Search

Competitiveness among the manufacturing industries is rapidly increasing because of industrialization. In today’s era, every manufacturing industry is trying to produce low cost products with superior quality and with propriety to deliver them in stipulated time period. Every manufacturing organisation needs appropriate system to produce high quality products in minimum cycle time, which can be achieved by maintaining higher productivity. In this research study, an attempt has been made to enhance the productivity of automotive component oil pan by reducing the machining cycle time and balancing the line to avoid unwanted movement of work piece and bottlenecks. It has been found that by incorporating CBN cutters and spot facing tool in machining, cycle time has been reduced by 7.02minutes per job. U type machine layout has been suggested to balance the production line.It has been observed that, with reduction of cycle time by 7.02 minutes per job, production capacity per shift can be increased from current 28 jobs per shift to 36 jobs per shift. With machining of additional 8 jobs per shift, production capacity of oil pan can be increased from 2000 jobs per month to 2900 jobs per month. Keywords: Productivity, Cycle Time Reduction, Line Balancing, Machine Shop

Digital Twin-based Cyber-Physical Quality System (DT-CPQS) concept involves automated quality checking, simulation, and prediction of manufacturing operations to improve production efficiency and flexibility as part of Industrie4.0 initiatives. DT-CPQS will provide the basis for the manufacturing process to march towards an autonomous quality platform for zero defect manufacturing in the future. Analysing sensor data from the CNC machine and vision monitoring system it was concluded that there was enough signal data to detect quality issues in a part being machined in advance using statistical/mathematical models (Smart PLS) and using machine learning algorithms. This allows the operator to take corrective actions before the resultant part ends in a quality failure and reduces the inspection time. The proposed approach forms the basis in expanding this concept to a large machine shop wherein by monitoring various parameters of the machines and state variables of the tools we can detect quality issues and develop an automated quality system using machine learning techniques.

In 2009, I wrote in these pages about my efforts to add an electric motor to a bicycle—a project that involved a considerable outlay of money and many hours in a machine shop behind a lathe. At the time, e-bikes were still rather exotic, at least in the United States, so it seemed worth the expense and effort to build my own.

A digitalized Smart Factory can be considered as a data island. Moreover, engineers have focused on the development of new technologies and techniques not only for transforming information to data but also to achieve efficient data utilization to further optimize manufacturing processes. However, the Zero-Defect Manufacturing concept has emerged, where the main goal is production optimization. The cornerstone in achieving the factories of the future is to further optimize the design of new assets so as they comply with the unique requirements of the customers. Therefore, this paper proposes the conceptualization, design, and initial development of a platform for the utilization of data derived from industrial environments for the optimization of the equipment design. The main aspects of the proposed framework are the data acquisition, data processing and the simulation. The applicability of the proposed framework has been tested in a laboratory-based machine shop utilizing data from a real-life industrial scenario.

Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) can be considered as the cornerstones of the lifecycle of a manufacturing asset. Since the above-mentioned processes often involve multiple engineers, from different departments even from different companies, it is crucial to ensure the flawless communication between the different individuals as well as to make the design process more intuitive. In the era of digitalization and Internet of Things, digital technologies such as Mixed Reality (MR) are utilized from engineers in order to leverage the capabilities of existing computer aided tools (CAx). Therefore, in this research work, the design and development of a Cloud-based and Mixed Reality-based framework is presented. The purpose of the framework is to facilitate engineers during the design and manufacturing phase of new components by providing a mobile application and common design tools via MR graphical user interfaces (GUI). Further to that, taking into consideration the advanced capabilities of MR devices in terms of user interaction, special emphasis is given on the interaction of engineers with the holograms of the components via simple hand gestures. The resulting 3D geometries can be imported in CAD suites either for further development or for manufacturing the designed component. The applicability of the proposed framework is tested and validated in a laboratory-based machine shop.

New technologies such as Unmanned Aerial Vehicles (UAV) are constantly being introduced under the Industry 4.0 framework. Although UAVs are mainly used in civil and military applications, the opportunities for Industrial integration must be examined, namely real-time remote monitoring, wireless coverage, and remote sensing. Therefore, UAVs can be considered as proactive solvers, simultaneously contributing to enhanced decision making as they are considered to be Internet of Things (IoT) platforms for efficient cost-effective data collection and monitoring. However, the use of UAVs in confined and crowded Industrial environments, e.g. machine shops, need further research. Therefore, this paper focuses on highlighting the limitations regarding the integration of UAVs in modern manufacturing systems as well as on presenting an intelligent framework based on Industrial Internet of Things (IIoT) for real-time machine shop monitoring. The applicability of the developed framework is tested in-vitro, in a laboratory-based machine shop.

Total Productive Maintenance (TPM) is a lean manufacturing technique that concentrates on improving the overall equipment effectiveness of a machine which is used for production. TPM ensures the availability of a machine and its implementation method involves continuous improvements which lead to reduced losses. This paper presents a case study on improving Overall Equipment Effectiveness (OEE) of equipment in a manufacturing company which manufactures steering housing and also to determine and address the facets which act as barriers in implementing TPM in the machine shop. The major issues faced by this company are the lack of stock in feed and tool changeover time which reduces the availability of the machine which in turn results in tremendous drop in OEE. The current level of OEE for that particular machine is found to be 54.09%. By implementing Lean concepts such as TPM and Single Minute Exchange of Dies (SMED) would improve OEE by 6.06%.

The results of theoretical and experimental studies aimed at identifying hidden defects in the structure of hard alloys and superhard composites used in the manufacture of cutting tools in order to control and predict gradual and sudden failures of cutting inserts. Damage control of cutting inserts is carried out by microscopic analysis. Deeper damage to the structure can be detected using the method of chemographic imaging. The proposed method is based on obtaining photographs of the oxidative reactions of materials of ultra-low concentrations occurring on the surface of solids under thermobaric loading. Before the moment of a sharp release of the energy of destruction, chemical processes of ultra-low concentrations are activated. Chemography allows to fix the zones where the incipient microcracks and microdefects are ready to actively develop, which can lead to the onset of macrodamage and failure to work. The chemographic image of the plate obtained as a result of the study is compared with the reference sample, as a result of which it is possible to assess the initial defect state of the material and predict the further period of the plate’s operation. The criterion for the existing defects and imperfections in the structure is the change in the blackness index of the chemographic image, theminimum value of which indicates aminimum of structural defects and internal defects in the material under study. The results allow to propose a new method for controlling the surface of cutting plates, which can be easily implemented in any machine shop, which makes its application very promising

The evolution of artificial intelligence (AI) and big data resulted in the full potential realization of technologies through convergence. Tremendous acceptance, adoption and implementation of the United Nations Sustainable Development Goals (SDG) Agenda 2030, has resulted in original equipment manufacturers (OEM) developing various designs of rotary machines in a bid to improve energy efficiency, with more improvements expected in the coming decade. An effective technique to manage energy efficiency in the smart grid is through integration of demand side management, inclusive of optimization of rewinding of an electric motor in a machine shop. This paper aims to conceptualize application of AI and augmented reality (AR) towards process visibility of remanufacturing rotary machine stators by robotic vision. SLT is the triangulation methodology used in laser scanning for 3D modelling, and instantaneous condition assessment of the core. A pre-defined robotic path is used towards identification of features for range image acquisition. Therefore, the potential of industry 4.0 in resuscitation of end-of-life products through service remanufacturers by RE in a rewinding shop are presented.

Artificial Intelligence (AI) through robotic system offer solutions to the quest for autonomous system with high cutting efficiency for lawn mowing. This work discusses the design, fabrication as well as the performance evaluation of a prototype robot for lawn mowing. The Auto-desk Inventor 2018 was used for the computer aided design and simulation of the automatic lawn mower. The electrical circuit connections between the microcontroller and the rest of the electrical systems was also designed with the aim of producing a system that can cut grass at high efficiency with little or no human intervention. The machine was incorporated with GPS, cameras, infrared and ultrasonic sensors for the detection and avoidance of obstacles. The control systems comprises of the Arduino micro-controller which controls the two geared motors alternatively in order to control the direction of the lawn mower. This is done according to the readings from the sensors governed by the C-programmed instructions fed into the micro- controller. The fabrication of the robot was done with the combination of machining and assembly operations in the machine shop and the results obtained from the performance evaluation indicated that the developed robot can autonomously avoid obstacles, make 180 degree turn and cut a lawn to an even height at high efficiency. Hence, this work provides a self-governing lawn mower that can cut grass at high efficiency and avoid obstacles thus minimizing human drudgery.

Simplicity in doing a work leads to its quality and efficiency. It also improves workers satisfaction and thus productivity. The testing table of differential used now in industry posses many difficulties. Automation and 5′S techniques improve effective time consumption in machine shop for production and housekeeping. The testing of differential is done by coupling the unit with a motor shaft. The motor is allowed to move forward and back through a guide way assembly. The motion is obtained by appoint machine. The end of motor shaft is designed as a mating part of the special designed part which can be screwed on differential. Thus the rotation of gears inside differential enables and testing of gear contact can be done. Existing manual clamping of the differential to the table by tightening of the screw was redesigned by two plates which are designed as mating with differential side, which move from both sides of the table to lock the differential part tight to the table. Both the plates are enabled with individual levers which can operate with both hands of worker simultaneously. Backlash and face out problem occurred in differential can be rectified by inserting the adjusting tool to the cut inside the table which is newly designed. With the effective implementation of all the three methods together, will leads to less consumption of time by 29% and, thus improves productivity by 40%.

In the modern market scheme, Market 4.0, there is an evident need for improved quality, increased quantities and lower delivery times of products. However, customers seek for highly personalized products. Consequently, production engineers must include customers in the product design phase so as to meet their unique requirements. On the contrary, during the fourth industrial revolution, cutting-edge digital technologies, such as Augmented Reality (AR) and Cloud technologies have reached a high level of maturity. AR is a digital technology, capable of super-imposing digital information to the user’s physical environment. Furthermore, with the adoption of the Learning Factory concept, academia contributes to the development of new skills and competencies, for the new generation of engineers, that align with the ever-increasing global market demands. As such, in this paper, a framework enabling collaborative product design based on AR is presented, which acts as the digital thread for connecting engineers to customers. The participants will design and develop a Cloud platform for supporting file exchange and storage. Moreover, the Cloud platform can be realized as the communication layer between the stakeholders. In addition to that, an end-user application for the product design, customization and visualization with the use of AR will also be developed. Finally, the proposed framework will be tested in vitro, in a machine shop, in order to gather useful insight into the skills and competencies acquired by the participants during the development of the project.

In modern manufacturing world, MRO (Maintenance and Repair Operations) are the cornerstone for keeping industrial equipment in near-optimum condition. Successful completion of MRO has been benefited from Augmented Reality (AR), by considerably decreasing MTTR (Mean Time to Repair). To that end, AR delivers the digital tools that help on-the-field technicians to perform MRO easily and intuitively, however intense development is required for generating AR instructions. The latest advances in computer technologies, concretely in Convolutional Neural Networks, have enabled advanced computer vision. This research paper presents a framework for generating AR maintenance instructions, based on advanced computer vision and Convolutional Neural Networks (CNN). The applicability of the framework is tested in-vitro in a lab-based machine shop.

Modern manufacturing systems require increased levels of automation for fast and low-cost production, but also high levels of flexibility and adaptability to dynamic production requirements. Human-robot interaction allows for closer collaboration in assembly tasks that require increased productivity by combining the performance of robotic systems with the flexibility and dexterity of human workers. However, the main challenge is establishing human safety while performing near to robots. Towards enabling safe human-robot collaboration several physical and software systems should be combined. In this paper, a cyber-physical system for enabling and controlling safe human-robot collaborative assembly operations is discussed. The approach considers a shared fenceless working space where humans, industrial robots, or other moving objects, such as auto-guided vehicles, may operate. The working space is monitored by optical sensors. In this context, human safety is the major challenge. The paper focuses on a Cyber-Physical System for enabling human-robot collaboration based on real-time evaluation of safety distance and a closed-loop control for triggering collision preventive actions. An initial implementation is evaluated in a specific use case in a laboratory machine shop with an industrial robot, and its results are compared regarding the use of one or more sensors. The comparison is performed in terms of system response time to detect human presence within a predefined safety zone.

Summarization: Glanceable interfaces are Augmented Reality (AR) User Interfaces (UIs) for information retrieval ”at a glance” relying on eye gaze for implicit input. While they provide rapid information retrieval, they often occlude a large part of the real-world. This is compounded as the amount of virtual information increases. Interacting with complex glanceable interfaces often results in unintentional eye gaze interaction and selections due to the Midas Touch problem. In this work, we present Holo-box, an innovative AR UI design that combines 2D compact glanceable interfaces with 3D virtual ”Holo-boxes”. We can utilize the glanceable 2D interface to provide compact information at a glance while using Holo-box for explicit input such as hand tracking activated when necessary, surpassing the Midas Touch problem and resulting in Level-of-Detail(LOD) for AR glanceable UIs. We test our proposed system inside a real-world machine shop to provide on-demand virtual information while minimizing unintentional real-world occlusion. Παρουσιάστηκε στο: Special Interest Group on Computer Graphics and Interactive Techniques Conference Posters

Hybrid flow shop with energy consumption (HFS-EC) combine the flow shop scheduling and parallel machine scheduling problem with the aim to optimize energy utilization, besides regular makespan in the production scheduling. This paper optimizes an HFS-EC case study using Moth Flame Optimization (MFO). The case study has been conducted in a machine shop concentrating on three machining types; lathe, milling and deburring. The objectives were to optimize makespan and total energy consumption in the machine schedule. Optimization using MFO has been conducted and the results was compared with well-established algorithm like Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization. The results were also compared with relatively recent algorithm such as Whale Optimization Algorithm and Harris Haws Optimization. Based on the optimization results, the MFO outperformed other comparison algorithms for the mean fitness and also the best fitness. Although there were other solutions with better individual optimization objectives, but results obtained by MFO compromised between minimum makespan and energy consumption. The proposed HFS-EC model and MFO algorithm has a great potential to be implemented in other scheduling case study due to benefit of reducing carbon emission and at the same time maintain the production output.

In engineering industries, multifarious machineries of differing functions are required for manufacturing, transportation, maintenance, and automation purposes. Some parts in these machineries can become critically damaged at a certain stage; there may also be lack of availability of these parts in the market or technical data of the machine might have been lost. Over the course of time, new or modified machine tools are required to increase the production rates and efficiency while considering sustainability issues. The process of Reverse Engineering provides an effective solution to these problems, abating the production cycle of the required parts, and minimizing the financial losses. The primary objective of this chapter is to explain how to implement the principles of both geometric and functional reverse engineering using two real-life examples to improve the strength and performance of the selected machine tools and to make them safer to use. Both selected machines were located at the machine shop facility of Middle East Technical University – Northern Cyprus Campus (METU – NCC). In the first case study, reverse engineering of a bench-type drilling machine was carried out. General procedure of reverse engineering was applied to morph it into an improved replication through rigorous computational analyses. The applied procedure comprises disassembling the drilling machine, taking measurements of the disassembled parts, CAD solid modelling of the parts of the drilling machine, and carrying out the finite element, sustainability, and cost analyses of the spindle, which is a crucial component of the drilling machine. An evaluation matrix was then used to find the optimum solution for improving the spindle based on the weights assigned to the factor of safety, cost of material, and its environmental impact. In the second case study, reverse engineering of a metal/wood cutting band saw was conducted in a similar fashion. For digitizing complex geometrical features, a semicontact picture designation method was used. Following that, stress and sustainability analyses were performed, yielding critical output in the form of maximum von Mises stresses, maximum displacements, factor of safeties, carbon dioxide footprint, and water and energy usage during production of parts. Both case studies proved that reverse engineering coupled with computational analysis is a powerful platform to improve existing machine tools with a particular attention paid on more sustainable and economical machine design.

The pedagogical mechanisms that underlie hands-on/maker education’s efficacy have not yet been thoroughly understood and compared across cultures. This preliminary study explores the hypothesis that design-build, machine shop sessions increase confidence in African youth. A “pre” and “post” reflection prompt completed by all first year engineering students at a university in Ghana reveal that a single 3 hour design-build session at the machine shop caused a significant increase in student confidence $(p_{males}=0.014,\ p_{females}=0.009)$, with medium to large effect size $(g_{males}=0.659,\ g_{females}=1.024)$. Interestingly, none of the students had experience working in a machine shop prior to this experience, yet in the pre-test, 63% of the respondents reported feeling “Confident” or “Very Confident” to use the tools in the workshop. The top two reasons cited were the availability of tools and experienced personnel to guide them. This suggests other factors that are necessary to explore, such as the role of the physical environment itself in creating a sense of possibility.

We study several two-machine shop scheduling problems, namely flow shop, job shop and open shop scheduling problems under linear constraints. In these problems, the processing times of two stages of jobs are also decision variables and satisfy a system of linear constraints. The goal of each problem is to determine the processing time of each job, and to schedule the jobs to the shop machine such that the makespan, i.e., the completion time of all jobs, is minimized. These problems can find application in various areas, such as industrial production, advertising and automotive maintenance. We study the computational complexity and propose polynomial-time optimal or approximation algorithms for them. In particular, we show that although a two-machine flow shop scheduling problem and a two-machine job shop scheduling problem without recirculation can be solved in polynomial time, the problems where processing times satisfy linear constraints are generally NP-hard in the strong sense. Then, we design algorithms for various settings of these problems. We design polynomial-time algorithms for them when there are a fixed number of constraints. For the general case, we first propose a simple 2-approximation algorithm, and then design a polynomial-time approximation schemes. In contrast to the previous two problems, we show that the two-machine open shop scheduling problem under linear constraints can be solved in polynomial time.

Communication latency has been a significant barrier for many applications deployed in various network domains over the last few years. Until now, Internet of Things (IoT) enabled the interconnection of low-power, sensors of limited transition and latency-tolerant smart devices. However, despite the development of communication protocols and standards, the latency problem still exists, decreasing the Quality of Services (QoS) and Quality of experience (QoE) in many applications. Therefore, high availability, security and ultra-fast reaction times offered by the Tactile Internet, will offer new dimension to human-to machine interaction by enabling haptic and tactile sensations. Furthermore, the fifth generation (5G) mobile communications systems will support this emerging Internet at the wireless edge. Therefore, Tactile Internet can be used as backbone for delay mitigation in cooperation with 5G networks, especially for ultra-reliable low-latency applications such as Smart Healthcare, Virtual and Augmented Reality, and Smart Education and e-Learning. To address these challenges, this paper is focused on the identification of the challenges and opportunities underlined by the integration of the Tactile Internet and the emerging 5G systems in modern education. Moreover, the paper outlines the key technical requirements, frameworks, and architectural approaches for the implementation of the Teaching Factory Network with the next generation 5G network capabilities. Under the scope of Teaching Factory, a framework for the fusion of a virtual, simulated machine shop to its physical counterpart is presented, highlighting the key aspects for the successful Human Machine Interface and the communication of the physical and digital machine shop in real-time.

The article proposes a model with an artificial intelligence approach that integrates risks through the Grey Clustering method applying the "Triangulation of center-point based on Whitening functions -CTWF", for this, the data established is standard data (minimum standards that the four workshops of a company in the industrial sector must meet) and sampled data (real data obtained in the field) to test the grey classes. In this study, the different types of risks (lighting, noise and hand-arm vibration) were globally evaluated and analyzed in the four workshops of a heavy machinery maintenance services company in the industrial sector (welding shop, hydraulic shop, machine shop 1 and machine shop 2), located in Lima, Peru. According to the results obtained from the level of hygienic quality in each workshop, the welding workshop is at a very poor-quality level, while the others are at a good and very good level; regarding the four workshops, it was determined that the noise level is not recommended as they do not meet the minimum required standards. Therefore, control measures were proposed in the four workshops where the level of irrigation is bad and very bad. This study will benefit companies in the industrial sector that need to analyze the level of hygienic quality in their work areas with a global approach in order to apply control measures with prevention, protection of health and physical integrity of workers.

This work introduces the formulation and application of a MILP model to solve the problem of planning the weekly production of a machine shop using numerical control lathes to manufacture spare parts for agricultural machines. The machine shop works under a Flexible Job Shop system and in turn has limited labor force with different abilities to operate the various high-complexity lathes and to carry out the setup tasks in each of them. The developed model is based on a basic formulation for the classical problem; and flexible adjustment is shown for the various situations that may arise from different scheduling problems. The application of the model to various scenarios is presented and the improvements brought about by the analysis of solutions are discussed.