Nik Mohd Zuki Nik Mohamed

The development of a hybrid knowledge-based system for the design of a Low Volume Automotive Manufacturing (LVAM) system

A conceptual design approach is an important stage for the development of a hybrid Knowledge-Based System (KBS) for Low Volume Automotive Manufacturing (LVAM). The development of a hybrid KBS, which is a blend of KBS and Gauging Absences of Pre-requisites (GAP), is proposed for LVAM research. The hybrid KB/GAP system identifies all potential elements of LVAM issues throughout the development of this system. The KBS used in the system design stage of the LVAM system analyses the gap between the existing and the benchmark organisations for an effective implementation through the GAP analysis technique. The proposed KBLVAM model at the design stage explores three major components, namely LVAM car body parts manufacturing perspective, LVAM competitive priorities perspective and LVAM lean environment perspective. Initial results reveal that the KBLVAM system has identified, for each perspective modules and sub-modules, the Problem Categories (PC) in a prioritised manner.

Investigation on musculoskeletal discomfort and ergonomics risk factors among production team members at an automotive component assembly plant

Musculoskeletal discomfort (MSD) is very common condition in automotive industry. MSD is affecting the workers health, well-being and lower down the productivity. Therefore, the main objective of this study was to identify the prevalence of MSD and ergonomics risk factors among the production team members at a selected automotive component manufacturer in Malaysia. MSD data were collected by conducting structure interview with all participants by referring to the Cornell Musculoskeletal Disorder Questionnaire (CMDQ). Those production team members who achieved a total discomfort score for all body regions more than 100 was selected for job task assessment. The physical exposure risk factors of work related musculoskeletal disorders (WMSD) has evaluated by using Quick Exposure Check (QEC) techniques. The results of the study identified the severe MSD associated with production assembly team members. It is expected that the prevalence of MSD for those production assembly team members was lower back (75.4%), upper back (63.2%), right shoulder (61.4%), and right wrist (60%). The QEC analysis discovered that about 70% of job tasks had very high risks for neck posture and 60% had high risks for the back (in moving condition) and shoulder/arm postures. There were 80% of respondents have produced a high score for exposure risk to vibration. As a conclusion, the main implication of the current study is that special attention should be paid to the physical and psychosocial aspects in production team members with musculoskeletal discomfort to improve their safety, health, and well-being, maintain work ability and productivity.

Analytic network process model for sustainable lean and green manufacturing performance indicator

Sustainable manufacturing is regarded as the most complex manufacturing paradigm to date as it holds the widest scope of requirements. In addition, its three major pillars of economic, environment and society though distinct, have some overlapping among each of its elements. Even though the concept of sustainability is not new, the development of the performance indicator still needs a lot of improvement due to its multifaceted nature, which requires integrated approach to solve the problem. This paper proposed the best combination of criteria en route a robust sustainable manufacturing performance indicator formation via Analytic Network Process (ANP). The integrated lean, green and sustainable ANP model can be used to comprehend the complex decision system of the sustainability assessment. The finding shows that green manufacturing is more sustainable than lean manufacturing. It also illustrates that procurement practice is the most important criteria in the sustainable manufacturing performance indicator.

Planning and design of a knowledge based system for green manufacturing management

This paper presents a conceptual design approach to the development of a hybrid Knowledge Based (KB) system for Green Manufacturing Management (GMM) at the planning and design stages. The research concentrates on the GMM by using a hybrid KB system, which is a blend of KB system and Gauging Absences of Pre-requisites (GAP). The hybrid KB/GAP system identifies all potentials elements of green manufacturing management issues throughout the development of this system. The KB system used in the planning and design stages analyses the gap between the existing and the benchmark organizations for an effective implementation through the GAP analysis technique. The proposed KBGMM model at the design stage explores two components, namely Competitive Priority and Lean Environment modules. Through the simulated results, the KBGMM System has identified, for each modules and sub-module, the problem categories in a prioritized manner. The System finalized all the Bad Points (BP) that need to be improved to achieve benchmark implementation of GMM at the design stage. The System provides valuable decision making information for the planning and design a GMM in term of business organization.

A Review of Multi-holes Drilling Path Optimization Using Soft Computing Approaches

In today’s competitive environment, optimization is considered as an important element for maintaining and improving both aspect of manufacturing such as quality and productivity. In multi-holes drilling process, 70% of the machining time involved the tool movement and tool switching. Various researches had been conducted to reduce the tool movement and switching time. This paper reviews the research publications on the drilling path optimization using soft computing approaches. In particular, this review focuses on four main aspects; drilling application areas, problem modeling, optimization algorithms and objective functions of drilling path optimization. Based on the review, the researchers’ interest in this area is still growing. However, the existing researches were limited to implement, modify and hybridized the well-established optimization algorithms. Furthermore, there is a lack of awareness to consider the environmental and sustainable issues in the existing research. In future, the researcher is suggested to give focus on energy consumption that related with sustainable manufacturing and also to explore the potential of new meta-heuristics algorithms that can lead to significant in reduction machining time.

A Knowledge-Based Ergonomics Assessment System for WMSD Prevention Using AHP Methodology

This research develops a knowledge-based ergonomics assessment system (KBEAS) that measure and predicts the degree of criticality of risk factors related to work-related musculoskeletal disorders (WMSD). Predicting WMSD individual risk level provides critical decision support information to occupational safety and health (OSH) practitioners in the ergonomic analysis. The KBEAS is based on the analytic hierarchy process (AHP) methodology. The current study integrates AHP method with real workplace ergonomics risk data and design web-based system assisting a sensible multi-criteria WMSD related risk factors. The objectives involve knowledge acquisition performed through preliminary study, MSD symptom study, literature analysis, and tacit knowledge analysis and practitioner survey to identify the ergonomics risk factors that include individual, organizational, physical and psychosocial. The application of this system shows that the design of the proposed KBEAS for WMSD risk factors has been validated and gets each risk factors weight easily by using AHP. The study findings showed that ‘organizational ergonomics risk factors’ is more critical than other factors. The overall prioritization revealed that ‘exposure to physical demands’ had a priority vector of 26.33%, and it was perceived as the item with the most critical factor. The KBEAS could help the user to make an objective judgement on the subjective description and get the correct result of the ergonomics risk factors.

Optimization of Multi-holes Drilling Path Using Particle Swarm Optimization

In multi-holes drilling process, the tool movement and tool switching consumed on average 70% of the total machining time. Tool path optimization is able to reduce the time taken in machining process. This paper is focus on the modeling and optimization of multi-holes drilling path. The problem is modeled as traveling salesman problem (TSP) and optimized using Particle Swarm Optimization (PSO). To test the PSO performance, 15 test problems were created with different range of holes numbers. The optimization results from PSO were compared with other top algorithms such Genetic Algorithm and Ant Colony Optimization algorithm. PSO is also compared with another algorithm like Whale Optimization Algorithm, Ant Lion Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Moth-flame Optimization and Sine Cosine Algorithm. The result indicates that PSO algorithm is performed better than comparison algorithms. PSO algorithm gives the minimum value of fitness path and their CPU time compared to other algorithms. Hence, the smaller their value, the algorithm is better and more efficient. In future, researchers should more focus on environmental issues and energy consumption for sustainable manufacturing. Besides, need to explore other potential of new meta-heuristics algorithms to increase the hole drilling operation efficiencies.In multi-holes drilling process, the tool movement and tool switching consumed on average 70% of the total machining time. Tool path optimization is able to reduce the time taken in machining process. This paper is focus on the modeling and optimization of multi-holes drilling path. The problem is modeled as traveling salesman problem (TSP) and optimized using Particle Swarm Optimization (PSO). To test the PSO performance, 15 test problems were created with different range of holes numbers. The optimization results from PSO were compared with other top algorithms such Genetic Algorithm and Ant Colony Optimization algorithm. PSO is also compared with another algorithm like Whale Optimization Algorithm, Ant Lion Optimizer, Dragonfly Algorithm, Grasshopper Optimization Algorithm, Moth-flame Optimization and Sine Cosine Algorithm. The result indicates that PSO algorithm is performed better than comparison algorithms. PSO algorithm gives the minimum value of fitness path and their CPU time compared to other algorithms. Hence, the smaller their value, the algorithm is better and more efficient. In future, researchers should more focus on environmental issues and energy consumption for sustainable manufacturing. Besides, need to explore other potential of new meta-heuristics algorithms to increase the hole drilling operation efficiencies.

A Future Framework of Knowledge-Based Ergonomics Assessment System at Workplace in Automotive Assembly Plant

There are several parameters must be correctly evaluated to guarantee a good level of interaction between worker and working system, in order to avoid safety and health problems. The lack of attention to occupational ergonomics issues may to potential ergonomics risk for which decision makers are ignore when develop new product and process. This paper proposed a novel framework to facilitate the ergonomics knowledge management for occupational risk assessment. It serves two objectives, the first objective is to aid the decision makers predicting ergonomics risk element at early stage of development product and process. The second objective is to develop knowledge-based ergonomics assessment system (KBEAS) in automotive assembly plant. The respondents of the study are about 250 and consist of assembly workers ranging from operator to executive level in automotive component assembly plant. The activities of direct observation, activity analysis, photography, video, survey questionnaire and interviews, are employed to measure the occupational ergonomics risk factors. The outcome of these activities will be used as an input for analytical hierarchy process (AHP) technique to prioritize the occupational ergonomics risk ate workplace. The outcome of this framework could ease decision makers in assessing and prioritizing the ergonomics risk at the early stage of product and process in automotive component manufacturer.

The challenges of lean manufacturing implementation in kitting assembly

Literature studies shows that lean manufacturing goes way back with the original founder Eli Whitney in year 1799. The main purpose of lean manufacturing is to identify and eliminate waste in production. The application of lean manufacturing can be carried out in any industrial processes with regards to the understanding of lean principles, theories and practices. Kitting is one of the important aspects in a successful production. The continuous supply of materials from store to production has to be systematic and able to achieve lean standard for it to be successful. The objective of this paper is to review the implementation of lean manufacturing in kitting assembly. Previous papers show that, the implementation of lean manufacturing in kitting assembly may be beneficial to the organization such as reduce in space occupancy, part shortages, lead time and manpower. Based on previous research, some industries may tend to change between kitting and line stocking which are due to lack of understanding when implementing kitting and causes longer lead time and materials overflow in store. With a proper understanding on what to kit, where to kit, how to kit, why to kit and who kits the material with a standardised process flow may ensure the success of kitting.

Hybrid Knowledge-Based System for Collaborative Green Automotive Manufacturing Management

The objective of this research paper is to demonstrate the application of hybrid knowledge-based system, gauging absences of pre-requisites (GAP), and analytic hierarchy process (AHP) approaches for selecting the improvement programs for Collaborative Green Manufacturing Management (CGMM) system. In this research, a generic knowledge-based system is developed to measure the level of CGMM adoption in automotive manufacturers compared to the ideal system. Using the GAP and AHP tools, the key green manufacturing improvement programs can be prioritized and demonstrated with an illustrative example.