Machine Shop Research Articles

Clear future vision

Vision Engineering is best known for its non-contact and contact optical microscopy and measuring systems but is targeting growth of its subcontract manufacturing division. Manufacturing services lead Ian Muir and machine shop manager Scott Blackwell filled Justin Burns in on developments

Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.

Machining and Fabrication Equipment in Workplaces

Increasingly well-developed workplace acoustic standards have resulted in more consistent outcomes across projects and normalized occupant expectations of acoustic quality, enhancing productivity and satisfaction. Yet these standards are often not developed for or applied to R&D and manufacturing spaces that include traditional workplace room types and uses; design criteria is limited to OSHA-assessment for noise-at-work violations. Hybrid office buildings incorporating prototyping and maker spaces are common today and often contain high-noise equipment traditionally found in dedicated machine shops. As these facilities are incorporated alongside traditional offices, noise and vibration levels generated by fabrication equipment should be accurately quantified to avoid compromised workplace acoustics. While sound data is available for most large construction equipment, available data for smaller fabrication machines typically found in machine shops is often non-standardized and difficult to obtain. Field measurement of existing equipment installations can ground an acoustical analysis with real-world data and be highly valuable in evaluating potential noise and vibration impacts and applying cost-effective mitigation during design. This case study will present measurements obtained during a noise and vibration assessment of an existing machine shop located within an office building. The discussion will include limitations of the data and an assessment of potential for disruptions.

Smart Device Tech Costing £200 Delivering Productivity Gains

Many machine shops operate mixed machine tool fleets and in need of a low-cost easy to use vendor-neutral machine monitoring technology. Sheffield-based FourJaw Manufacturing Analytics has come up with the answer

A new technology for locating very low frequency and negative signal-to-noise ratio sound sources

This paper presents a new technology that enables one to locate multiple sound sources with very a large dynamic range simultaneously, including very low frequency and negative signal-to-noise ration sound sources in a non-ideal environment, where there are random background noise and unknown interfering signals. In particular, spatial resolution of source localization is frequency independent. In other words, spatial resolution remains very high at very low as well as at very high frequencies. The underlying principle of this new technology is a hybrid methodology that includes a passive SODAR (nic etection nd anging), advanced signal processing and least-squares minimization. Using this technology, engineers will be able to visualize sound sources in both real time and post processing in an adversary test environment. Live videos of sound sources localization inside a crowd machine shop are shown, where there are unknown background noise, unspecified sound reflections and reverberation, and interfering signals.

Experience in using regulations when performing acoustic designs in graduate qualification work

This work is devoted to the problems of acoustic design that a future specialist may face in the field of labor protection. The article provides a review of existing noise protection regulations, including those that have lost their force due to the emergence of a new set of rules (Sanitary Regulations) but contain useful information. Using the example of the acoustic design of a machine shop (within the framework of the graduate qualification work (GQW), the article discusses the difficulties of using existing literature and regulations, as well as the inconsistencies and contradictions between them. The material of the article can be used in the development of methodological training materials for technical specialists in the field of technosphere safety.

On-machine diameter measurement of a cylindrical workpiece with a reference artefact

A system for on-machine diameter measurement of a ceramic cylindrical workpiece on a precision grinding machine is developed by employing a commercial optical micrometer, which can measure the diameter of a workpiece in various sizes. One of the drawbacks of the optical micrometer is its poor thermal stability, which could affect the on-machine diameter measurement in a machine shop where the room temperature is not controlled well. To address the issue, in this paper, a comparative measurement method with a calibrated reference artefact having identical material and nominal diameter with respect to a ceramic workpiece is newly proposed. In the proposed comparative method, the workpiece diameter is evaluated through detecting its deviation from that of the reference artefact calibrated by using a high-precision measuring instrument such as a coordinate measuring machine in advance of the on-machine measurement. Meanwhile, in the comparative measurement method, the uncertainty of the reference artefact should also be taken into consideration as a source of uncertainty. In this paper, measurement uncertainty analysis is carried out for the proposed comparative method based on GUM, while verifying the uncertainty of the reference artefact diameter through numerical calculations by a Monte Carlo method.

Flexible job-shop scheduling problem with unrelated parallel machines and resources-dependent processing times: a tabu search algorithm

ABSTRACT Flexible job shop scheduling problem is one of the most important topics in production management and is one of the most complex topics in combinatorial optimization. This problem is a generalization of job shop and parallel machines scheduling problem. Since the efficient allocation of resources can improve the performance of manufacturing, here, to reduce the processing time of jobs, additional resources are assigned to machines. In fact, in this paper, the effect of flexible resources in the flexible job shop scheduling problem with unrelated parallel machines and sequence-dependent setup time is investigated. Also, by presenting a mixed-integer linear programming model, an attempt has been made to minimize the costs of makespan, total weighted tardiness, delivery time and inventory. After solving this model by the GAMS, due to the NP-hardness of the problem, a tabu search (TS) algorithm is utilized for large-size instances. Finally, the obtained results are compared with the genetic algorithm (GA). To verify the statistical validity of the computational experiments and confirm which the best algorithm between the TS algorithm and GA is, a Kruskal–Wallis test is used. The results show that the TS algorithm is better than the GA.

Construction method of shop-floor digital twin based on MBSE

Digital twin (DT) technology is essential for achieving the fusion of virtual-real cyber-physical systems. Academics and companies have made great strides in the theoretical research and case studies of constructing the shop-floor digital twin (SDT), which is the premise of applying DT technology on the shop floor. A shop floor is a large complex system that involves many elements including people, machines, materials, methods, and the environment and processes, such as the technical flow, business process, logistics, and control flow. However, most of the developed cases lack a hierarchical, structured and modularized implementation framework for the development of an SDT system, which leads to problems such as a low reuse rate of the system blocks, lack of scalability, and high upgrade and maintenance costs. In response to these issues, we propose a construction method of the DT for the shop floor based on model-based systems engineering from the perspective of the system. In this method, a comprehensive DT model for the shop floor is gradually constructed by using system modeling language, the modeling method “MagicGrid,” and the “V model” of systems engineering. The model includes four dimensions of the shop-floor requirements, structure, behavior, and parameters, as well as three stages (the problem domain, solution domain, and implementation domain), and connects nine steps of the “V model,” including the system requirements, system architecture, subsystem implementation, subsystem integration, and system verification. Then, based on an example of a real NC machining shop floor, subsystems including a visualization system, synchronization system, and simulation system, are discussed. Finally, the functions of the integrated systems are verified based on the requirements, including the real-time synchronization of “man, machine, material, and method” and the transient simulation in real time. The numerical indicators of the integrated system are verified, including the model completeness and synchronization timeliness.

Nothing Lightweight About Lightweighting

The shift to lightweighting across manufacturing sectors is presenting benefits and challenges to machining shops. Justin Burns investigates

Cyber-Physical Quality Systems in Manufacturing

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.

Selecting the Safest CNC Machining Workshop Using AHP and TOPSIS Approaches

Machining operations on computer numerically controlled (CNC) machine tools are essential for the machining industry. Most of these operations take place in machining workshops. Safety issues in machining workshops shops can affect not only the health of the operators, which is extremely important, but also the productivity of the process and the accuracy of the parts. The research presented in this article addresses the issue of evaluating the safety of a CNC machining workshop, using a combined approach based on the analytic hierarchy process (AHP) and technique for order performance by similarity to ideal solution (TOPSIS) methods. A set of four evaluation criteria was proposed and the methods of processing the information for each criterion were used to extract the significant data needed for the evaluation. The proposed method was used to select the safest CNC machining workshop out of a total of three considered for assessment.

A case study on the applications of productivity indices with resources frugality

The basic attributes of any industry or a firm are productivity, quality of products, as well as the cost and flexibility of manufacturing systems. Productivity out of all is the most imperative attribute and hence becomes centre of study in the field of Industrial engineering, for the researchers all around the world . This paper presented a study in this same regard through a case study carried out in XYZ Industry. Considered industry comprises many shops like Machine shop, Maintenance shop, Assembly shop and Fabrication shop. Out of these, Maintenance shop and Assembly shop are taken into account for the productivity improvement exercise. Fragile productivity points were identified by analysing the processes that were going on floor area of shops under consideration and hence those processes were exchanged with new ones, exchanged processes implemented practically on floor area and results were obtained . Obtained results are shown in this paper as savings, from which it can be concluded that desired objective of productivity improvements have been achieved. So study presented in this paper gives encouraging consequences by improving the productivity of concerned manufacturing industry.

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.

A complexity analysis and algorithms for two-machine shop scheduling problems under linear constraints

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.

A new many-objective green dynamic scheduling disruption management approach for machining workshop based on green manufacturing

Since disturbance events such as equipment failure often occur in the production process of machining workshop, the scheduling plan needs to be rescheduled. However, the existing rescheduling strategy is difficult to effectively reduce the deviation between the rescheduling plan and the initial scheduling plan. In this article, a new disruption management method is proposed. It includes the disruption management model and the many-objective optimization algorithm. The production scheduling system is a typical man-machine system. The disruption management model not only measures scheduling stability and robustness but also measures the dissatisfaction degree of behavioral agent. It is conducive to comprehensively and scientifically measures the deviation between the rescheduling plan and the initial scheduling plan. When solving the disruption management model, the existing many-objective optimization algorithm cannot effectively balance convergence and diversity. A new algorithm named NSGA–III–RPE is proposed. The NSGA–III–RPE designs improved the r-dominance relationship and a new elite strategy to dynamically transform the Pareto set into the partially ordered set. These improved operations can increase the selection pressure and improve the convergence speed. Different from traditional rescheduling strategy, the new disruption management can comprehensively measure disturbance deviation including the behavioral agents and physical entities in the system, and use NSGA-Ⅲ-RPE algorithm to optimize the disruption management model. Thus, it can quickly obtain an adjusted scheduling plan that has the smallest deviation from the initial scheduling plan. Experimental results on benchmarks also demonstrate the effectiveness and competitiveness of the NSGA–III–RPE algorithm. IGD and HV indicators of NSGA-Ⅲ-RPE are superior to other algorithms. Finally, the new disruption management method is applied to the machining workshop. Compared with the results of total rescheduling method, robustness, stability, and the satisfaction degree of customer increase by 37.25%, 34.14%, 13.87% respectively. It can effectively reduce the deviation between the rescheduling plan and the initial scheduling plan. The new disruption management method is conducive to improve production efficiency, stability and reduce process costs in dynamic manufacturing environment of the machining workshop.

Android based maintenance information system for machining workshop in polytechnic high education

There are six state polytechnics aged 36 years old in Indonesia. The six polytechnics have machining workshops with the same number and machines. The problem that arises in Semarang State Polytechnic is the maintenance system that has not been integrated into a system, even though the electronic maintenance and repair activities have been carried out. The purpose of this study is to develop a maintenance information system that can control all of its activities well. The system is built with an object-oriented approach implemented by android. Research results include: database systems, web application systems, and android application systems that have delivered output correctly.

Multidimensional Data Modeling and Model Validation for Digital Twin Workshop

Abstract Digital twin workshop (DTW) is an important embodiment of intelligent manufacturing in the workshop level, which enables the smart production control and management of the workshop. However, there still exist problems including data modeling and verification of digital model in the process of DTW construction. To solve these problem, multidimensional data modeling and model validation methods of DTW are proposed in this article. First, five-order tensor models for representing manufacturing elements are established to unify the data from physical workshop (PW) and virtual workshop (VW). Then, the mathematical method for verifying DTW twin model is proposed from the recessive and explicit perspective. Finally, a case study of an aerospace machining workshop is carried out to verify the operability and effectiveness of the proposed method. The case analysis shows that the proposed methods can effectively evaluate whether the twin model accurately provides the description of the actual behavior process of physical workshop, and the proposed methods have good performance.

PERANCANGAN SISTEM INFORMASI PENJUALAN BERBASIS WEB PADA BENGKEL KARUNIA BAN PAMULANG

The use of technology in the company not only supports business processes but also very important on corporate earning. Business ownerhave always pay attention to the development of the existing technology in order to improve their business success. Therefore many companies want to implement the technology edgein every aspect ofits business processes. Karunia Ban machine shop is a trading company that sells a wide range of tires, especially for goods and service fees. Sales transactions that occur on Karunia Ban shop are still being done manually. Companies often find it difficult to know which sales transaction that are for spare parts or for repair services, so we need an information system tosolve the problem. Information systems is expected to optimize the sales in Karunia Ban machine shop. The methodology that we used to analysis and to develop the structured system are using UML (Unified Modelling Laguage). The expected results from this paper is to establisha procedure that describes a system using UML analysis and design a system that runs the system, the draft also produced a web-based sales application using php and my sql.

Development of process performance simulator (PPS) and parametric optimization for sustainable machining considering carbon emission, cost and energy aspects

The manufacturing industries consume one-third of global energy. Intensive use of electrical energy in industries provides the researchers and experts strong reasons to develop and propose strategies to minimize energy consumption. Evidently, the advanced technologies such as nanofluid minimum quantity lubrication in machining and process optimization accompanied by holistic models can eliminate the use of conventional fluids to reduce production cost and to cope up with environmental issues of global warming and climate change. This study investigates the holistic analysis of four metrics, i.e., surface quality, energy, cost, and carbon emission that influence the impact of the machining process on the environment of China. Al2O3 based nanofluid was prepared and used in the external turning of Haynes 25 alloys to improve the machining and to promote sustainability. Multi-objective optimization was performed to find out a trade-off relation for product quality, energy consumption and production cost. Results showed that the minimum levels of energy consumption and carbon emission were obtained at the high levels of feed rate and cutting speed. The Carbon Emission Factors (CEF) of used resources have the most significant effects on CO2 emissions. Furthermore, the feed rate was found to be the most significant parameter on the machining performance indices. The application of nanoparticles helped to reduce the cutting energy and CO2 emission, which are proportional to electricity consumption. A holistic component activity-based cost model was developed, and it was noted that the overhead and workpiece cost shared more than 95% of the total cost. Multi-objective optimization reduced specific energy by 18.10%, carbon emission by 22.17% and product cost by 16.25%. Moreover, the present study deals with 3E, i.e., Energy, Environment, and Economy. The optimum cutting parameters obtained from the concept of 3E at the machine shop level can significantly improve efficiency of nanofluid MQL assisted machining (NFMQL) process, reduce cost per unit of product, and achieve the low carbon manufacturing goal.