is one of the great challenges in wafer fabrication. Computer-integrated manufacturing (CIM) has arisen as a means by which to reduce wafer rework and continuously improve the semiconductor production process. This study uses statistical process control (SPC) and data mining technology to analyze the collected semiconductor process data. A real-time feedback algorithm is employed to ensure that each product lot is manufactured using optimized process parameters. This maximizes production capability, increases the semiconductor yield rate and reduces the cost of manufacturing. This paper focuses on wafer fabrication facilities (often called “fabs,” or “foundries”). The data mining architecture is implemented between CIM and the manufacturing execution system. Association rules and the k-means clustering algorithm are used together with real-time feedback control analysis to extract and analyze each parameter that affects the semiconductor production yield. This combination of real-time feedback and SPC using historical process data allows the system to predict the optimum process parameters for the next lot. The system compensates dynamically to accommodate differences among various machines and products, giving each machine a level of flexibility in manufacturing the product. The proposed semiconductor system can be applied to traditional manufacturing industry process analysis. Our results show how our system can improve the semiconductor manufacturing process in terms of processing capability, yield rate, stability and flexibility, while reducing costs, thus creating a competitive advantage for the wafer fab.

Task-centric method for shipyard hoisting process modelling and its application in CAPP

Shipyard hoisting process is a project type manufacturing process which entails more sophisticated elements and relationships than other kinds of processes, this feature brings particular challenges when constructing a structured description of the process, called enterprise model, to introduce various Computer Integrated Manufacturing (CIM) technologies. However, most of the modelling methods devised so far do not consider the individual demand of project type manufacturing process. Therefore, we envisage the necessity to expand, extend, and refactor current methodologies in order to exploit this remaining dimension. In this line, we present a dedicated method aiming at appropriately modelling the project type manufacturing process, it employs seven collaborative views and task-centric idea to emphasize work flow abstraction without sticking to the material flow abstraction as current methods do, which is the key factor to successfully achieve horizontal functions/organizations integration and vertical management hierarchies integration for this special process. The proposed method adopts semi-formal meta model to interpret its framework, building blocks and constraints graphically and rigorously. All these works are performed taking shipyard hoisting process as example. According to the modelling method, a two-stage Computer Aided Process Planning (CAPP) method is also initiated to demonstrate how the process model can serve as the bridge to converge manufacturing technology and information technology. Furthermore, a CAPP prototype is developed to carry out case study with an actual shipyard hoisting process. The study reveals promising contributions to not only enhancing the industrial competitiveness but also enriching the enterprise modelling research on project type manufacturing process.